US20110228999A1 - System and method of image reconstruction using a temporal subset of sparsified image data - Google Patents
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Definitions
- Embodiments of the invention relate generally to tomographic imaging and, more particularly, to an apparatus and method of acquiring tomographic imaging data and increasing temporal resolution of a tomographic image.
- an x-ray source emits a fan-shaped or cone-shaped beam toward a subject or object, such as a patient or a piece of luggage.
- subject and object shall include anything capable of being imaged.
- the beam after being attenuated by the subject, impinges upon an array of radiation detectors.
- the intensity of the attenuated beam of radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject.
- Each detector element of the detector array produces an electrical signal indicative of the attenuated beam received by the detector element.
- the electrical signals are converted to digital signals and transmitted to a data processing system for analysis, which ultimately produces an image.
- X-ray sources typically include x-ray tubes, which emit the x-ray beam from a focal point.
- X-ray detectors typically include a collimator for collimating x-ray beams directed toward the detector, a scintillator adjacent to the collimator for converting x-rays to light energy, and photodiodes for receiving the light energy from the scintillator and producing electrical signals therefrom.
- each scintillator of a scintillator array converts x-rays to light energy and discharges the light energy to a photodiode adjacent thereto.
- Each photodiode detects the light energy and generates a corresponding electrical signal.
- the outputs of the photodiodes are digitized and then transmitted to the data processing system for image reconstruction.
- the x-ray detector extends typically over a circumferential angular range or fan angle of 60°.
- CT imaging encompasses multiple configurations.
- one configuration includes multi-slice or multi-detector CT imaging (MDCT), which may be employed for cardiac imaging.
- MDCT multi-slice or multi-detector CT imaging
- Such a system may be used to generate a cardiac image using imaging data that is obtained over a portion or phase of a cardiac cycle.
- the minimum projection angle of imaging data for image reconstruction is 180° of gantry rotation plus the x-ray detector fan angle.
- the minimum projection angle or temporal aperture is 240° of projection data for image reconstruction, and projection data obtained over this “half-scan” or “short scan” range of coverage may be reconstructed using known reconstruction techniques.
- the amount of time taken to obtain the half-scan projection dataset together with the reconstruction algorithm defines the temporal resolution of the imaging system.
- the temporal resolution is defined as the time taken to obtain minimally adequate data for image reconstruction and the data actually used in the reconstruction.
- short scan data is obtained for 240° of gantry rotation with some type of weighting function, as is understood in the art.
- temporal resolution is thus approximately 135 ms for a gantry rotational speed of 270 ms, and approximately 175 ms for a gantry rotational speed of 350 ms with a Parker weighting, as examples. In many imaging applications, such temporal resolution is adequate to provide images with acceptable motion artifacts.
- the temporal resolution may be inadequate, and images reconstructed with short scan data can suffer from blurring, streaking, or other imaging artifacts.
- Temporal resolution could be improved by increasing the gantry speed and thereby decreasing overall acquisition time. As such, artifacts may be reduced or eliminated because acquisition occurs over a smaller time period.
- weight of the gantry components and other forces acting on the gantry limit the speed at which the gantry can operate, and a reduction in the acquisition time typically includes more powerful x-ray tubes in order to achieve comparable image quality.
- load on the gantry increases generally as a factor that is squared with respect to gantry rotational speed. Thus there are life, reliability, and performance considerations to take into account, and it is highly nontrivial to maintain stability and functionality of components on the gantry at increased gantry speeds.
- Another technique to improve temporal resolution includes a two-tube/two-detector system.
- two tubes operate simultaneously, thus decreasing overall acquisition time and increasing the temporal resolution as compared to a single source system.
- the cost, however, of two-tube/two-detector CT systems can be prohibitive.
- limited space on the gantry prevents the placement of two x-ray tubes and two full-FOV detectors.
- the second detector often covers only a fraction of the desired scan FOV.
- a two-tube/two-detector CT system typically includes significantly more utility resources (i.e., coolant flow, electrical) when compared to a single tube system.
- imaging suites containing such systems sometimes need significant and costly upgrades to provide the additional utility supply.
- SPECT single photon emission computed tomography
- PET positron emission tomography
- Such blurring may be caused by inadequate data acquisition during a given acquisition, or may be caused by an inordinate amount of time that may be used in order to obtain tomographic imaging data having reduced blurring and image artifact characteristics.
- Embodiments of the invention are directed to a method and apparatus for acquiring imaging data and reconstructing an image having an improved temporal resolution.
- a tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer.
- the computer is programmed to acquire a scan dataset of the object, define a temporal subset of the acquired scan dataset for image reconstruction, reconstruct a prior image using the acquired scan imaging dataset, and reconstruct a refined image using the defined subset of scan data and the prior image.
- a method of tomographic imaging includes positioning a detector to receive radiation from a heart of a patient, acquiring projection datasets of the heart using the detector, reconstructing a prior image of the heart using the acquired projection datasets, temporally defining a reduced number of projection datasets from the acquired projection datasets, and reconstructing a final image of the heart using the defined number of projection datasets and using the prior image.
- a computer readable storage medium having stored thereon a computer program comprising instructions which when executed by a computer cause the computer to acquire a set of projections from a cardiac region of a patient, reconstruct a prior image of the cardiac region using the acquired set of projections, and iteratively reconstruct a refined image of the cardiac region using a temporally defined subset of the acquired set of projections and the prior image.
- FIG. 1 is a flowchart illustrating data acquisition and image reconstruction according to embodiments of the invention.
- FIG. 2 is a flowchart illustrating aspects of iterative reconstruction of a medical image according to embodiments of the invention.
- FIG. 3 is a flowchart illustrating aspects of iterative reconstruction of a medical image according to embodiments of the invention.
- FIG. 4 is a flowchart illustrating data acquisition and image reconstruction in a CT system according to an embodiment of the invention.
- FIG. 5 is a pictorial view of a CT system illustrating aspects of data acquisition as applicable to the flowchart illustrated in FIG. 4 .
- FIG. 6 is a flowchart illustrating data acquisition in a CT system according to an embodiment of the invention.
- FIG. 7 is a flowchart illustrating data acquisition and image reconstruction in a CT system according to an embodiment of the invention.
- FIG. 8 is a pictorial view of a SPECT imaging system incorporating embodiments of the invention.
- FIG. 9 is a flowchart illustrating data acquisition and image reconstruction in a SPECT system according to an embodiment of the invention.
- FIG. 10 is a pictorial view and block diagram of a PET system incorporating embodiments of the invention.
- FIG. 11 is a view of a detector ring of the PET system of FIG. 10 .
- FIG. 12 is a flowchart illustrating data acquisition and image reconstruction in a PET system according to an embodiment of the invention.
- FIG. 13 is a pictorial view of a baggage scanning system incorporating embodiments of the invention.
- Tomographic imaging devices comprise x-ray systems, magnetic resonance (MR) systems, ultrasound systems, computed tomography (CT) systems, positron emission tomography (PET) systems, ultrasound, nuclear medicine, single photon emission computed tomography (SPECT) systems, and other types of imaging systems.
- Applications of x-ray sources comprise imaging, medical, security, and industrial inspection applications.
- Embodiments of the invention herein will be described with respect to tomographic imaging systems that include CT, SPECT, and PET. However, it is to be understood that the embodiments of the invention are generally applicable to any imaging system in which data is reconstructed from a temporal window in which data outside of the temporal reconstruction window may be available and employed to improve image reconstruction and reduce blurring and other artifacts therein.
- FIG. 1 is a flowchart general to many tomographic imaging systems illustrating data acquisition and image reconstruction to obtain improved temporal resolution of images according to embodiments of the invention.
- FIGS. 2 and 3 are flowcharts general to many tomographic imaging systems illustrating image reconstruction according to embodiments of the invention.
- FIGS. 4-7 illustrate a CT imaging system and a detailed flowchart illustrating data acquisition and image reconstruction to obtain improved temporal resolution of CT images according to embodiments of the invention. Additional imaging modalities and systems, including SPECT ( FIGS. 8-9 ), PET ( FIGS. 10-12 ), and a CT baggage scanner ( FIG. 13 ) incorporating embodiments of the invention will be further described as well.
- An enabling technology is an image reconstruction method referred to as Prior Image Constrained Compressed Sensing (PICCS).
- PICCS Prior Image Constrained Compressed Sensing
- cardiac tomography images can be accurately reconstructed using projection data acquired over a CT gantry angular range of 90°-130°, and in approximately 120° in one embodiment.
- the temporal resolution of MDCT cardiac imaging can be universally improved by approximately a factor of 2 according to embodiments of the invention, when compared to an image reconstructed using conventional short-scan data acquisition over a 240° angular range.
- Cardiac coronary CT imaging can be successfully performed at high heart rates (e.g., up to 94 beats per minute or greater) using a single-source MDCT scanner and projection data from a single heart beat with gantry rotation times of 400 and 350 ms, as examples, according to embodiments of the invention.
- heart rates e.g., up to 94 beats per minute or greater
- temporal resolution of cardiac CT imaging can be effectively improved by approximately a factor of 2 without modifying any scanner hardware versus a traditional method.
- embodiments of the invention include a method for single-source MDCT scanners to achieve reliable coronary CT imaging for patients at heart rates higher than the current and conventional heart rate limit of 70 bpm using conventional acquisition and reconstruction techniques.
- Embodiments of the invention also allow, for instance, a dual-source MDCT scanner to achieve a higher temporal resolution without hardware modifications versus a dual-source MDCT scanner not using embodiments of the invention. Embodiments also allow for improved SPECT and PET temporal resolution as well.
- Embodiments of the invention include using half of the acquired short-scan CT data and a low temporal resolution prior image for cardiac reconstruction.
- the short-scan angular range is approximately 240°, which is a minimal data sufficiency condition to reconstruct an entire cross section within a scanning field of view.
- the available 120° angular range normally does not enable accurate image reconstruction, and the images are contaminated by limited view-angle shading artifacts. Without a priori information, this type of image reconstruction raises classical tomosynthetic reconstruction issues that usually do not have an algorithm to enable accurate image reconstruction.
- Embodiments of the invention include incorporation of a prior image, which is reconstructed from the short-scan angular range.
- a prior image having known similarity to a reconstructed target image reduces or eliminates limited-view angle shading artifacts, wherein the prior image does not have limited-view-angle shading artifacts. This constraint is imposed by minimizing a cost or objective function that will be explained later.
- Two commonly encountered CT sampling issues in x-ray tomographic reconstruction are view angle undersampling and limited-view-angle sampling. In the first case, the angular range of x-ray source trajectory is sufficient to provide accurate reconstruction, but sampling density is too low.
- the angular range is insufficient for accurate reconstruction, as determined by the known Tuy data sufficiency condition.
- the PICCS algorithm may be applied to address the above two issues, which may appear in different clinical applications. Similar issues related to temporal resolution may also be present in imaging applications in other modalities, such as SPECT and PET imaging applications.
- the PICCS algorithm is used to address the limited-view-angle sampling issue, enabling improved temporal resolution by using CT data from an angular range of about 120° for image reconstruction.
- the prior image is reconstructed using a short-scan angular range of 240°, which is typically 600-700 view angles, while a temporal subset of the short-scan data, used in image reconstruction of approximately 120° includes approximately 300-350 view angles. Because one of the issues being addressed is that of mitigating limited-view angle induced artifacts, embodiments of the invention use the similarity between the prior image and the target image to effectively mitigate the low frequency shading artifacts typically induced by limited-view-angle acquisitions.
- the acquired data should be well distributed in the entire frequency space, although the sampling pattern need not be uniform.
- the frequency space is not well sampled as might occur in limited-view-angle sampling, the shading artifacts are still inevitably present in the reconstructed image.
- the gradient of the difference between the to-be-reconstructed target image, I, and the prior image, I P is minimized.
- dissimilarity between the target image I and the prior image I P is minimized to reduce or eliminate potential limited view-angle shading artifacts in the target image I. This is achieved by minimizing the following objective function:
- I refers to a target image
- I P refers to a prior image.
- CTA coronary computed tomography angiography
- the PICCS algorithm includes an additional term, and a total variation of the to-be-reconstructed target image may be included in the above objective function to remove these potential motion streaks.
- the relative weight of these two terms is prescribed by a weighting factor ⁇ .
- weighting factor is ⁇ ; however, one skilled in the art will recognize that weighting factor ⁇ may be selected based on empirical data or historical experience with respect to each imaging modality.
- mathematics in the PICCS algorithm includes iteratively solving a constrained minimization problem, as described in the following objective function:
- the l 1 -norm in the above equations is the sum of the absolute value of each image pixel in an image.
- P is the system projection operator that calculates the ray sum along a given x-ray path, and Y represents the measured x-ray projection values.
- the discrete gradient transform in Eqn. 2 is defined as:
- ⁇ m,n I ⁇ square root over ([ I ( m+ 1 ,n ) ⁇ I ( m,n )] 2 +[I ( m,n+ 1) ⁇ I ( m,n )] 2 ) ⁇ square root over ([ I ( m+ 1 ,n ) ⁇ I ( m,n )] 2 +[I ( m,n+ 1) ⁇ I ( m,n )] 2 ) ⁇ square root over ([ I ( m+ 1 ,n ) ⁇ I ( m,n )] 2 +[I ( m,n+ 1) ⁇ I ( m,n )] 2 ) ⁇ square root over ([ I ( m+ 1 ,n ) ⁇ I ( m,n )] 2 +[I ( m,n+ 1) ⁇ I ( m,n )] 2 ) ⁇ square root over ([ I ( m+ 1 ,n ) ⁇ I ( m,n )] 2 +[I ( m,n+ 1) ⁇ I ( m
- Eqn. 2 may be solved in two alternating and iterative steps.
- ART algebraic reconstruction technique
- the equality is not fulfilled when data contain noise.
- a relaxation factor has been introduced in the ART algorithm to account for this inexactness in data consistency constraint.
- the PICCS algorithm is applied to improve temporal resolution, only those projection data from a selected range of view angles corresponding to a target cardiac window are used.
- a technique 100 general to many tomographic imaging systems begins at step 102 , and a scan dataset of an object is acquired at step 104 over a given temporal range or during a period that may be particular for the given imaging system.
- the data may be obtained during a time for the gantry to acquire short-scan data.
- the data may be obtained over a period of, for instance, 10 minutes and over 180° of gantry rotation.
- imaging data may be obtained over, for example, a 5 minute period.
- a subset of the acquired data is defined temporally at step 106 .
- the temporal window for the subset of acquired data ranges from approximately 90° to 130° of gantry rotation and is approximately 120° in one embodiment.
- a temporally reduced subset of the acquired dataset is defined that includes a fraction of the acquired dataset having a desired temporal distribution.
- PET likewise, a temporally reduced subset of data is defined for image reconstruction.
- An image referred to herein as a prior image, is reconstructed at step 108 using the scan dataset obtained at step 104 , and a final or refined image is iteratively reconstructed at step 110 using data from the defined temporal subset of data and using the prior image.
- the iterative reconstruction includes generating the objective function using an initial image estimate and the prior image, as described above, minimizing the objective function to generate the target image, and iterating if subsequent target images generated are not within a threshold difference, as described above.
- Technique 100 ends at step 112 .
- Technique 100 may be applied to imaging modalities that include CT, SPECT, and PET.
- technique 100 may be applied to any imaging modality in which data is reconstructed from a temporal window in which data outside of the temporal reconstruction window may be available and employed to improve image reconstruction and reducing blurring and other artifacts therein.
- FIG. 2 is a flowchart showing image reconstruction occurs according to the technique described above and using an iteration technique.
- iterative reconstruction 200 begins at step 202 , and an objective function is generated or formed beginning at step 204 and using an initial image estimate that is refined based on the prior image.
- the objective function formed at step 204 is based on Eqn. 1 above.
- the objective function is sparsified at step 206 .
- the sparsification at step 206 is via the subtraction of the prior image from the target image. As stated above, such subtraction results in a sparsified image.
- sparsification may also be implemented by applying the discrete gradient transform as described above with respect to Eqn. 3.
- sparsification may include subtracting the prior image from the target image, application of the discrete gradient transform, or a combination thereof. Further, it is to be understood that the invention is not limited to the sparsification techniques described, but other sparsification techniques may be applied as commonly understood in the art.
- the l 1 -norm is calculated at step 208 and, as summarized above, the objective function is minimized at step 210 .
- a final or refined image from the minimization is compared with an image generated previously from an earlier iteration at step 212 . If the comparison of successive images is not within the given threshold 214 as described above, then the image estimate is revised at step 216 based on the output from the last minimization, and the objective function is again generated at step 204 . However, if successive images are within the given threshold 218 , the process ends at step 220 . Alternatively, the iterative process can stop after a pre-defined number of iterations.
- Iterative reconstruction technique 300 begins at step 302 , and a weighting factor ( ⁇ in Eqn. 2) is set at step 304 .
- ⁇ is 0.5.
- the objective function is generated beginning at step 306 , and as seen in Eqn. 2 above, the objective function includes a first term and a second term, the combination of which is minimized as described above.
- the first term of the objective function corresponds to steps identified in a first box 308
- the second term of the objective function corresponds to steps identified in a second box 310 .
- First box 308 includes, as described above with respect to the first term of Eqn. 2, subtracting a prior image from an image estimate at step 312 , sparsifying at step 314 and as described above with respect to FIG. 2 , calculating its l 1 -norm at step 316 , and applying the weighting function a thereto at step 318 as seen in Eqn. 2.
- Second box 310 includes sparsifying the image estimate at step 320 and as described above with respect to FIG. 2 , calculating its l 1 -norm at step 322 , and applying the weighting function a thereto at step 324 as seen in Eqn. 2.
- the objective function including both terms of Eqn.
- step 326 the resulting image from the minimization is compared to an image generated previously and from an earlier iteration at step 328 . If the comparison of successive images is not within the given threshold 330 as described above, then the image estimate is revised at step 332 based on the output from the last minimization, and the objective function is again generated at step 306 . However, if successive images are within the given threshold 334 , the process ends at step 336 .
- FIGS. 4 and 5 The operating environment of one embodiment of the invention is described with respect to FIGS. 4 and 5 , and data acquisition and image reconstruction for a CT application is described below with respect to FIGS. 6 and 7 .
- the CT system is described as a sixty-four-slice CT system. However, it will be appreciated by those skilled in the art that the invention is equally applicable for use with other multi-slice configurations. Moreover, the invention will be described with respect to the detection and conversion of x-rays. One skilled in the art will further appreciate that the invention is equally applicable for the detection and conversion of other high frequency electromagnetic energy. This embodiment of the invention will be described with respect to a “third generation” CT scanner, but is equally applicable with other CT systems.
- FIGS. 4 and 5 illustrate, respectively, a pictorial view of a CT system 400 and a schematic block diagram thereof.
- CT imaging system 400 is shown as including a gantry 402 representative of a “third generation” CT scanner.
- Gantry 402 has an x-ray source 404 that projects a beam of x-rays toward a detector assembly 406 on the opposite side of the gantry 402 .
- detector assembly 406 is formed by a plurality of detectors 408 and a data acquisition systems (DAS) 410 .
- DAS data acquisition systems
- the plurality of detectors 408 sense projected x-rays 412 that pass through a medical patient 414 having, in one embodiment, a motion monitor 416 , such as an electrocardiographic device (ECG), is attached thereto.
- DAS 410 converts data from detectors 408 to digital signals for subsequent processing.
- Each detector 408 produces an analog electrical signal that represents the intensity of an impinging x-ray beam and hence the attenuated beam as it passes through medical patient 414 .
- gantry 402 and the components mounted thereon rotate about a center of rotation 418 .
- control mechanism 420 includes a motion monitoring system 422 configured to acquire data from motion monitor 416 and pass patient motion information to a computer 424 . Examples of the patient motion information include respiratory and cardiac phase information.
- Control mechanism 420 includes an x-ray controller 426 that provides power and timing signals to x-ray source 404 and a gantry motor controller 428 that controls a rotational speed and position of gantry 402 .
- An image reconstructor 430 receives sampled and digitized x-ray data from data acquisition systems (DAS) 410 and performs high speed reconstruction. The reconstructed image is applied as an input to computer 424 , which stores the image in a mass storage device 432 .
- DAS data acquisition systems
- Computer 424 also receives commands and scanning parameters from an operator via an operator console 434 that includes an operator interface, such as a keyboard, mouse, voice activated controller, or any other suitable input apparatus.
- An associated display 436 allows the operator to observe the reconstructed image and other data from computer 424 .
- the operator supplied commands and parameters are used by computer 424 to provide control signals and information to data acquisition systems (DAS) 410 , x-ray controller 426 and gantry motor controller 428 .
- DAS data acquisition systems
- computer 424 operates a table motor controller 438 which controls a motorized table 440 to position medical patient 414 and gantry 402 .
- motorized table 440 moves medical patient 414 through a gantry opening 442 of FIG. 4 in whole or in part.
- CT imaging system 400 includes a second x-ray source 444 and a corresponding second detector assembly 446 positioned to receive x-rays passing through medical patient 414 in order to obtain additional imaging data.
- the second source 444 /detector 446 combination may be controlled and used to obtain imaging data similarly to that illustrated with respect to x-ray source 404 and detector assembly or collimator 406 and may be used, for instance, to improve the overall temporal resolution of CT imaging system 400 while incorporating embodiments of the invention.
- FIGS. 6 and 7 illustrate acquisition and reconstruction of imaging data in a CT system, such as CT imaging system 400 of FIGS. 4 and 5 , according to an embodiment of the invention.
- Representation 500 includes an object 502 , which may be a heart within a patient, within an inner bore 504 of a CT gantry. Imaging data may be obtained of object 502 over a short-scan angular range 506 or over a portion thereof. In the illustrated embodiment, short-scan angular range 506 is 240°. A temporal subset 508 of the acquired short-scan data may be defined and reconstructed, according to the invention.
- a technique 510 begins at step 512 and includes acquiring a short-scan CT dataset at step 514 .
- a temporal subset of data, ranging between 90° and 130°, is defined at step 516 , and a prior image is reconstructed of the object using the short-scan data obtained over 240° of gantry rotation at step 518 .
- the prior image is reconstructed with conventional means and as understood within the art.
- the prior image is reconstructed using a known filtered backprojection (FBP) technique.
- FBP filtered backprojection
- a final or refined image is iteratively reconstructed at step 520 as described above with respect to FIGS.
- the process ends at step 522 when subsequent images generated are below a given threshold of difference, as understood in the art.
- the process may be stopped when a squared difference of two successive images reaches a predetermined threshold.
- the process may stop after a pre-defined number of iterations.
- FIG. 8 illustrates an exemplary SPECT system 600 for acquiring and processing image data in accordance with embodiments of the invention.
- SPECT system 600 includes a collimator assembly 602 and a detector assembly 604 .
- SPECT system 600 also includes a control module 606 , an image reconstruction and processing module 608 , an operator workstation 610 , and an image display workstation 612 .
- a subject support 614 may be moved into position in a field-of-view (FOV) 616 of SPECT system 600 .
- subject support 614 is configured to support a subject 618 (e.g., a human patient, a small animal, a plant, a porous object, etc.) in position for scanning.
- subject support 614 may be stationary, while SPECT system 600 may be moved into position around subject 618 for scanning.
- Subject 618 may be supported in any suitable position for scanning.
- subject 618 may be supported in FOV 616 in a generally vertical position, a generally horizontal position, or any other suitable position (e.g., inclined) for the desired scan.
- subject 618 may have a motion monitoring system 620 , such as an ECG, attached thereto and connected to a motion monitor 622 within control module 606 .
- motion monitoring system 620 may be controlled and used to obtain patient motion information such as respiratory and cardiac phase information, as examples.
- subject 618 is typically injected with a solution that contains a radioactive tracer.
- the solution is distributed and absorbed throughout subject 618 in different degrees, depending on the tracer employed and, in the case of living subjects, the functioning of the organs and tissues.
- the radioactive tracer emits electromagnetic rays 624 (e.g., photons or gamma quanta) known as “gamma rays” during a nuclear decay event.
- Collimator assembly 602 receives gamma rays 624 emanating from FOV 616 .
- Collimator assembly 602 is generally configured to limit and define a direction and angular divergence of gamma rays 624 .
- collimator assembly 602 is disposed between detector assembly 604 and FOV 616 .
- Gamma rays 624 that pass through collimator assembly 602 impact detector assembly 604 .
- detector assembly 604 may include a plurality of detector elements configured to detect gamma rays 624 emanating from subject 618 in FOV 616 and passing through one or more apertures defined by collimator assembly 602 .
- each of the plurality of detector elements in detector assembly 604 produces an electrical signal in response to the impact of the gamma rays 624 .
- the detector elements may be arranged in detector assembly 604 in any suitable manner.
- Detector assembly 604 may extend at least partially around FOV 616 .
- detector assembly 604 may include modular detector elements arranged around FOV 616 .
- detector assembly 406 may be arranged in a ring that may extend up to 360° around FOV 616 .
- detector assembly 604 may extend from about 180° to about 360° around FOV 616 .
- collimator assembly 602 may be configured to rotate about subject 618 positioned within FOV 616 .
- collimator assembly 602 may be configured to rotate with respect to detector assembly 604 .
- Detector assembly 604 may be stationary while collimator assembly 602 may be configured to rotate about FOV 616 .
- detector assembly 604 may rotate while collimator assembly 602 is stationary.
- collimator assembly 602 and detector assembly 604 may both be configured to rotate, either together or independently of one another. Alternatively, if sufficient pinhole apertures and/or slit apertures are provided through collimator assembly 602 or if the slit apertures are orthogonal to the longitudinal axis of collimator assembly 602 , then no rotation may be required.
- control module 606 includes a motor controller 626 and a data acquisition module 628 .
- gantry motor controller 626 may control a rotational speed and position of collimator assembly 602 , detector assembly 604 , and/or a position of subject support 614 .
- Data acquisition module 628 may be configured to obtain signals generated in response to impact of gamma rays 624 with detector assembly 604 .
- data acquisition module 628 may receive sampled electrical signals from detector assembly 604 and convert the data to digital signals for subsequent processing by image reconstruction and processing module 608 . Any suitable technique for data acquisition may be used with SPECT system 600 .
- the data needed for image reconstruction may be acquired in a list or a frame mode. Data may be acquired, parsed, and reconstructed according to embodiments of the invention.
- Technique 700 begins at step 702 and includes acquiring, at step 704 , a minimum SPECT dataset of an object such as a heart within patient 618 as illustrated above in FIG. 8 .
- gantry speed is relatively slow compared to CT system 400 described above and is in terms of minutes (as opposed to sub-second gantry rotation in typical CT imaging).
- data is acquired in one embodiment for a 10 minute period and over 180° of rotation, since parallel hole collimation can be used and as understood in the art.
- the fraction of the acquired dataset is defined temporally (that is, having using a subset of acquired data having a desired temporal distribution) at step 706 , and a prior image is reconstructed at step 708 using the data acquired over the 180° of rotation.
- the imaging data is iteratively reconstructed at step 710 , and the process ends at step 712 .
- the data acquisition can be divided into two steps. In the first step, projections over 180° (for parallel collimation) or 180° (for fan-beam or cone-beam collimator) are quickly collected.
- Images are reconstructed and serve as the prior images. Note that poor temporal resolution due to slow data acquisition results.
- a pinhole collimator is used to acquire the projections simultaneously over a limited angular range while the gantry is stationary. Since the projection data are acquired at the same time (without gantry rotation), the data acquisition can be effectively gated by the physiological signals such as ECG. The projections acquired with the pinhole collimator are used for the iterative reconstruction to refine the prior image.
- FIG. 10 is a block diagram of an exemplary embodiment of a PET system 800 in which various embodiments of the invention may be implemented.
- PET system 800 includes a plurality of detector ring assemblies. One such detector ring assembly, detector ring assembly 802 , is illustrated in FIG. 11 .
- PET system 800 further includes a controller 804 to control normalization and image reconstruction processes.
- Controller 804 includes a processor 806 and an operator workstation 808 .
- Processor 806 includes a data acquisition processor 810 and an image reconstruction processor 812 that are interconnected and connected with detector ring assembly 802 via a communication link 814 .
- PET system 800 acquires scan data and transmits the data to data acquisition processor 810 . The scanning operation is controlled from operator workstation 808 .
- the data acquired by data acquisition processor 810 is reconstructed using image reconstruction processor 812 .
- Detector ring assembly 802 includes a central opening 816 in which a patient or object 818 may be positioned using, for example, a motorized table (not shown) that is aligned with a central axis 820 of detector ring assembly 802 .
- the motorized table moves object 818 into central opening 816 of detector ring assembly 802 in response to one or more commands received from operator workstation 808 .
- a PET scanner controller 822 also referred to as the gantry controller, is provided (e.g., mounted) within PET system 800 . PET scanner controller 822 responds to commands received from operator workstation 808 through communication link 814 .
- Detector ring assembly 802 includes a plurality of detector units 824 (e.g., in one known PET system, there are 420 crystals per ring, and 24 rings in the scanner). While not shown, it is contemplated that each detector unit 824 includes a set of scintillator crystals arranged in a matrix disposed in front of a plurality of photomultiplier tubes (e.g., four tubes). When a photon collides with a scintillator crystal on a detector unit 824 , it produces a scintilla on the scintillator crystal. Each photomultiplier tube produces an analog signal on a communication line 826 when a scintillation event occurs. A set of acquisition circuits 828 is provided to receive these analog signals.
- each detector unit 824 includes a set of scintillator crystals arranged in a matrix disposed in front of a plurality of photomultiplier tubes (e.g., four tubes). When a photon collides with a scintill
- Acquisition circuits 828 produce digital signals indicating a location in 3-dimensional (3D) space and a total energy of the event. Acquisition circuits 828 also produce an event detection pulse, which indicates the time or moment the scintillation event occurred. These digital signals are transmitted through a communication link 830 such as a cable, for example, to an event locator circuit 832 in data acquisition processor 810 .
- PET system 800 includes a motion monitoring system 834 , such as an ECG, attached to object 818 and attached to acquisition circuit 828 that may be used to obtain patient motion information such as respiratory and cardiac phase information, as examples, via data acquisition processor 810 .
- Data acquisition processor 810 includes event locator circuit 832 , an acquisition CPU 836 and a coincidence detector 838 .
- Data acquisition processor 810 periodically samples the signals produced by acquisition circuits 828 .
- Acquisition CPU 836 controls communications on a back-plane bus 840 and on communication link 814 .
- Event locator circuit 832 processes information regarding each valid event and provides a set of digital numbers or values indicative of the detected event. For example, this information indicates when the event took place and the position of the scintillation crystal that detected the event.
- An event data packet (not shown) containing the event information is communicated to coincidence detector 838 through back-plane bus 840 .
- Coincidence detector 838 receives the event data packets from event locator circuit 832 and determines if any two of the detected events are in coincidence.
- Coincidence is determined by a number of factors.
- time markers in each event data packet should be within a predetermined time period of each other such as, for example, 12.5 nanoseconds.
- a line of response (LOR) formed by a straight line joining the two detectors that detect the coincidence event should pass through the central opening 816 or through a field of view in PET system 800 . Events that cannot be paired are discarded.
- Coincident event pairs are located and recorded as a coincidence data packet that is communicated through a communication link 842 to a sorter 844 in image reconstruction processor 812 .
- Image reconstruction processor 812 includes sorter 844 , a memory module 846 , an image CPU 848 , an array processor 850 and a back-plane bus 852 .
- Sorter 844 counts all events occurring along each projection ray and organizes them into 3D data. This 3D data (or sinograms) is organized, in one exemplary embodiment, as a data array 854 .
- Data array 854 is stored in memory module 846 .
- Back-plane bus 852 is linked to communication link 814 through image CPU 848 , and image CPU 848 controls communication through back-plane bus 852 .
- Array processor 850 is also connected to back-plane bus 852 .
- Array processor 850 receives data array 854 as an input and reconstructs images in the form of image arrays 856 . Resulting image arrays 856 are stored in memory module 846 .
- Images stored in image arrays 856 are communicated by image CPU 848 to operator workstation 808 .
- Operator workstation 808 includes a CPU 858 , a display device 860 and an input device 862 .
- Acquisition CPU 858 connects to communication link 814 and receives inputs (e.g., user commands) from input device 862 .
- Input device 862 may be, for example, a keyboard, mouse, or a touch-screen panel.
- an operator can control calibration of PET system 800 and can control positioning of object 818 for a scan.
- an operator can control display of a resulting image on display device 860 and perform image-enhancement functions using programs executed by acquisition CPU 858 .
- the data array received by array processor 850 may be corrected for errors before being reconstructed.
- the level of correction may be based on, for example, a desired or required resolution level for a reconstructed image.
- One correction includes removing scatter coincidences from the image data.
- FIG. 11 illustrates a single scatter coincidence with respect to detector ring assembly 802 of FIG. 10 .
- An annihilation event occurs at an annihilation point 864 inside object 818 .
- the annihilation event produces a photon 866 that impacts a detector element 868 at a first detection point 870 , and a scattered photon 872 that impacts a detector element 874 at a second detection point 876 .
- Scattered photon 872 is scattered from a scattering point 878 inside object 818 .
- Detector element 868 records a time at which photon 866 is detected and a time at which scattered photon 872 is detected.
- Detector element 868 and detector element 874 form a detector pair.
- detector element pair 868 / 874 map to a unique sinogram bin with indices, r and ⁇ , and indices r and ⁇ denote a radial distance from the center of the detector ring and an angle of the line joining 868 and 876 from a horizontal axis, respectively.
- a difference between detection times for first detection point 870 and second detection point 876 maps to a unique time bin index for the time-of-flight scatter sinogram.
- the total number of annihilation events and the time at which each event is recorded is sent to processor 806 (shown in FIG. 11 ). Based on the received information, the detected events are binned into sinograms with indices r and ⁇ , used to generate a time-of-flight scatter sinogram S(r, ⁇ , t).
- Technique 900 begins at step 902 includes acquiring at step 904 a PET dataset of an object, such as a heart within patient or object 818 as illustrated above in FIG. 10 .
- a conventional PET dataset may be obtained (e.g., over a 5 minute period) and used to generate the prior image, and reconstructed according to the invention.
- Data collected over a fractional period of time i.e., a defined temporal window
- a quiescent time period may be selected within the original acquisition window (5 minutes, in this example) to iteratively produce a final or refined image.
- the final image exhibits the noise property of the longer scan time (e.g., 5 minutes) but exhibits the motion property of an improved temporal window.
- a conventional or normal PET dataset is obtained of the object at step 904 .
- a fraction of the acquired dataset is defined temporally at step 906 , a prior image is reconstructed using the dataset obtained at step 908 , and an image is iteratively reconstructed as describe above at step 910 with respect to FIGS. 2 and 3 .
- the process ends at step 912 .
- a package/baggage inspection system 1000 that can use the image acquisition and reconstructions techniques according to embodiments of the invention and which includes a rotatable gantry 1002 having an opening 1004 therein through which packages or pieces of baggage may pass.
- the rotatable gantry 1002 houses one or more x-ray energy sources 1006 as well as a detector assembly 1008 having scintillator arrays comprised of scintillator cells.
- a conveyor system 1010 is also provided and includes a conveyor belt 1012 supported by structure 1014 to automatically and continuously pass packages or baggage pieces 1016 through opening 1004 to be scanned.
- Objects 1016 are passed through opening 1004 by conveyor belt 1012 , imaging data is then acquired, and the conveyor belt 1012 removes the packages 1016 from opening 1004 in a controlled and continuous manner.
- postal inspectors, baggage handlers, and other security personnel may non-invasively inspect the contents of packages 1016 for explosives, knives, guns, contraband, etc.
- An implementation of embodiments of the invention in an example comprises a plurality of components such as one or more of electronic components, hardware components, and/or computer software components. A number of such components can be combined or divided in an implementation of the embodiments of the invention.
- An exemplary component of an implementation of the embodiments of the invention employs and/or comprises a set and/or series of computer instructions written in or implemented with any of a number of programming languages, as will be appreciated by those skilled in the art.
- An implementation of the embodiments of the invention in an example employs one or more computer readable storage media.
- An example of a computer-readable signal-bearing medium for an implementation of the embodiments of the invention comprises the recordable data storage medium of the image reconstructor 34 , and/or the mass storage device 38 of the computer 36 .
- a computer-readable storage medium for an implementation of the embodiments of the invention in an example comprises one or more of a magnetic, electrical, optical, biological, and/or atomic data storage medium.
- an implementation of the computer-readable signal-bearing medium comprises floppy disks, magnetic tapes, CD-ROMs, DVD-ROMs, hard disk drives, and/or electronic memory.
- a technical contribution for the disclosed method and apparatus is that it provides for a computer-implemented apparatus and method of tomographic imaging and, more particularly, an apparatus and method of acquiring tomographic imaging data and increasing temporal resolution of a tomographic image.
- a tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer.
- the computer is programmed to acquire a scan dataset of the object, define a temporal subset of the acquired scan dataset for image reconstruction, reconstruct a prior image using the acquired scan imaging dataset, and reconstruct a refined image using the defined subset of scan data and the prior image.
- a method of tomographic imaging includes positioning a detector to receive radiation from a heart of a patient, acquiring projection datasets of the heart using the detector, reconstructing a prior image of the heart using the acquired projection datasets, temporally defining a reduced number of projection datasets from the acquired projection datasets, and reconstructing a final image of the heart using the defined number of projection datasets and using the prior image.
- a computer readable storage medium having stored thereon a computer program comprising instructions which when executed by a computer cause the computer to acquire a set of projections from a cardiac region of a patient, reconstruct a prior image of the cardiac region using the acquired set of projections, and iteratively reconstruct a refined image of the cardiac region using a temporally defined subset of the acquired set of projections and the prior image.
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Abstract
A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer. The computer is programmed to acquire a scan dataset of the object, define a temporal subset of the acquired scan dataset for image reconstruction, reconstruct a prior image using the acquired scan imaging dataset, and reconstruct a refined image using the defined subset of scan data and the prior image.
Description
- The present application is a continuation of and claims priority to U.S. patent application Ser. No. 12/950,241 filed Nov. 19, 2010, which is a continuation of U.S. patent application Ser. No. 12/775,968 filed May 7, 2010, both of which claim priority to U.S. Provisional Application 61/314,937 filed Mar. 17, 2010, the disclosures of which are incorporated herein.
- Embodiments of the invention relate generally to tomographic imaging and, more particularly, to an apparatus and method of acquiring tomographic imaging data and increasing temporal resolution of a tomographic image.
- Typically, in x-ray systems, such as a computed tomography (CT) imaging systems, an x-ray source emits a fan-shaped or cone-shaped beam toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” shall include anything capable of being imaged. The beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the attenuated beam of radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject. Each detector element of the detector array produces an electrical signal indicative of the attenuated beam received by the detector element. The electrical signals are converted to digital signals and transmitted to a data processing system for analysis, which ultimately produces an image.
- Generally, the x-ray source and the detector array are rotated about the gantry within an imaging plane and around the subject. X-ray sources typically include x-ray tubes, which emit the x-ray beam from a focal point. X-ray detectors typically include a collimator for collimating x-ray beams directed toward the detector, a scintillator adjacent to the collimator for converting x-rays to light energy, and photodiodes for receiving the light energy from the scintillator and producing electrical signals therefrom. Typically, each scintillator of a scintillator array converts x-rays to light energy and discharges the light energy to a photodiode adjacent thereto. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are digitized and then transmitted to the data processing system for image reconstruction. The x-ray detector extends typically over a circumferential angular range or fan angle of 60°.
- CT imaging encompasses multiple configurations. For example, one configuration includes multi-slice or multi-detector CT imaging (MDCT), which may be employed for cardiac imaging. Such a system may be used to generate a cardiac image using imaging data that is obtained over a portion or phase of a cardiac cycle. Conventionally, the minimum projection angle of imaging data for image reconstruction is 180° of gantry rotation plus the x-ray detector fan angle. Thus, with a typical fan angle of 60°, the minimum projection angle or temporal aperture is 240° of projection data for image reconstruction, and projection data obtained over this “half-scan” or “short scan” range of coverage may be reconstructed using known reconstruction techniques. The amount of time taken to obtain the half-scan projection dataset together with the reconstruction algorithm, in this conventional example, defines the temporal resolution of the imaging system. In other words, the temporal resolution is defined as the time taken to obtain minimally adequate data for image reconstruction and the data actually used in the reconstruction. In this case, short scan data is obtained for 240° of gantry rotation with some type of weighting function, as is understood in the art.
- As such, the range of angular coverage (or temporal aperture) and gantry rotational speed are primary factors that define temporal resolution in a MDCT scanner. In a typical single source MDCT scanner, temporal resolution is thus approximately 135 ms for a gantry rotational speed of 270 ms, and approximately 175 ms for a gantry rotational speed of 350 ms with a Parker weighting, as examples. In many imaging applications, such temporal resolution is adequate to provide images with acceptable motion artifacts.
- Due to motion of the heart during the 240° of gantry rotation during which short scan data is obtained, however, the temporal resolution may be inadequate, and images reconstructed with short scan data can suffer from blurring, streaking, or other imaging artifacts. Thus, it is desirable to increase temporal resolution in cardiac imaging applications and in applications in general where imaging artifacts may occur due to object motion. In some applications, it would be desirable to increase the temporal resolution by a factor of up to 2, or even greater, in order to improve images and reduce or eliminate image artifacts.
- Temporal resolution could be improved by increasing the gantry speed and thereby decreasing overall acquisition time. As such, artifacts may be reduced or eliminated because acquisition occurs over a smaller time period. Generally, however, weight of the gantry components and other forces acting on the gantry limit the speed at which the gantry can operate, and a reduction in the acquisition time typically includes more powerful x-ray tubes in order to achieve comparable image quality. As is known in the art, though, load on the gantry increases generally as a factor that is squared with respect to gantry rotational speed. Thus there are life, reliability, and performance considerations to take into account, and it is highly nontrivial to maintain stability and functionality of components on the gantry at increased gantry speeds.
- Another technique to improve temporal resolution includes a two-tube/two-detector system. In such a system, two tubes operate simultaneously, thus decreasing overall acquisition time and increasing the temporal resolution as compared to a single source system. The cost, however, of two-tube/two-detector CT systems can be prohibitive. In addition, limited space on the gantry prevents the placement of two x-ray tubes and two full-FOV detectors. Thus, the second detector often covers only a fraction of the desired scan FOV. Further, a two-tube/two-detector CT system typically includes significantly more utility resources (i.e., coolant flow, electrical) when compared to a single tube system. Thus, imaging suites containing such systems sometimes need significant and costly upgrades to provide the additional utility supply. And, with an increased number of operational components, reliability of the overall system may be compromised because of the doubling in the number of primary components (i.e., tube, detector, and DAS). Thus, though such a system may improve temporal resolution, the increased temporal resolution comes at the cost of increased initial system expense and cost of ongoing operation, costly suite upgrades, and possibly a reduced system reliability when compared to a single source system.
- Further, other imaging modalities such as single photon emission computed tomography (SPECT) and positron emission tomography (PET) also suffer from blurring and other image artifacts due to cardiac or respiratory motions. Such blurring may be caused by inadequate data acquisition during a given acquisition, or may be caused by an inordinate amount of time that may be used in order to obtain tomographic imaging data having reduced blurring and image artifact characteristics.
- Thus there is a need for a system and method that minimizes motion blurring in tomographic imaging in a cost-effective and overall efficient manner.
- Embodiments of the invention are directed to a method and apparatus for acquiring imaging data and reconstructing an image having an improved temporal resolution.
- According to an aspect of the invention, a tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer. The computer is programmed to acquire a scan dataset of the object, define a temporal subset of the acquired scan dataset for image reconstruction, reconstruct a prior image using the acquired scan imaging dataset, and reconstruct a refined image using the defined subset of scan data and the prior image.
- According to another aspect of the invention, a method of tomographic imaging includes positioning a detector to receive radiation from a heart of a patient, acquiring projection datasets of the heart using the detector, reconstructing a prior image of the heart using the acquired projection datasets, temporally defining a reduced number of projection datasets from the acquired projection datasets, and reconstructing a final image of the heart using the defined number of projection datasets and using the prior image.
- According to yet another aspect of the invention, a computer readable storage medium having stored thereon a computer program comprising instructions which when executed by a computer cause the computer to acquire a set of projections from a cardiac region of a patient, reconstruct a prior image of the cardiac region using the acquired set of projections, and iteratively reconstruct a refined image of the cardiac region using a temporally defined subset of the acquired set of projections and the prior image.
- These and other advantages and features will be more readily understood from the following detailed description of preferred embodiments of the invention that is provided in connection with the accompanying drawings.
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FIG. 1 is a flowchart illustrating data acquisition and image reconstruction according to embodiments of the invention. -
FIG. 2 is a flowchart illustrating aspects of iterative reconstruction of a medical image according to embodiments of the invention. -
FIG. 3 is a flowchart illustrating aspects of iterative reconstruction of a medical image according to embodiments of the invention. -
FIG. 4 is a flowchart illustrating data acquisition and image reconstruction in a CT system according to an embodiment of the invention. -
FIG. 5 is a pictorial view of a CT system illustrating aspects of data acquisition as applicable to the flowchart illustrated inFIG. 4 . -
FIG. 6 is a flowchart illustrating data acquisition in a CT system according to an embodiment of the invention. -
FIG. 7 is a flowchart illustrating data acquisition and image reconstruction in a CT system according to an embodiment of the invention. -
FIG. 8 is a pictorial view of a SPECT imaging system incorporating embodiments of the invention. -
FIG. 9 is a flowchart illustrating data acquisition and image reconstruction in a SPECT system according to an embodiment of the invention. -
FIG. 10 is a pictorial view and block diagram of a PET system incorporating embodiments of the invention. -
FIG. 11 is a view of a detector ring of the PET system ofFIG. 10 . -
FIG. 12 is a flowchart illustrating data acquisition and image reconstruction in a PET system according to an embodiment of the invention. -
FIG. 13 is a pictorial view of a baggage scanning system incorporating embodiments of the invention. - Tomographic imaging devices comprise x-ray systems, magnetic resonance (MR) systems, ultrasound systems, computed tomography (CT) systems, positron emission tomography (PET) systems, ultrasound, nuclear medicine, single photon emission computed tomography (SPECT) systems, and other types of imaging systems. Applications of x-ray sources comprise imaging, medical, security, and industrial inspection applications. Embodiments of the invention herein will be described with respect to tomographic imaging systems that include CT, SPECT, and PET. However, it is to be understood that the embodiments of the invention are generally applicable to any imaging system in which data is reconstructed from a temporal window in which data outside of the temporal reconstruction window may be available and employed to improve image reconstruction and reduce blurring and other artifacts therein.
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FIG. 1 is a flowchart general to many tomographic imaging systems illustrating data acquisition and image reconstruction to obtain improved temporal resolution of images according to embodiments of the invention.FIGS. 2 and 3 are flowcharts general to many tomographic imaging systems illustrating image reconstruction according to embodiments of the invention.FIGS. 4-7 illustrate a CT imaging system and a detailed flowchart illustrating data acquisition and image reconstruction to obtain improved temporal resolution of CT images according to embodiments of the invention. Additional imaging modalities and systems, including SPECT (FIGS. 8-9 ), PET (FIGS. 10-12 ), and a CT baggage scanner (FIG. 13 ) incorporating embodiments of the invention will be further described as well. - An enabling technology according to embodiments of the invention is an image reconstruction method referred to as Prior Image Constrained Compressed Sensing (PICCS). Using the method, cardiac tomography images can be accurately reconstructed using projection data acquired over a CT gantry angular range of 90°-130°, and in approximately 120° in one embodiment. As a result and as understood in the art, the temporal resolution of MDCT cardiac imaging can be universally improved by approximately a factor of 2 according to embodiments of the invention, when compared to an image reconstructed using conventional short-scan data acquisition over a 240° angular range.
- Cardiac coronary CT imaging can be successfully performed at high heart rates (e.g., up to 94 beats per minute or greater) using a single-source MDCT scanner and projection data from a single heart beat with gantry rotation times of 400 and 350 ms, as examples, according to embodiments of the invention. As will be illustrated, using the PICCS method, temporal resolution of cardiac CT imaging can be effectively improved by approximately a factor of 2 without modifying any scanner hardware versus a traditional method. Thus, embodiments of the invention include a method for single-source MDCT scanners to achieve reliable coronary CT imaging for patients at heart rates higher than the current and conventional heart rate limit of 70 bpm using conventional acquisition and reconstruction techniques. Embodiments of the invention also allow, for instance, a dual-source MDCT scanner to achieve a higher temporal resolution without hardware modifications versus a dual-source MDCT scanner not using embodiments of the invention. Embodiments also allow for improved SPECT and PET temporal resolution as well.
- Embodiments of the invention include using half of the acquired short-scan CT data and a low temporal resolution prior image for cardiac reconstruction. As stated, the short-scan angular range is approximately 240°, which is a minimal data sufficiency condition to reconstruct an entire cross section within a scanning field of view. When the cardiac window is narrowed to half of the short-scan window, the available 120° angular range normally does not enable accurate image reconstruction, and the images are contaminated by limited view-angle shading artifacts. Without a priori information, this type of image reconstruction raises classical tomosynthetic reconstruction issues that usually do not have an algorithm to enable accurate image reconstruction.
- Embodiments of the invention include incorporation of a prior image, which is reconstructed from the short-scan angular range. Using a prior image having known similarity to a reconstructed target image reduces or eliminates limited-view angle shading artifacts, wherein the prior image does not have limited-view-angle shading artifacts. This constraint is imposed by minimizing a cost or objective function that will be explained later. Two commonly encountered CT sampling issues in x-ray tomographic reconstruction are view angle undersampling and limited-view-angle sampling. In the first case, the angular range of x-ray source trajectory is sufficient to provide accurate reconstruction, but sampling density is too low. In the second case, the angular range is insufficient for accurate reconstruction, as determined by the known Tuy data sufficiency condition. However, when a prior image of the image object is available, the PICCS algorithm may be applied to address the above two issues, which may appear in different clinical applications. Similar issues related to temporal resolution may also be present in imaging applications in other modalities, such as SPECT and PET imaging applications.
- According to embodiments of the invention, the PICCS algorithm is used to address the limited-view-angle sampling issue, enabling improved temporal resolution by using CT data from an angular range of about 120° for image reconstruction. According to embodiments of the invention, the prior image is reconstructed using a short-scan angular range of 240°, which is typically 600-700 view angles, while a temporal subset of the short-scan data, used in image reconstruction of approximately 120° includes approximately 300-350 view angles. Because one of the issues being addressed is that of mitigating limited-view angle induced artifacts, embodiments of the invention use the similarity between the prior image and the target image to effectively mitigate the low frequency shading artifacts typically induced by limited-view-angle acquisitions.
- The following describes mathematical details of the PICCS algorithm. When a prior image is available, it can then be utilized to significantly sparsify a target image. When a subtraction of the target image I from a prior image IP is performed, the subtracted image, I−IP (i.e., a difference image), is significantly more sparse than either IP or I. When the total number of nonzero image pixels is counted in these three images (I, IP, and the difference image), there are only typically a few thousand pixels (2700 in one example) in the difference image. As understood in the art, this is only approximately 3% of the total pixels in the target image I or the prior image IP. The sparser an image is, the fewer data are needed to accurately reconstruct the image. To enable this image reconstruction, the acquired data should be well distributed in the entire frequency space, although the sampling pattern need not be uniform. When the frequency space is not well sampled as might occur in limited-view-angle sampling, the shading artifacts are still inevitably present in the reconstructed image.
- In the PICCS algorithm, the gradient of the difference between the to-be-reconstructed target image, I, and the prior image, IP, is minimized. As such, dissimilarity between the target image I and the prior image IP is minimized to reduce or eliminate potential limited view-angle shading artifacts in the target image I. This is achieved by minimizing the following objective function:
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min[|∇m,n(I−IP)|l1 ]; Eqn. 1. - Referring to Eqn. 1, I refers to a target image, and IP refers to a prior image. However, in coronary computed tomography angiography (CTA) imaging, for example, images reconstructed using Eqn. 1 include significant motion induced streaking artifacts present in the prior image. Thus, the PICCS algorithm includes an additional term, and a total variation of the to-be-reconstructed target image may be included in the above objective function to remove these potential motion streaks. According to the algorithm, the relative weight of these two terms is prescribed by a weighting factor α. In one embodiment weighting factor is α; however, one skilled in the art will recognize that weighting factor α may be selected based on empirical data or historical experience with respect to each imaging modality. As a result, mathematics in the PICCS algorithm includes iteratively solving a constrained minimization problem, as described in the following objective function:
-
min[α|∇m,n(I−IP)|l1 +(1−α)|∇m,n(I)|l1 ]; Eqn 2, - such that PI=Y.
- The l1-norm in the above equations is the sum of the absolute value of each image pixel in an image. P is the system projection operator that calculates the ray sum along a given x-ray path, and Y represents the measured x-ray projection values. The discrete gradient transform in Eqn. 2 is defined as:
-
∇m,n I=√{square root over ([I(m+1,n)−I(m,n)]2 +[I(m,n+1)−I(m,n)]2)}{square root over ([I(m+1,n)−I(m,n)]2 +[I(m,n+1)−I(m,n)]2)}{square root over ([I(m+1,n)−I(m,n)]2 +[I(m,n+1)−I(m,n)]2)}{square root over ([I(m+1,n)−I(m,n)]2 +[I(m,n+1)−I(m,n)]2)}; Eqn. 3. - Several methods can be used to solve the constrained minimization problem in Eqn. 2. In one example, Eqn. 2 may be solved in two alternating and iterative steps. In the first step, images may be reconstructed using a commonly known algorithm known as the algebraic reconstruction technique (ART) to meet the constraint PI=Y. Regarding this data constraint condition, the equality is not fulfilled when data contain noise. Thus, a relaxation factor has been introduced in the ART algorithm to account for this inexactness in data consistency constraint. Also regarding the data constraint condition, when the PICCS algorithm is applied to improve temporal resolution, only those projection data from a selected range of view angles corresponding to a target cardiac window are used.
- In the second alternating step, the objective function of Eqn. 2 is thus minimized using a known gradient descent method, according to embodiments of the invention. Because the CT projection data set or temporal subset of data is limited to an angular range of about 120°, when the constraint PI=Y is imposed in the ART step, shading artifacts may appear in the ART image, which make it dissimilar from the prior image. Thus, when the objective function of Eqn. 2 is minimized, motion streaks and dissimilarity relative to the prior image will be reduced in the reconstructed image. As such, Eqn. 2 is iteratively solved according to embodiments of the invention, and the process of iterating may be stopped when successively iterated images have a difference that is within a given threshold. In one embodiment the threshold is met when a squared difference of two successively iterated images reaches a predetermined threshold.
- Thus, in general and according to embodiments of the invention, and as illustrated in
FIG. 1 , atechnique 100 general to many tomographic imaging systems begins atstep 102, and a scan dataset of an object is acquired atstep 104 over a given temporal range or during a period that may be particular for the given imaging system. For example, for CT imaging, the data may be obtained during a time for the gantry to acquire short-scan data. For SPECT, the data may be obtained over a period of, for instance, 10 minutes and over 180° of gantry rotation. For PET, imaging data may be obtained over, for example, a 5 minute period. A subset of the acquired data is defined temporally atstep 106. In the case of CT, the temporal window for the subset of acquired data ranges from approximately 90° to 130° of gantry rotation and is approximately 120° in one embodiment. For SPECT, a temporally reduced subset of the acquired dataset is defined that includes a fraction of the acquired dataset having a desired temporal distribution. For PET, likewise, a temporally reduced subset of data is defined for image reconstruction. - An image, referred to herein as a prior image, is reconstructed at
step 108 using the scan dataset obtained atstep 104, and a final or refined image is iteratively reconstructed atstep 110 using data from the defined temporal subset of data and using the prior image. The iterative reconstruction includes generating the objective function using an initial image estimate and the prior image, as described above, minimizing the objective function to generate the target image, and iterating if subsequent target images generated are not within a threshold difference, as described above.Technique 100 ends atstep 112.Technique 100 may be applied to imaging modalities that include CT, SPECT, and PET. However, it is to be understood that the invention is not to be so limited, and thattechnique 100 may be applied to any imaging modality in which data is reconstructed from a temporal window in which data outside of the temporal reconstruction window may be available and employed to improve image reconstruction and reducing blurring and other artifacts therein. -
FIG. 2 is a flowchart showing image reconstruction occurs according to the technique described above and using an iteration technique. After obtaining imaging data as described above with respect toFIG. 1 ,iterative reconstruction 200 begins atstep 202, and an objective function is generated or formed beginning atstep 204 and using an initial image estimate that is refined based on the prior image. In one embodiment, the objective function formed atstep 204 is based on Eqn. 1 above. The objective function is sparsified atstep 206. In one embodiment, the sparsification atstep 206 is via the subtraction of the prior image from the target image. As stated above, such subtraction results in a sparsified image. However, sparsification may also be implemented by applying the discrete gradient transform as described above with respect to Eqn. 3. Thus, sparsification may include subtracting the prior image from the target image, application of the discrete gradient transform, or a combination thereof. Further, it is to be understood that the invention is not limited to the sparsification techniques described, but other sparsification techniques may be applied as commonly understood in the art. - The l1-norm is calculated at
step 208 and, as summarized above, the objective function is minimized atstep 210. A final or refined image from the minimization is compared with an image generated previously from an earlier iteration atstep 212. If the comparison of successive images is not within the giventhreshold 214 as described above, then the image estimate is revised atstep 216 based on the output from the last minimization, and the objective function is again generated atstep 204. However, if successive images are within the giventhreshold 218, the process ends atstep 220. Alternatively, the iterative process can stop after a pre-defined number of iterations. - Referring now to
FIG. 3 , a flowchart is illustrated showing an image reconstruction technique according another embodiment of the invention. The iterative steps ofFIG. 3 are described with respect to the objective function in Eqn. 2.Iterative reconstruction technique 300 begins atstep 302, and a weighting factor (α in Eqn. 2) is set atstep 304. In one embodiment, α is 0.5. The objective function is generated beginning atstep 306, and as seen in Eqn. 2 above, the objective function includes a first term and a second term, the combination of which is minimized as described above. The first term of the objective function corresponds to steps identified in afirst box 308, and the second term of the objective function corresponds to steps identified in asecond box 310.First box 308 includes, as described above with respect to the first term of Eqn. 2, subtracting a prior image from an image estimate atstep 312, sparsifying atstep 314 and as described above with respect toFIG. 2 , calculating its l1-norm atstep 316, and applying the weighting function a thereto atstep 318 as seen in Eqn. 2.Second box 310 includes sparsifying the image estimate atstep 320 and as described above with respect toFIG. 2 , calculating its l1-norm atstep 322, and applying the weighting function a thereto atstep 324 as seen in Eqn. 2. As summarized above, the objective function, including both terms of Eqn. 2 and as generated according toboxes step 326, and the resulting image from the minimization is compared to an image generated previously and from an earlier iteration atstep 328. If the comparison of successive images is not within the giventhreshold 330 as described above, then the image estimate is revised atstep 332 based on the output from the last minimization, and the objective function is again generated atstep 306. However, if successive images are within the giventhreshold 334, the process ends atstep 336. - The operating environment of one embodiment of the invention is described with respect to
FIGS. 4 and 5 , and data acquisition and image reconstruction for a CT application is described below with respect toFIGS. 6 and 7 . The CT system is described as a sixty-four-slice CT system. However, it will be appreciated by those skilled in the art that the invention is equally applicable for use with other multi-slice configurations. Moreover, the invention will be described with respect to the detection and conversion of x-rays. One skilled in the art will further appreciate that the invention is equally applicable for the detection and conversion of other high frequency electromagnetic energy. This embodiment of the invention will be described with respect to a “third generation” CT scanner, but is equally applicable with other CT systems. -
FIGS. 4 and 5 illustrate, respectively, a pictorial view of aCT system 400 and a schematic block diagram thereof. Referring toFIG. 4 ,CT imaging system 400 is shown as including agantry 402 representative of a “third generation” CT scanner.Gantry 402 has anx-ray source 404 that projects a beam of x-rays toward adetector assembly 406 on the opposite side of thegantry 402. Referring now toFIG. 5 ,detector assembly 406 is formed by a plurality ofdetectors 408 and a data acquisition systems (DAS) 410. The plurality ofdetectors 408 sense projectedx-rays 412 that pass through amedical patient 414 having, in one embodiment, amotion monitor 416, such as an electrocardiographic device (ECG), is attached thereto.DAS 410 converts data fromdetectors 408 to digital signals for subsequent processing. Eachdetector 408 produces an analog electrical signal that represents the intensity of an impinging x-ray beam and hence the attenuated beam as it passes throughmedical patient 414. During a scan to acquire x-ray projection data,gantry 402 and the components mounted thereon rotate about a center ofrotation 418. - Rotation of
gantry 402 and operation ofx-ray source 404 are governed by acontrol mechanism 420 ofCT imaging system 400. In one embodiment,control mechanism 420 includes amotion monitoring system 422 configured to acquire data frommotion monitor 416 and pass patient motion information to acomputer 424. Examples of the patient motion information include respiratory and cardiac phase information.Control mechanism 420 includes anx-ray controller 426 that provides power and timing signals to x-raysource 404 and agantry motor controller 428 that controls a rotational speed and position ofgantry 402. Animage reconstructor 430 receives sampled and digitized x-ray data from data acquisition systems (DAS) 410 and performs high speed reconstruction. The reconstructed image is applied as an input tocomputer 424, which stores the image in amass storage device 432. -
Computer 424 also receives commands and scanning parameters from an operator via an operator console 434 that includes an operator interface, such as a keyboard, mouse, voice activated controller, or any other suitable input apparatus. An associateddisplay 436 allows the operator to observe the reconstructed image and other data fromcomputer 424. The operator supplied commands and parameters are used bycomputer 424 to provide control signals and information to data acquisition systems (DAS) 410,x-ray controller 426 andgantry motor controller 428. In addition,computer 424 operates atable motor controller 438 which controls a motorized table 440 to positionmedical patient 414 andgantry 402. Particularly, motorized table 440 movesmedical patient 414 through agantry opening 442 ofFIG. 4 in whole or in part. In one embodiment,CT imaging system 400 includes asecond x-ray source 444 and a correspondingsecond detector assembly 446 positioned to receive x-rays passing throughmedical patient 414 in order to obtain additional imaging data. Thesecond source 444/detector 446 combination may be controlled and used to obtain imaging data similarly to that illustrated with respect tox-ray source 404 and detector assembly orcollimator 406 and may be used, for instance, to improve the overall temporal resolution ofCT imaging system 400 while incorporating embodiments of the invention. -
FIGS. 6 and 7 illustrate acquisition and reconstruction of imaging data in a CT system, such asCT imaging system 400 ofFIGS. 4 and 5 , according to an embodiment of the invention. - Referring now to
FIG. 6 , a pictorial representation forimage acquisition 500 for a CT system, such asCT system 400 inFIGS. 4 and 5 , is illustrated.Representation 500 includes anobject 502, which may be a heart within a patient, within aninner bore 504 of a CT gantry. Imaging data may be obtained ofobject 502 over a short-scanangular range 506 or over a portion thereof. In the illustrated embodiment, short-scanangular range 506 is 240°. Atemporal subset 508 of the acquired short-scan data may be defined and reconstructed, according to the invention. - Referring now to
FIG. 7 , atechnique 510 begins atstep 512 and includes acquiring a short-scan CT dataset atstep 514. A temporal subset of data, ranging between 90° and 130°, is defined atstep 516, and a prior image is reconstructed of the object using the short-scan data obtained over 240° of gantry rotation atstep 518. According to embodiments of the invention, the prior image is reconstructed with conventional means and as understood within the art. In one example, the prior image is reconstructed using a known filtered backprojection (FBP) technique. A final or refined image is iteratively reconstructed atstep 520 as described above with respect toFIGS. 2 and 3 , and the process ends atstep 522 when subsequent images generated are below a given threshold of difference, as understood in the art. In one embodiment the process may be stopped when a squared difference of two successive images reaches a predetermined threshold. Alternatively, the process may stop after a pre-defined number of iterations. -
FIG. 8 illustrates anexemplary SPECT system 600 for acquiring and processing image data in accordance with embodiments of the invention.SPECT system 600 includes acollimator assembly 602 and adetector assembly 604.SPECT system 600 also includes acontrol module 606, an image reconstruction andprocessing module 608, anoperator workstation 610, and animage display workstation 612. - As illustrated, a subject support 614 (e.g., a table) may be moved into position in a field-of-view (FOV) 616 of
SPECT system 600. In the illustrated embodiment,subject support 614 is configured to support a subject 618 (e.g., a human patient, a small animal, a plant, a porous object, etc.) in position for scanning. Alternatively,subject support 614 may be stationary, whileSPECT system 600 may be moved into position aroundsubject 618 for scanning. Subject 618 may be supported in any suitable position for scanning. In one example, subject 618 may be supported inFOV 616 in a generally vertical position, a generally horizontal position, or any other suitable position (e.g., inclined) for the desired scan. In another example, subject 618 may have amotion monitoring system 620, such as an ECG, attached thereto and connected to amotion monitor 622 withincontrol module 606. Thus,motion monitoring system 620 may be controlled and used to obtain patient motion information such as respiratory and cardiac phase information, as examples. - In SPECT imaging, subject 618 is typically injected with a solution that contains a radioactive tracer. The solution is distributed and absorbed throughout
subject 618 in different degrees, depending on the tracer employed and, in the case of living subjects, the functioning of the organs and tissues. The radioactive tracer emits electromagnetic rays 624 (e.g., photons or gamma quanta) known as “gamma rays” during a nuclear decay event. -
Collimator assembly 602 receivesgamma rays 624 emanating fromFOV 616.Collimator assembly 602 is generally configured to limit and define a direction and angular divergence ofgamma rays 624. In general,collimator assembly 602 is disposed betweendetector assembly 604 andFOV 616.Gamma rays 624 that pass throughcollimator assembly 602impact detector assembly 604. Due to collimation ofgamma rays 624 bycollimator assembly 602, detection ofgamma rays 624 may be used to determine a line of response along which each ray ofgamma rays 624 travels before impactingdetector assembly 604, allowing localization of an origin for each gamma ray to that line. In general,detector assembly 604 may include a plurality of detector elements configured to detectgamma rays 624 emanating from subject 618 inFOV 616 and passing through one or more apertures defined bycollimator assembly 602. In exemplary embodiments, each of the plurality of detector elements indetector assembly 604 produces an electrical signal in response to the impact of thegamma rays 624. - The detector elements may be arranged in
detector assembly 604 in any suitable manner.Detector assembly 604 may extend at least partially aroundFOV 616. In certain embodiments and as illustrated,detector assembly 604 may include modular detector elements arranged aroundFOV 616. Alternatively,detector assembly 406 may be arranged in a ring that may extend up to 360° aroundFOV 616. In embodiments,detector assembly 604 may extend from about 180° to about 360° aroundFOV 616. - To acquire multiple lines of response emanating from subject 618 in
FOV 616 during a scan,collimator assembly 602 may be configured to rotate about subject 618 positioned withinFOV 616. In one example,collimator assembly 602 may be configured to rotate with respect todetector assembly 604.Detector assembly 604 may be stationary whilecollimator assembly 602 may be configured to rotate aboutFOV 616. Alternatively,detector assembly 604 may rotate whilecollimator assembly 602 is stationary. In another example,collimator assembly 602 anddetector assembly 604 may both be configured to rotate, either together or independently of one another. Alternatively, if sufficient pinhole apertures and/or slit apertures are provided throughcollimator assembly 602 or if the slit apertures are orthogonal to the longitudinal axis ofcollimator assembly 602, then no rotation may be required. - In the illustrated embodiment,
control module 606 includes a motor controller 626 and adata acquisition module 628. In general, gantry motor controller 626 may control a rotational speed and position ofcollimator assembly 602,detector assembly 604, and/or a position ofsubject support 614.Data acquisition module 628 may be configured to obtain signals generated in response to impact ofgamma rays 624 withdetector assembly 604. For example,data acquisition module 628 may receive sampled electrical signals fromdetector assembly 604 and convert the data to digital signals for subsequent processing by image reconstruction andprocessing module 608. Any suitable technique for data acquisition may be used withSPECT system 600. In examples and as understood in the art, the data needed for image reconstruction may be acquired in a list or a frame mode. Data may be acquired, parsed, and reconstructed according to embodiments of the invention. - Referring now to
FIG. 9 , a flowchart is illustrated showing atechnique 700 applying steps oftechnique 100 toFIG. 1 to a SPECT system to obtain and reconstruct imaging data according to an embodiment of the invention.Technique 700 begins atstep 702 and includes acquiring, atstep 704, a minimum SPECT dataset of an object such as a heart withinpatient 618 as illustrated above inFIG. 8 . According to embodiments of the invention, gantry speed is relatively slow compared toCT system 400 described above and is in terms of minutes (as opposed to sub-second gantry rotation in typical CT imaging). To improve resolution according to an embodiment of the invention, data is acquired in one embodiment for a 10 minute period and over 180° of rotation, since parallel hole collimation can be used and as understood in the art. Using the concept of iterative reconstruction outlined above as described with respect toFIGS. 2 and 3 , only a fraction of the acquired dataset is then used for the final image production. The fraction of the acquired dataset is defined temporally (that is, having using a subset of acquired data having a desired temporal distribution) atstep 706, and a prior image is reconstructed atstep 708 using the data acquired over the 180° of rotation. The imaging data is iteratively reconstructed atstep 710, and the process ends atstep 712. Alternatively, the data acquisition can be divided into two steps. In the first step, projections over 180° (for parallel collimation) or 180° (for fan-beam or cone-beam collimator) are quickly collected. Images are reconstructed and serve as the prior images. Note that poor temporal resolution due to slow data acquisition results. Next, a pinhole collimator is used to acquire the projections simultaneously over a limited angular range while the gantry is stationary. Since the projection data are acquired at the same time (without gantry rotation), the data acquisition can be effectively gated by the physiological signals such as ECG. The projections acquired with the pinhole collimator are used for the iterative reconstruction to refine the prior image. -
FIG. 10 is a block diagram of an exemplary embodiment of aPET system 800 in which various embodiments of the invention may be implemented.PET system 800 includes a plurality of detector ring assemblies. One such detector ring assembly,detector ring assembly 802, is illustrated inFIG. 11 .PET system 800 further includes acontroller 804 to control normalization and image reconstruction processes.Controller 804 includes aprocessor 806 and anoperator workstation 808.Processor 806 includes adata acquisition processor 810 and animage reconstruction processor 812 that are interconnected and connected withdetector ring assembly 802 via acommunication link 814.PET system 800 acquires scan data and transmits the data todata acquisition processor 810. The scanning operation is controlled fromoperator workstation 808. The data acquired bydata acquisition processor 810 is reconstructed usingimage reconstruction processor 812. -
Detector ring assembly 802 includes acentral opening 816 in which a patient or object 818 may be positioned using, for example, a motorized table (not shown) that is aligned with acentral axis 820 ofdetector ring assembly 802. The motorized table moves object 818 intocentral opening 816 ofdetector ring assembly 802 in response to one or more commands received fromoperator workstation 808. APET scanner controller 822, also referred to as the gantry controller, is provided (e.g., mounted) withinPET system 800.PET scanner controller 822 responds to commands received fromoperator workstation 808 throughcommunication link 814. -
Detector ring assembly 802 includes a plurality of detector units 824 (e.g., in one known PET system, there are 420 crystals per ring, and 24 rings in the scanner). While not shown, it is contemplated that eachdetector unit 824 includes a set of scintillator crystals arranged in a matrix disposed in front of a plurality of photomultiplier tubes (e.g., four tubes). When a photon collides with a scintillator crystal on adetector unit 824, it produces a scintilla on the scintillator crystal. Each photomultiplier tube produces an analog signal on acommunication line 826 when a scintillation event occurs. A set ofacquisition circuits 828 is provided to receive these analog signals.Acquisition circuits 828 produce digital signals indicating a location in 3-dimensional (3D) space and a total energy of the event.Acquisition circuits 828 also produce an event detection pulse, which indicates the time or moment the scintillation event occurred. These digital signals are transmitted through acommunication link 830 such as a cable, for example, to anevent locator circuit 832 indata acquisition processor 810. In one embodiment,PET system 800 includes amotion monitoring system 834, such as an ECG, attached to object 818 and attached toacquisition circuit 828 that may be used to obtain patient motion information such as respiratory and cardiac phase information, as examples, viadata acquisition processor 810. -
Data acquisition processor 810 includesevent locator circuit 832, anacquisition CPU 836 and acoincidence detector 838.Data acquisition processor 810 periodically samples the signals produced byacquisition circuits 828.Acquisition CPU 836 controls communications on a back-plane bus 840 and oncommunication link 814.Event locator circuit 832 processes information regarding each valid event and provides a set of digital numbers or values indicative of the detected event. For example, this information indicates when the event took place and the position of the scintillation crystal that detected the event. An event data packet (not shown) containing the event information is communicated tocoincidence detector 838 through back-plane bus 840.Coincidence detector 838 receives the event data packets fromevent locator circuit 832 and determines if any two of the detected events are in coincidence. Coincidence is determined by a number of factors. First, time markers in each event data packet should be within a predetermined time period of each other such as, for example, 12.5 nanoseconds. Second, a line of response (LOR) formed by a straight line joining the two detectors that detect the coincidence event should pass through thecentral opening 816 or through a field of view inPET system 800. Events that cannot be paired are discarded. Coincident event pairs are located and recorded as a coincidence data packet that is communicated through acommunication link 842 to asorter 844 inimage reconstruction processor 812. -
Image reconstruction processor 812 includessorter 844, amemory module 846, animage CPU 848, anarray processor 850 and a back-plane bus 852.Sorter 844 counts all events occurring along each projection ray and organizes them into 3D data. This 3D data (or sinograms) is organized, in one exemplary embodiment, as adata array 854.Data array 854 is stored inmemory module 846. Back-plane bus 852 is linked tocommunication link 814 throughimage CPU 848, andimage CPU 848 controls communication through back-plane bus 852.Array processor 850 is also connected to back-plane bus 852.Array processor 850 receivesdata array 854 as an input and reconstructs images in the form ofimage arrays 856. Resultingimage arrays 856 are stored inmemory module 846. - Images stored in
image arrays 856 are communicated byimage CPU 848 tooperator workstation 808.Operator workstation 808 includes aCPU 858, adisplay device 860 and aninput device 862.Acquisition CPU 858 connects tocommunication link 814 and receives inputs (e.g., user commands) frominput device 862.Input device 862 may be, for example, a keyboard, mouse, or a touch-screen panel. Throughinput device 862 and associated control panel switches, an operator can control calibration ofPET system 800 and can control positioning ofobject 818 for a scan. Similarly, an operator can control display of a resulting image ondisplay device 860 and perform image-enhancement functions using programs executed byacquisition CPU 858. - The data array received by
array processor 850 may be corrected for errors before being reconstructed. The level of correction may be based on, for example, a desired or required resolution level for a reconstructed image. One correction includes removing scatter coincidences from the image data. -
FIG. 11 illustrates a single scatter coincidence with respect todetector ring assembly 802 ofFIG. 10 . An annihilation event occurs at anannihilation point 864 insideobject 818. The annihilation event produces aphoton 866 that impacts adetector element 868 at afirst detection point 870, and ascattered photon 872 that impacts adetector element 874 at asecond detection point 876.Scattered photon 872 is scattered from ascattering point 878 insideobject 818.Detector element 868 records a time at whichphoton 866 is detected and a time at whichscattered photon 872 is detected.Detector element 868 anddetector element 874 form a detector pair. As known in the art,detector element pair 868/874 map to a unique sinogram bin with indices, r and θ, and indices r and θ denote a radial distance from the center of the detector ring and an angle of the line joining 868 and 876 from a horizontal axis, respectively. A difference between detection times forfirst detection point 870 andsecond detection point 876 maps to a unique time bin index for the time-of-flight scatter sinogram. For each of the plurality of detector pairs, the total number of annihilation events and the time at which each event is recorded is sent to processor 806 (shown inFIG. 11 ). Based on the received information, the detected events are binned into sinograms with indices r and θ, used to generate a time-of-flight scatter sinogram S(r, θ, t). - Referring now to
FIG. 12 , imaging data is obtained and reconstructed using the PET system illustrated with respect toFIGS. 10 and 11 and according totechnique 100 described with respect toFIG. 1 above.Technique 900 begins atstep 902 includes acquiring at step 904 a PET dataset of an object, such as a heart within patient or object 818 as illustrated above inFIG. 10 . According to the invention, a conventional PET dataset may be obtained (e.g., over a 5 minute period) and used to generate the prior image, and reconstructed according to the invention. Data collected over a fractional period of time (i.e., a defined temporal window) may then be used to refine the prior image to removemotion monitoring system 834 ofFIG. 10 , a quiescent time period may be selected within the original acquisition window (5 minutes, in this example) to iteratively produce a final or refined image. Thus, the final image exhibits the noise property of the longer scan time (e.g., 5 minutes) but exhibits the motion property of an improved temporal window. As such, and as described, a conventional or normal PET dataset is obtained of the object atstep 904. A fraction of the acquired dataset is defined temporally atstep 906, a prior image is reconstructed using the dataset obtained atstep 908, and an image is iteratively reconstructed as describe above atstep 910 with respect toFIGS. 2 and 3 . The process ends atstep 912. - Referring now to
FIG. 13 , there is shown a package/baggage inspection system 1000 that can use the image acquisition and reconstructions techniques according to embodiments of the invention and which includes arotatable gantry 1002 having anopening 1004 therein through which packages or pieces of baggage may pass. Therotatable gantry 1002 houses one or morex-ray energy sources 1006 as well as a detector assembly 1008 having scintillator arrays comprised of scintillator cells. Aconveyor system 1010 is also provided and includes aconveyor belt 1012 supported bystructure 1014 to automatically and continuously pass packages orbaggage pieces 1016 throughopening 1004 to be scanned.Objects 1016 are passed throughopening 1004 byconveyor belt 1012, imaging data is then acquired, and theconveyor belt 1012 removes thepackages 1016 from opening 1004 in a controlled and continuous manner. As a result, postal inspectors, baggage handlers, and other security personnel may non-invasively inspect the contents ofpackages 1016 for explosives, knives, guns, contraband, etc. - An implementation of embodiments of the invention in an example comprises a plurality of components such as one or more of electronic components, hardware components, and/or computer software components. A number of such components can be combined or divided in an implementation of the embodiments of the invention. An exemplary component of an implementation of the embodiments of the invention employs and/or comprises a set and/or series of computer instructions written in or implemented with any of a number of programming languages, as will be appreciated by those skilled in the art.
- An implementation of the embodiments of the invention in an example employs one or more computer readable storage media. An example of a computer-readable signal-bearing medium for an implementation of the embodiments of the invention comprises the recordable data storage medium of the image reconstructor 34, and/or the mass storage device 38 of the computer 36. A computer-readable storage medium for an implementation of the embodiments of the invention in an example comprises one or more of a magnetic, electrical, optical, biological, and/or atomic data storage medium. For example, an implementation of the computer-readable signal-bearing medium comprises floppy disks, magnetic tapes, CD-ROMs, DVD-ROMs, hard disk drives, and/or electronic memory.
- A technical contribution for the disclosed method and apparatus is that it provides for a computer-implemented apparatus and method of tomographic imaging and, more particularly, an apparatus and method of acquiring tomographic imaging data and increasing temporal resolution of a tomographic image.
- According to an embodiment of the invention, a tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer. The computer is programmed to acquire a scan dataset of the object, define a temporal subset of the acquired scan dataset for image reconstruction, reconstruct a prior image using the acquired scan imaging dataset, and reconstruct a refined image using the defined subset of scan data and the prior image.
- According to another embodiment of the invention, a method of tomographic imaging includes positioning a detector to receive radiation from a heart of a patient, acquiring projection datasets of the heart using the detector, reconstructing a prior image of the heart using the acquired projection datasets, temporally defining a reduced number of projection datasets from the acquired projection datasets, and reconstructing a final image of the heart using the defined number of projection datasets and using the prior image.
- According to yet another embodiment of the invention, a computer readable storage medium having stored thereon a computer program comprising instructions which when executed by a computer cause the computer to acquire a set of projections from a cardiac region of a patient, reconstruct a prior image of the cardiac region using the acquired set of projections, and iteratively reconstruct a refined image of the cardiac region using a temporally defined subset of the acquired set of projections and the prior image.
- While the invention has been described in detail in connection with only a limited number of embodiments, it should be readily understood that the invention is not limited to such disclosed embodiments. Rather, the invention can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described, but which are commensurate with the spirit and scope of the invention. Additionally, while various embodiments of the invention have been described, it is to be understood that aspects of the invention may include only some of the described embodiments. Accordingly, the invention is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.
Claims (26)
1. A tomographic system comprising:
a gantry having an opening for receiving an object to be scanned;
a radiation source;
a detector positioned to receive radiation from the source that passes through the object; and
a computer programmed to:
acquire a short scan dataset of the object;
define a temporal subset of the acquired short scan dataset for image reconstruction;
reconstruct a short scan image using the acquired short scan dataset; and
iteratively reconstruct a refined image using a sparsified set of data that is based on at least the defined temporal subset of the short scan dataset.
2. The tomographic system of claim 1 wherein the defined temporal subset of the acquired short scan dataset comprises approximately half of an angular range of the short scan dataset.
3. The tomographic system of claim 1 comprising a motion monitor configured to monitor motion of the object, wherein the computer is programmed to define the temporal subset of the acquired scan dataset based on data obtained from the motion monitor.
4. The tomographic system of claim 1 wherein the computer is programmed to acquire the short scan dataset during a single cardiac cycle.
5. The tomographic system of claim 1 wherein the tomographic system comprises more than one radiation source.
6. The tomographic system of claim 1 wherein the tomographic system is a computed tomography (CT) system.
7. The tomographic system of claim 1 wherein the object is a heart of a patient.
8. The tomographic system of claim 1 wherein the computer is programmed to acquire the short scan dataset over 180° of a CT gantry rotation plus a fan angle of the detector.
9. The tomographic system of claim 8 wherein the fan angle of the detector is approximately 60°.
10. The tomographic system of claim 1 wherein the defined temporal subset of the acquired short scan dataset is a subset of the short scan dataset that is obtained over a CT gantry circumferential range between 90° and 130°.
11. A method of tomographic imaging comprising:
positioning a detector to receive radiation passing through a heart of a patient;
acquiring projection datasets of the heart using the detector;
reconstructing a first image of the heart using the acquired projection datasets;
defining a temporally reduced number of projection datasets from the acquired projection datasets; and
iteratively reconstructing a final image of the heart using a sparsified set of data that is based on at least the temporally reduced number of projection datasets.
12. The method of claim 11 wherein the step of iteratively reconstructing the final image of the heart comprises using the sparsified set of data that is based on the first image.
13. The method of claim 11 wherein defining the temporally reduced number of projection datasets comprises defining a reduced number of sequentially acquired projection datasets of the heart.
14. The method of claim 11 wherein acquiring projection datasets of the heart comprises acquiring the projection datasets during a single cardiac cycle.
15. The method of claim 11 wherein acquiring the projection datasets comprises acquiring the projection datasets over a CT gantry angular range of approximately 180° plus a fan angle of the detector.
16. The method of claim 15 wherein the fan angle is approximately 60°.
17. The method of claim 11 wherein defining the temporally reduced number of projection datasets comprises temporally defining datasets acquired over a CT gantry angular range between 90° and 130°.
18. The method of claim 11 wherein reconstructing the prior image of the heart comprises reconstructing the prior image using a filtered backprojection algorithm.
19. A non-transitory computer readable storage medium having stored thereon a computer program comprising instructions which when executed by a computer cause the computer to:
acquire a set of projections from a cardiac region of a patient;
reconstruct a prior image of the cardiac region using the acquired set of projections; and
iteratively reconstruct a refined image of the cardiac region using a temporally defined and sparsified subset of the acquired set of projections.
20. The computer readable storage medium of claim 19 wherein the computer is programmed to iteratively reconstruct the refined image of the cardiac region using the prior image.
21. The computer readable storage medium of claim 19 wherein the computer is caused to iteratively reconstruct the refined image of the cardiac region using sequentially obtained projections.
22. The computer readable storage medium of claim 19 wherein the acquired set of projections is obtained of the cardiac region during a single cardiac cycle.
23. The computer readable storage medium of claim 19 wherein the computer is caused to:
acquire the set of projections over an angular range spanning approximately 180° of circumferential coverage plus a fan angle of a CT detector used to acquire the set of half-scan projections.
24. The computer readable storage medium of claim 23 wherein the fan angle is approximately 60°.
25. The computer readable storage medium of claim 19 wherein the computer is caused to reconstruct the prior image using a filtered backprojection algorithm.
26. The computer readable storage medium of claim 19 wherein the subset of the set of projections comprises half-scan projections obtained over a circumferential range of a CT detector spanning between 90° and 130.
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Publication number | Priority date | Publication date | Assignee | Title |
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WO2015073962A1 (en) * | 2013-11-18 | 2015-05-21 | Regents Of The University Of Minnesota | System and method for temporal sparse promoting imaging of cardiac activation |
US9606245B1 (en) | 2015-03-24 | 2017-03-28 | The Research Foundation For The State University Of New York | Autonomous gamma, X-ray, and particle detector |
US20180259656A1 (en) * | 2017-03-13 | 2018-09-13 | Parto Negar Persia Co. | Single photon emission computed tomography imaging with a spinning parallel-slat collimator |
US20180271470A1 (en) * | 2017-03-23 | 2018-09-27 | Wisconsin Alumni Research Foundation | Methods for quantifying pancreatic beta cell function and mass properties with radiomanganese positron emission tomography |
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US20210049795A1 (en) * | 2019-10-29 | 2021-02-18 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for medical imaging |
US10950014B2 (en) * | 2019-03-01 | 2021-03-16 | Canon Medical Systems Corporation | Method and apparatus for adaptive compressed sensing (CS) to correct motion artifacts in magnetic resonance imaging (MRI) |
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US11628312B2 (en) | 2017-11-06 | 2023-04-18 | The Research Foundation For The State University Of New York | System and method for dual-use computed tomography for imaging and radiation therapy |
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Publication number | Priority date | Publication date | Assignee | Title |
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US8204172B1 (en) * | 2010-03-17 | 2012-06-19 | General Electric Company | System and method of prior image constrained image reconstruction using short scan image data and objective function minimization |
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Citations (39)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5860927A (en) * | 1996-06-04 | 1999-01-19 | Kabushiki Kaisha Toshiba | Diagnostic ultrasound Doppler imaging system and Doppler imaging method for ultrasonic diagnosis |
US6487435B2 (en) * | 1998-04-10 | 2002-11-26 | Wisconsin Alumni Research Foundation | Magnetic resonance angiography using undersampled 3D projection imaging |
US6614874B2 (en) * | 2002-01-28 | 2003-09-02 | Ge Medical Systems Global Technology Company, Llc | Robust and efficient decomposition algorithm for digital x-ray de imaging |
US6661873B2 (en) * | 2002-01-28 | 2003-12-09 | Ge Medical Systems Global Technology Company, Llc | Motion artifacts reduction algorithm for two-exposure dual-energy radiography |
US20040136490A1 (en) * | 2002-07-23 | 2004-07-15 | Edic Peter Michael | Method and apparatus for correcting motion in image reconstruction |
US6841998B1 (en) * | 2001-04-06 | 2005-01-11 | Mark Griswold | Magnetic resonance imaging method and apparatus employing partial parallel acquisition, wherein each coil produces a complete k-space datasheet |
US6909769B2 (en) * | 2001-04-19 | 2005-06-21 | Siemens Aktiengesellschaft | Method and apparatus for three-dimensional imaging of a moving examination subject, particularly for heart imaging |
US6950689B1 (en) * | 1998-08-03 | 2005-09-27 | Boston Scientific Scimed, Inc. | Dynamically alterable three-dimensional graphical model of a body region |
US20060029279A1 (en) * | 2004-08-09 | 2006-02-09 | Donoho David L | Method and apparatus for compressed sensing |
US7068826B2 (en) * | 2002-01-28 | 2006-06-27 | Ge Medical Systems Global Technology Company, Llc | Automatic selection of the log-subtraction decomposition parameters for dual energy chest radiography |
US20060257012A1 (en) * | 2001-07-17 | 2006-11-16 | Accuimage Diagnostics Corp | Methods and software for self-gating a set of images |
US20070010731A1 (en) * | 2005-07-08 | 2007-01-11 | Mistretta Charles A | Highly constrained image reconstruction method |
US20070049817A1 (en) * | 2005-08-30 | 2007-03-01 | Assaf Preiss | Segmentation and registration of multimodal images using physiological data |
US7209535B2 (en) * | 2003-06-20 | 2007-04-24 | Wisconsin Alumni Research Foundation | Fourier space tomographic image reconstruction method |
US20070106149A1 (en) * | 2005-09-22 | 2007-05-10 | Mistretta Charles A | Image reconstruction method for cardiac gated magnetic resonance imaging |
US7218702B2 (en) * | 2004-05-10 | 2007-05-15 | Wisconsin Alumni Research Foundation | X-ray system for use in image guided procedures |
US20070156044A1 (en) * | 2005-09-22 | 2007-07-05 | Mistretta Charles A | Highly constrained reconstruction of motion encoded MR images |
US20070167728A1 (en) * | 2005-09-22 | 2007-07-19 | Mistretta Charles A | Image acquisition and reconstruction method for functional magnetic resonance imaging |
US20070167729A1 (en) * | 2005-09-22 | 2007-07-19 | Mistretta Charles A | Highly constrained magnetic resonance spectroscopy image reconstruction method |
US7289049B1 (en) * | 2006-08-21 | 2007-10-30 | L3 Communications Integrated Systems L.P. | Method and apparatus for compressed sensing |
US7330027B2 (en) * | 2003-07-02 | 2008-02-12 | Universitat Zurich | System and method of magnetic resonance imaging for producing successive magnetic resonance images |
US7358730B2 (en) * | 2005-09-22 | 2008-04-15 | Wisconsin Alumni Research Foundation | Diffusion tensor imaging using highly constrained image reconstruction method |
US20080170654A1 (en) * | 2007-01-15 | 2008-07-17 | John Eric Tkaczyk | Method and apparatus of ct cardiac diagnostic imaging using a priori motion information from 3d ultrasound and ecg gating |
US7415145B2 (en) * | 2003-12-30 | 2008-08-19 | General Electric Company | Methods and apparatus for artifact reduction |
US20080199063A1 (en) * | 2007-02-19 | 2008-08-21 | O'halloran Rafael L | Iterative Highly Constrained Image Reconstruction Method |
US20080219535A1 (en) * | 2007-02-19 | 2008-09-11 | Mistretta Charles A | Localized and Highly Constrained Image Reconstruction Method |
US20090076369A1 (en) * | 2007-09-17 | 2009-03-19 | Mistretta Charles A | Method For Reducing Motion Artifacts In Highly Constrained Medical Images |
US20090129651A1 (en) * | 2007-11-13 | 2009-05-21 | Zagzebski James A | Method for producing highly constrained ultrasound images |
US7545901B2 (en) * | 2005-07-08 | 2009-06-09 | Wisconsin Alumni Research Foundation | Backprojection reconstruction method for CT imaging |
US20090161932A1 (en) * | 2007-12-20 | 2009-06-25 | Guang-Hong Chen | Method For Prior Image Constrained Image Reconstruction |
US20090161933A1 (en) * | 2007-12-20 | 2009-06-25 | Guang-Hong Chen | Method for dynamic prior image constrained image reconstruction |
US7558414B2 (en) * | 2006-09-11 | 2009-07-07 | Case Western Reserve University | Iterative image reconstruction |
US7613275B2 (en) * | 2005-12-19 | 2009-11-03 | General Electric Company | Method and apparatus for reducing cone beam artifacts using spatially varying weighting functions |
US20090274355A1 (en) * | 2008-01-14 | 2009-11-05 | Guang-Hong Chen | Method for Prior Image Constrained Progressive Image Reconstruction |
US20100128958A1 (en) * | 2008-11-26 | 2010-05-27 | Guang-Hong Chen | Method for prior image constrained image reconstruction in cardiac cone beam computed tomography |
US7792347B2 (en) * | 2004-06-18 | 2010-09-07 | Koninklijke Philips Electronics N.V. | Artifact reduction |
US20100310144A1 (en) * | 2009-06-09 | 2010-12-09 | Guang-Hong Chen | Method for Dynamic Prior Image Constrained Image Reconstruction |
US8131043B2 (en) * | 2005-09-16 | 2012-03-06 | The Ohio State University | Method and apparatus for detecting interventricular dyssynchrony |
US8204172B1 (en) * | 2010-03-17 | 2012-06-19 | General Electric Company | System and method of prior image constrained image reconstruction using short scan image data and objective function minimization |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6370217B1 (en) * | 1999-05-07 | 2002-04-09 | General Electric Company | Volumetric computed tomography system for cardiac imaging |
DE10251448A1 (en) * | 2002-11-05 | 2004-05-19 | Siemens Ag | CT method for imaging of a periodically moving examination area, especially the heart, whereby image segments are combined to form image planes, which are subsequently joined together to form a complete image |
DE102004042491B4 (en) * | 2004-08-31 | 2009-07-09 | Siemens Ag | A method for generating tomographic slice images of an examination subject with at least two angularly offset beams and computed tomography device for performing this method |
US7424088B2 (en) * | 2004-09-29 | 2008-09-09 | Kabushiki Kaisha Toshiba | Image reconstruction method using Hilbert transform |
WO2006073584A2 (en) * | 2004-11-24 | 2006-07-13 | Wisconsin Alumni Research Foundation | Cone-beam filtered backprojection image reconstruction method for short trajectories |
JP2007175258A (en) * | 2005-12-28 | 2007-07-12 | Ge Medical Systems Global Technology Co Llc | Tomographic x-ray equipment and x-ray tomographic method |
ATE494061T1 (en) | 2007-07-10 | 2011-01-15 | Hoffmann La Roche | MICROFLUIDIC DEVICE, MIXING METHOD AND USE OF THE DEVICE |
-
2010
- 2010-11-19 US US12/950,510 patent/US8204172B1/en active Active
-
2011
- 2011-02-17 US US13/029,733 patent/US20110228999A1/en not_active Abandoned
-
2012
- 2012-06-11 US US13/493,051 patent/US8315353B1/en active Active
Patent Citations (51)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5860927A (en) * | 1996-06-04 | 1999-01-19 | Kabushiki Kaisha Toshiba | Diagnostic ultrasound Doppler imaging system and Doppler imaging method for ultrasonic diagnosis |
US6487435B2 (en) * | 1998-04-10 | 2002-11-26 | Wisconsin Alumni Research Foundation | Magnetic resonance angiography using undersampled 3D projection imaging |
US6950689B1 (en) * | 1998-08-03 | 2005-09-27 | Boston Scientific Scimed, Inc. | Dynamically alterable three-dimensional graphical model of a body region |
US6841998B1 (en) * | 2001-04-06 | 2005-01-11 | Mark Griswold | Magnetic resonance imaging method and apparatus employing partial parallel acquisition, wherein each coil produces a complete k-space datasheet |
US6909769B2 (en) * | 2001-04-19 | 2005-06-21 | Siemens Aktiengesellschaft | Method and apparatus for three-dimensional imaging of a moving examination subject, particularly for heart imaging |
US20060257012A1 (en) * | 2001-07-17 | 2006-11-16 | Accuimage Diagnostics Corp | Methods and software for self-gating a set of images |
US6661873B2 (en) * | 2002-01-28 | 2003-12-09 | Ge Medical Systems Global Technology Company, Llc | Motion artifacts reduction algorithm for two-exposure dual-energy radiography |
US6792072B2 (en) * | 2002-01-28 | 2004-09-14 | Ge Medical Systems Global Technology Company, Llc. | System and method for mitigating image noise with multi-energy image decomposition |
US7068826B2 (en) * | 2002-01-28 | 2006-06-27 | Ge Medical Systems Global Technology Company, Llc | Automatic selection of the log-subtraction decomposition parameters for dual energy chest radiography |
US6614874B2 (en) * | 2002-01-28 | 2003-09-02 | Ge Medical Systems Global Technology Company, Llc | Robust and efficient decomposition algorithm for digital x-ray de imaging |
US20040136490A1 (en) * | 2002-07-23 | 2004-07-15 | Edic Peter Michael | Method and apparatus for correcting motion in image reconstruction |
US6934357B2 (en) * | 2002-07-23 | 2005-08-23 | Ge Medical Systems Global Technology Company Llc | Methods and apparatus for motion compensation in image reconstruction |
US7221728B2 (en) * | 2002-07-23 | 2007-05-22 | General Electric Company | Method and apparatus for correcting motion in image reconstruction |
US7209535B2 (en) * | 2003-06-20 | 2007-04-24 | Wisconsin Alumni Research Foundation | Fourier space tomographic image reconstruction method |
US7330027B2 (en) * | 2003-07-02 | 2008-02-12 | Universitat Zurich | System and method of magnetic resonance imaging for producing successive magnetic resonance images |
US7415145B2 (en) * | 2003-12-30 | 2008-08-19 | General Electric Company | Methods and apparatus for artifact reduction |
US7218702B2 (en) * | 2004-05-10 | 2007-05-15 | Wisconsin Alumni Research Foundation | X-ray system for use in image guided procedures |
US7792347B2 (en) * | 2004-06-18 | 2010-09-07 | Koninklijke Philips Electronics N.V. | Artifact reduction |
US20060029279A1 (en) * | 2004-08-09 | 2006-02-09 | Donoho David L | Method and apparatus for compressed sensing |
US7545901B2 (en) * | 2005-07-08 | 2009-06-09 | Wisconsin Alumni Research Foundation | Backprojection reconstruction method for CT imaging |
US7519412B2 (en) * | 2005-07-08 | 2009-04-14 | Wisconsin Alumni Research Foundation | Highly constrained image reconstruction method |
US20070038073A1 (en) * | 2005-07-08 | 2007-02-15 | Mistretta Charles A | Backprojection reconstruction method for undersampled MR imaging |
US20070010731A1 (en) * | 2005-07-08 | 2007-01-11 | Mistretta Charles A | Highly constrained image reconstruction method |
US20070049817A1 (en) * | 2005-08-30 | 2007-03-01 | Assaf Preiss | Segmentation and registration of multimodal images using physiological data |
US8131043B2 (en) * | 2005-09-16 | 2012-03-06 | The Ohio State University | Method and apparatus for detecting interventricular dyssynchrony |
US20070167729A1 (en) * | 2005-09-22 | 2007-07-19 | Mistretta Charles A | Highly constrained magnetic resonance spectroscopy image reconstruction method |
US20070167728A1 (en) * | 2005-09-22 | 2007-07-19 | Mistretta Charles A | Image acquisition and reconstruction method for functional magnetic resonance imaging |
US7647088B2 (en) * | 2005-09-22 | 2010-01-12 | Wisconsin Alumni Research Foundation | Reconstruction method for images of the beating heart |
US7408347B2 (en) * | 2005-09-22 | 2008-08-05 | Wisconsin Alumni Research Foundation | Highly constrained magnetic resonance spectroscopy image reconstruction method |
US7711166B2 (en) * | 2005-09-22 | 2010-05-04 | Wisconsin Alumni Research Foundation | Highly constrained reconstruction of motion encoded MR images |
US20070156044A1 (en) * | 2005-09-22 | 2007-07-05 | Mistretta Charles A | Highly constrained reconstruction of motion encoded MR images |
US20070106149A1 (en) * | 2005-09-22 | 2007-05-10 | Mistretta Charles A | Image reconstruction method for cardiac gated magnetic resonance imaging |
US7358730B2 (en) * | 2005-09-22 | 2008-04-15 | Wisconsin Alumni Research Foundation | Diffusion tensor imaging using highly constrained image reconstruction method |
US20070167707A1 (en) * | 2005-09-22 | 2007-07-19 | Mistretta Charles A | Reconstruction method for images of the beating heart |
US7613275B2 (en) * | 2005-12-19 | 2009-11-03 | General Electric Company | Method and apparatus for reducing cone beam artifacts using spatially varying weighting functions |
US7289049B1 (en) * | 2006-08-21 | 2007-10-30 | L3 Communications Integrated Systems L.P. | Method and apparatus for compressed sensing |
US7558414B2 (en) * | 2006-09-11 | 2009-07-07 | Case Western Reserve University | Iterative image reconstruction |
US20080170654A1 (en) * | 2007-01-15 | 2008-07-17 | John Eric Tkaczyk | Method and apparatus of ct cardiac diagnostic imaging using a priori motion information from 3d ultrasound and ecg gating |
US20080219535A1 (en) * | 2007-02-19 | 2008-09-11 | Mistretta Charles A | Localized and Highly Constrained Image Reconstruction Method |
US20080199063A1 (en) * | 2007-02-19 | 2008-08-21 | O'halloran Rafael L | Iterative Highly Constrained Image Reconstruction Method |
US20090076369A1 (en) * | 2007-09-17 | 2009-03-19 | Mistretta Charles A | Method For Reducing Motion Artifacts In Highly Constrained Medical Images |
US20090129651A1 (en) * | 2007-11-13 | 2009-05-21 | Zagzebski James A | Method for producing highly constrained ultrasound images |
US20090175523A1 (en) * | 2007-12-20 | 2009-07-09 | Guang-Hong Chen | Method For Image Reconstruction Using Sparsity-Constrained Correction |
US20090161933A1 (en) * | 2007-12-20 | 2009-06-25 | Guang-Hong Chen | Method for dynamic prior image constrained image reconstruction |
US20090161932A1 (en) * | 2007-12-20 | 2009-06-25 | Guang-Hong Chen | Method For Prior Image Constrained Image Reconstruction |
US8194937B2 (en) * | 2007-12-20 | 2012-06-05 | Wisconsin Alumni Research Foundation | Method for dynamic prior image constrained image reconstruction |
US20090274355A1 (en) * | 2008-01-14 | 2009-11-05 | Guang-Hong Chen | Method for Prior Image Constrained Progressive Image Reconstruction |
US20100128958A1 (en) * | 2008-11-26 | 2010-05-27 | Guang-Hong Chen | Method for prior image constrained image reconstruction in cardiac cone beam computed tomography |
US20100310144A1 (en) * | 2009-06-09 | 2010-12-09 | Guang-Hong Chen | Method for Dynamic Prior Image Constrained Image Reconstruction |
US8111893B2 (en) * | 2009-06-09 | 2012-02-07 | Wisconsin Alumni Research Foundation | Method for dynamic prior image constrained image reconstruction |
US8204172B1 (en) * | 2010-03-17 | 2012-06-19 | General Electric Company | System and method of prior image constrained image reconstruction using short scan image data and objective function minimization |
Non-Patent Citations (4)
Title |
---|
Black, Black's Law Dictionary, 1990, West Publishing Co., ISBN 0-314-77165-4, page 161 * |
Hsieh et al., Step-and-shoot data acquisition and reconstruction for cardiac x-ray computed tomography, 19 October 2006, Medical Physics, Volume 33, Number 11, Pages 4236-4248 * |
Nett et al., Low Dose Myocardial CT Perfusion Measurements Using Prior Image Constrained Compressed Sensing (PICCS), June 2008, Medical Physics, Volume 35, Number 6, Abstract MO-D-332-05, Page 2870 * |
Nett et al., Tomosynthesis via Total Variation Minimization Reconstruction and Prior Image Constrained Compressed Sensing (PICCS) on a C-arm System, 18 February 2008, SPIE Conference, Medical Imaging 2008: Physics of Medical Imaging. Volume 6913, Pages 2D-1 to 2D-10 * |
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WO2015073962A1 (en) * | 2013-11-18 | 2015-05-21 | Regents Of The University Of Minnesota | System and method for temporal sparse promoting imaging of cardiac activation |
US10098557B2 (en) | 2013-11-18 | 2018-10-16 | Regents Of The University Of Minnesota | System and method for temporal sparse promoting imaging of cardiac activation |
US11331030B2 (en) | 2013-11-18 | 2022-05-17 | Regents Of The University Of Minnesota | System and method for temporal sparse promoting imaging of cardiac activation |
US9606245B1 (en) | 2015-03-24 | 2017-03-28 | The Research Foundation For The State University Of New York | Autonomous gamma, X-ray, and particle detector |
US9835737B1 (en) | 2015-03-24 | 2017-12-05 | The Research Foundation For The State University Of New York | Autonomous gamma, X-ray, and particle detector |
US20180259656A1 (en) * | 2017-03-13 | 2018-09-13 | Parto Negar Persia Co. | Single photon emission computed tomography imaging with a spinning parallel-slat collimator |
US10795033B2 (en) * | 2017-03-13 | 2020-10-06 | Parto Negar Persia Co. | Single photon emission computed tomography imaging with a spinning parallel-slat collimator |
US20180271470A1 (en) * | 2017-03-23 | 2018-09-27 | Wisconsin Alumni Research Foundation | Methods for quantifying pancreatic beta cell function and mass properties with radiomanganese positron emission tomography |
US11628312B2 (en) | 2017-11-06 | 2023-04-18 | The Research Foundation For The State University Of New York | System and method for dual-use computed tomography for imaging and radiation therapy |
US10950014B2 (en) * | 2019-03-01 | 2021-03-16 | Canon Medical Systems Corporation | Method and apparatus for adaptive compressed sensing (CS) to correct motion artifacts in magnetic resonance imaging (MRI) |
US20210049795A1 (en) * | 2019-10-29 | 2021-02-18 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for medical imaging |
US11776170B2 (en) * | 2019-10-29 | 2023-10-03 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for medical imaging |
CN111009020A (en) * | 2019-12-02 | 2020-04-14 | 上海联影医疗科技有限公司 | Image reconstruction method and device, computer equipment and storage medium |
CN113506355A (en) * | 2021-09-10 | 2021-10-15 | 苏州瑞派宁科技有限公司 | Scattering correction method, device, imaging system and computer readable storage medium |
WO2023186153A1 (en) * | 2022-03-31 | 2023-10-05 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for medical image reconstruction |
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