JP2005509452A - Systems and methods for reducing the effects of rotational motion - Google Patents

Abstract

      Methods and systems are provided for improving image quality by correcting errors introduced by the rotational motion of the object being imaged. The method provides a computer-executable methodology for detecting rotation and selectively re-ordering, deleting and / or re-acquiring projection data.

Description

  The present invention relates to image processing techniques, and in particular to improving nuclear magnetic resonance imaging {MRI} system images. The present invention also includes X-ray, sheet (CT), single photon emission computerized tomography {SPECT), positron emission tomography (PET). }, And having application to other imaging systems such as others will be appreciated.

  Nuclear magnetic resonance imaging systems acquire diagnostic images without relying on ionizing radiation. Instead, MR eye uses a strong static magnetic field, the energy of a radio frequency pulse, and a time varying magnetic field gradient waveform. MR eye is a non-invasive procedure that uses nuclear magnetization and radio waves to create an internal image of a subject. Two or three-dimensional diagnostic image data is acquired for each “slice” of the patient's region. These data slices typically provide structural details having a resolution of, for example, 1 millimeter or better.

  The programmed process for collecting data used to generate slices of the diagnostic image is known as a nuclear magnetic resonance (MR) image pulse sequence. The MR image pulse sequence includes a process of generating a magnetic field gradient waveform applied along up to three axes and the energy of one or more RF pulses. The ramp waveform and RF pulse set are repeated a number of times to collect enough data to reproduce the image slice.

  Data is acquired during each excitation of the MR device. Ideally, there is little or no variation in nuclear magnetization during each excitation. However, patient motion caused by, for example, breathing, heart pulsation, blood pulsation, and / or spontaneous motion changes the nuclear magnetization from one excitation to the next. This change in the nuclear magnetization degrades the quality of the MR data used to create the image.

  The acquisition of the MR eye image takes a certain period of time (period of time). The time is determined, at least in part, by the number of scans performed and the number of data acquisitions in each scan. If the object being imaged moves during the scan, artifacts can be introduced into the image. Very small movements (eg 1 mm, 1 ° rotation) lead to blurring and ghosting artifacts. Some patients are difficult to lie completely stationary, which can lead to the MR eye images of these patients degraded by rotational movement. In addition, certain types of motion (eg, heart beat, breathing) require additional techniques to reduce the impact on imaging. Since these techniques may not produce ideal results, they can also lead to degradation of the MR eye image due to rotational motion.

  The following is a method, system, APIs, data packet for improving MR eye images that are degraded by the rotational movement of an object in MR eye so as to provide a basic understanding of these items. (Data packet) and a simple abstract of the computer readable medium is presented. This abstract is not a broad perspective and is not intended to identify key or critical elements of the method, system, computer readable medium, etc., or to delineate the scope of these items . This abstract provides a conceptual introduction in a simplified form as a prelude to a more detailed explanation presented later.

  This application describes a computer-implemented method for reducing the effect on rotational image quality during image acquisition by the MIR system. The method includes receiving data about a fiducial mark associated with an object to be imaged. The point source location of the reference is calculated from this point source data and is traced by the point source during acquisition of radial k-space data. ) Predicted sine wave in the projection data to be predicted. The method also includes the step of receiving the subject MR eye image data. This image data may have, but is not limited to, one or more projections in radial k-space associated with the object and an observed waveform that may be associated with the predicted sine wave. The method also includes comparing the predicted sine wave with the observed waveform, and selectively processing the image data to reduce the effect of the rotational motion of the object on the image based on the comparison; Have In one example, the selectively processed image data is used to create a visible image of the object.

  This application also describes a system for improving the quality of an image of an object in which the image has been degraded by the rotational movement of the object during image acquisition. The system has a data receiver that receives MR eye image data from the object to be imaged. The image data includes target data (object data) and reference data (fiducial data), but is not limited thereto. The data is stored in one or more data stores where the object and reference data can be stored individually and / or collectively. The system includes a reference analyzer that determines reference sinusoidal reference trajectory data and actual reference trajectory data. After these data are calculated, the reference analyzer decides whether and / or to selectively manipulate the object data to improve the quality of the image. The actual trajectory data is compared with the compliant sinusoidal reference trajectory data. The determination is based on whether the comparison indicates that the image has been degraded by the rotational movement of the object during image acquisition.

  Certain illustrative methods, systems, APIs, data packets, and computer readable media are now described in conjunction with the following description and accompanying drawings. However, these examples show only a few of the various principles in which the method principles, systems, APIs, data packets, and computer-readable media are used, and thus are intended to include equalization. Has been. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings.

  Exemplary methods, systems, APIs, data packets, and computer media are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. used. In the following description, for purposes of explanation, numerous specific details are set forth to describe the method, system, API, data packet, and computer-readable medium. It will be apparent, however, that the method, system, API, data packet, and computer readable medium may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to simplify the description.

  As used in this application, the term “computer component” can be hardware, firmware, software, a combination thereof, or running. Even software (software in execution) refers to a computer-related entity (computer-related entity). For example, the computer component can be a running process on a processor, a processor, an object, an executable, a thread of execution, a program, and a computer. , But not limited to them. By way of illustration, both an application running on a server and the server can be a computer component. One or more computer components may exist in a process and / or thread of execution, and the computer components may be localized on one computer and / or distributed between two or more computers.

  “Software”, as used herein, is one or more computer readable and / or causes a computer or other electronic device to perform functions, actions, and / or operate in a desired manner and / or Including but not limited to executable instructions. The instructions may be embodied in various forms such as routines, algorithms, modules, methods, threads, and / or programs. The software also includes stand-alone programs, function calls (local and / or remote), servlets, applets, instructions stored in memory, operating systems. Or it may be implemented in various feasible and / or loadable forms, including but not limited to browser parts and the like. The computer readable and / or executable instructions may be located within one computer component and / or distributed between two or more communicating, cooperating and / or parallel processing computer components. Thus, it can be loaded and / or executed serially, in parallel, massively parallel, and otherwise.

  To realize an understanding of correcting the rotational motion of an object during radial k-space image acquisition, imagine standing directly in front of a person. You are looking directly at them (eg, a viewing angle of 0 °) and you look at their faces. For some reason, you want to walk around the person and you want to take 360 photos as one photo for each time of the compass. Suppose you work around that person and you get exactly half as a result (for example, 180 °). At this point, imagine that the person rotates 180 degrees so that you face the surface again. Now you move further 1 ° around your circle and take the next image. The image is still good data, but it is a repeat of the image from 1 ° instead of an image taken from 181 ° (eg you take a picture from behind the person). Thus, when you develop your footage, you get two images taken from once for the person's face, not an image taken from 181 ° to the person.

  Imagine that when you reach exactly half of the person taking the picture, instead of just turning 180 degrees to face you, they rotate 90 degrees opposite the direction you walk around. When you complete a circle around that person, your image will be 90 ° out of phase. In addition to getting 90 duplicate photos, there will be 90 ° you did not take a picture of that person. Thus, the systems and methods described herein detect this type of rotational motion, correct data errors, and adapt image acquisition algorithms and / or systems to obtain viewing angles that were not taken back. Realize that. Although this analogy describes photography, it is appreciated that the present invention relates to MIR, PTE (PET), CTE (CT), optical imaging, etc. where projection data is used to reconstruct cross-sectional images. Should.

  Referring initially to FIG. 1, two views of the subject 100 are illustrated. In the first view 110, the object 100 is in an initial orientation, where the various projection angles used in the radial k-space are illustrated for the object 100. For example, view angles starting at 0 ° and progressing by 5 ° to 315 ° are illustrated. Although eight projection angles are illustrated, it should be appreciated that more and / or fewer number of projection angles can be used with radial k-space MR eye. In one example, a projection angle of 360 that starts at 0 ° and increases with increments of 1 degree may be used.

  In view 120, the object 100 undergoes a rotational motion. Thus, the radial k-space data collected from the various projection angles after the rotation does not accurately reflect the desired projection angle. Thus, the rotational movement of the object 100 during the acquisition of the MR eye image can negatively affect the image quality.

Turning now to FIG. 2, an example data structure 200 that stores a set of projection data 210 is illustrated. The data structure 200 can be, for example, an array that is addressed in order to implement a search for projection data by projection angle. For example, the first signal acquired at the projection angle Θ ₁ is stored in the data structure 200 at location L ₁ . Similarly, the second signal acquired at projection angle Θ ₂ may be stored in the data structure 200 at location L ₂ . It is appreciated that the signals can be acquired sequentially (eg, Θ ₁ , Θ ₂ ,... Θ ₈ ), but the data can be acquired in other orders that are neither linear nor sequential. Should be. However, storing the data from the various projection angles in the data structure 200 at a predetermined location associated with a programmed projection angle selectively processes the acquired data {eg, Re-ordering} is realized. Although the array is illustrated in FIG. 2, it is appreciated that other data structures may be used in accordance with various aspects of this application, including but not limited to trees, tables, lists, files, etc. It should be. The rotational movement described in FIG. 1 can lead to projection data being stored in locations that do not correspond to actual locations that negatively affect image quality.

  FIG. 3 illustrates an example of the relationship between the viewing angle and the rotational motion that realizes the correction of the rotational motion during the MR eye. The object 300 is initially imaged from a viewing angle of 0 ° as illustrated at 310. Data from this viewing angle is stored in a data structure that is addressable to a location programmatically associated with the viewing angle. The object 300 is then viewed from a 45 ° angle as illustrated at 320. This data from the next viewing angle is also stored in a data structure that is addressable to a location programmatically associated with the next viewing angle. Thereafter, the object 300 rotates 45 ° counterclockwise. If the object 300 is imaged from a 90 ° angle as illustrated at 330, this re-images the object 300 from a 45 ° angle as illustrated in FIG. imaging). Data from this viewing angle would be stored incorrectly in locations that accompany programmatically 90 ° instead of 45 °. Thus, there are two sets of data for a 45 ° projection angle (one correctly stored and one stored incorrectly) and no projection data for a 90 ° projection angle. In conventional systems that do not allow for the rotational motion of the object 300, data that is acquired at 90 ° assumably is incorrectly stored for the 90 ° projection angle. The systems and methods described herein detect the rotational motion of the object 300 and, if any, determine where unexpected projection angle data should be stored and how it should be processed. To realize.

  Turning now to FIG. 4, two images illustrate an example of an MR eye image before (FIG. 4a) and after correction (FIG. 4b) of rotational motion. Image 400 is an MR eye image before correction, where it has been degraded due to the rotational motion of the object during image acquisition. Image 410 is an MR eye image of the same item after detecting errors for rotational motion and correcting those errors. A bright dot in the center of the top of the image 410 represents an image marker placed on or close to the object but linked. This is described in detail below.

  Images 5a and 5b similarly illustrate the representation of the front and rear MR eye images. For example, the image 500 is an unmodified image of the knee rotated during the acquisition of the M eye image. An image 510 is an image of the knee after the rotational motion is detected and the correction process is performed. Again, notice the bright spot in the center of the top of image 510. The bright spots in FIGS. 4b and 5b are associated with fiducial marks associated with the object to be imaged. In one example, a high signal fiducial mark may be used, where the high signal fiducial mark is a gadolinium-filled tuned fiducial marker that implements receiving reference data from the object being imaged. filled tuned fiducial marker). The fiducial mark may be placed on the object to be imaged, but the fiducial mark is also close to the object to be imaged so that the fiducial mark will also rotate if the object rotates. It should be appreciated that it may be placed on and / or associated with the object to be imaged. Further, more than one fiducial is associated with the object to be imaged, which creates one or more reference waveforms that can be analyzed by the systems and methods described herein. It should be appreciated.

FIGS. 6, 7a and 7b show the relationship between image space and sinogram space. A sinogram is a two-dimensional map that represents the amplitude of a signal along one axis as a function of position along the projection direction, with the other axis representing the projection angle. Note that a point source in the image space is converted to a unique sine wave in the sinogram space. This sine wave can be reproduced from two projections at a minimum. This is a direct result of Shannon's sampling theorem. For example, take two projections of the point source P at angles θ ₁ = 0 ° and θ ₂ = 90 °, s ₁ (r, θ ₁ ), s ₂ (r, θ ₂ ), respectively. An illustration 600 shows the imaging coordinate system and the point source P. Illustration 610 shows the two projections about the center of rotation O. r ₁ and r ₂ represent the projection distance of P from s ₁ and s ₂ respectively from the rotation center O of the projection. If the point source rotates with the imaged object during acquisition of the radial data, the sine wave corresponding to the point source is destroyed. The corrupted waveform can be extracted from the sinogram data using an edge detection algorithm and / or other detection algorithms, methods, apparatus and processes.

FIG. 6 illustrates a fiducial mark measurement that provides for detecting and / or correcting the rotational motion of the object being imaged. Illustrations 600 and 610 show a point P associated with the reference mark. The first projection taken at a viewing angle of Θ ₁ results in projection data S ₁ from which the projection distance r ₁ can be calculated. A second projection taken at a viewing angle of Θ ₂ yields second projection data S ₂ from which a second projection distance r ₂ can be obtained. The first projection data and the second projection data can be referred to as point source data. In one example, the viewing angles Θ ₁ and Θ ₂ are orthogonal to each other, which facilitates subsequent rotational motion detection and correction. Although two projection data are illustrated at 610, it should be appreciated that a greater number of projections can be used to create the point source data. In addition, projection data that forms the point source data may be acquired at times, including but not limited to, before imaging the object, during acquisition of the MIR image, and after acquiring the MIR image. That should be appreciated. In one example, the first projection data S ₁ and the second projection data S ₂ is to be rotational movement did not occur is mathematically verifiable during the acquiring the two data are repeatedly acquired . This makes it easy to detect and correct the rotational movement of the object being imaged.

Once the point source data is acquired, the point source location can be calculated from the point source data. For example, in diagram 600, Θ _res and R _res can be calculated from S ₁ and S ₂ to simplify the next mathematical calculation used to detect and correct rotational motion. In one example, R _res is determined by R _res = square root of (r ₁ ² + r ₂ ² ). Similarly, in one example, Θ _res is determined by Θ _res = tan ⁻¹ (r ₂ / r ₁ ). Once Θ _res and R _res are calculated, a predicted sine wave traced by the reference mark can be calculated. In one example, the predicted sine wave is calculated by sin _res = (R _res ) cos (| Θ _res −Θ |), where Θ may vary throughout the viewing angle range.

Thus, referring to the calculations associated with FIG. 6, two projections are taken at viewing angles Θ ₁ and Θ ₂ , preferably where Θ ₁ and Θ ₂ should be orthogonal. Preferably, the projection is taken at a time during which it can be determined that no rotational movement has occurred. Given the two projection data, the values R _res and Θ _res can be calculated. Once R _res and Θ _res are calculated, a predicted reference waveform created from the signal generated by the reference marker can be calculated.

  Turning now to FIGS. 7a and 7b, an example point source and a sinogram resulting from the acquisition of radial k-space data of an image associated with the point source are presented. In addition to the point source data, data associated with the object associated with the point source is also stored. If the system calculates how far the point source has rotated from the expected location, how far the data associated with both the point source and the object should be shifted to correct the rotational motion. Calculations to determine can be made. Thus, the method of using an object with an associated a fiducial includes associating a point source 710 with the object to be imaged, calculating the point source location, and the radial direction k of the object data. -Predicting a sine wave 730 created during space acquisition; obtaining a unique actual waveform from the radial projection; and an actual unique waveform collected in the MR eye from the predicted sine wave 730. Comparing and correcting and / or reacquiring data based on the comparison of the predicted sine wave 730 with the actual waveform may be included.

  FIG. 8 illustrates an example sinogram showing the result of the rotational motion on a compliant waveform 800. The reference waveform 800 (brightest line) illustrates the step discontinuity of the rotational movement of the fiducial marker associated with the object to be imaged. An edge detection algorithm (or other detection method / device) can be used to detect the stepped discontinuities. By detecting discontinuities in the compliant waveform 800, both the point where the rotational motion occurred, the point where the object returned and rotated (if any), and the amount of the rotational motion can be calculated. Although stepped discontinuities are illustrated in FIG. 8, it should be appreciated that other deformations and / or irregularities may be detected in the acquired waveform.

  FIG. 9 illustrates two waveforms. Sine wave 900 is an example of a predicted sine wave, while waveform 910 is an example of an actual waveform acquired during a radial k-space MR eye. The system and method described herein predicts the sine wave 900, acquires the waveform 910, and whether the data points are within a predetermined, configurable tolerance (predetermined, configurable tolerance). Compare the two waveforms to determine. If the waveform is within the tolerances, the system and / or method can proceed to the next step such as reproducing an image from the acquired MR eye data. However, if the comparison yields data outside of the tolerance, additional processing, such as selective processing of the image data, can be undertaken to correct for rotational movement during image acquisition, which is described below. This will be described in more detail.

  Turning to FIG. 10, an example waveform 1010 collected during MR eye, the wave representing the result of the rotational motion, is illustrated alongside the predicted sine wave 1000. The predicted sine wave 1000 was calculated from point source data obtained from a reference body associated with the object to be imaged. The actual wave 1010 was then collected from the MIR data. As can be seen, the actual wave 1010 is distorted starting at an identifiable point 1020 where the rotational motion occurred. Furthermore, the broken wave 1010 facilitates identifying the point 1040 where the rotational motion stopped. Time 1030 facilitates, for example, identifying the magnitude and / or direction of the rotation. As explained previously, rotational movement leads to gaps in the projection data and / or duplicate projection data. Thus, based on the location 1020, the interval 1030, and the location 1040, data from the subject's MR eye can be selectively processed for rotational motion correction. The selective processing reorders the data, removes one or more data from the set of projection data acquired during MR eye, and generates a signal indicating that additional projection data should be acquired. Including, but not limited to. When a signal is generated indicating that additional projection data is to be acquired, the systems and methods described herein receive the additional projection data, and the predicted sine wave is updated with the updated waveform. The data that generated the wave 1010 may be updated to implement the comparison again. In one example, a signal is generated indicating that the additional projection data is to be acquired, the additional projection data is received, the observed waveform is updated, and the predicted sine wave and the updated Processes such as re-comparison with the waveform can occur repeatedly. For example, they may be made until a comparison of the predicted sine wave and the updated observed waveform falls within a predetermined configurable tolerance and / or a predetermined configurable number of attempts. You may repeat until it is finished.

  By a distributed system and / or method in which the comparison of the predicted sine wave and the acquired waveform operates substantially in parallel with the MIR system that may be distinguished from the system for acquiring the MIR data It should be appreciated that the system and method described herein for detecting the rotational motion of an object should not increase the MR eye scan time as it can be done. While the calculation does not increase the scan time, one example method achieves an improvement in the signal-to-noise ratio in the observed image data by at least 50%. By recognizing the location, direction and magnitude of the rotational motion, “errors” can be removed from the acquired data, which leads to an improvement in the signal-to-noise ratio. In another example, the systems and methods described herein can reduce the signal-to-noise ratio of the selectively processed observed image data to the signal-to-noise ratio that would have been in the corresponding image data that was not destroyed by rotational motion. Realize improvement to within 33%. The ability to undertake processes such as removing duplicate data, averaging duplicate data, obtaining the first lost data, and reordering the data stored in the wrong place Realize the process of bringing about improvements. Such improvements can be measured by measurements including but not limited to signal-to-noise ratio, resolution, artifact level, and the like.

  As described above, the image data received and observed from the MIR scan of the object includes the radial k-space projection data associated with the object and the observation associated with the reference object associated with the object being scanned. Includes waveforms. However, the data associated with the reference object may leave artifacts in the observed image data. Accordingly, in one example method, additional processing is performed to remove the waveform data from the image data associated with the reference body.

  The system and method described herein implements a process for identifying and correcting rotational motion during image acquisition, but once such detection and correction is undertaken, an example of the system and method is It should be appreciated that an image of the object may be created based on the observed image data that has been selectively processed to correct. Further, the process performed to correct the rotational motion during image acquisition may be used in systems and methods that also undertake a process designed to reduce the translational effects of the object to be imaged. That should be appreciated. In one example, the translational motion correction is undertaken before the rotational motion correction.

  Looking at the exemplary system shown and described herein, exemplary computer implemented methodologies are better appreciated with reference to the flow diagrams of FIGS. For ease of explanation, the illustrated methodology is shown and described as a series of blocks, although some blocks may occur in different orders and / or other blocks shown and described. It should be appreciated that the methodology is not limited by the order of the blocks, since they can be performed simultaneously. Further, fewer than all of the illustrated blocks may be required to implement an exemplary methodology. In addition, unillustrated blocks can be used with additional and / or alternative methodologies added. In one example, the methodology is an application specific integrated circuit {ASIC}, a compact disc {Sea Day (CD)}, a digital versatile disc {Data DVD)}, random access memory {RAM}, read only memory {ROM}, programmable read only memory {PROM} Electrically erasable programmable read-only memory (electro stored on a computer readable medium including, but not limited to, an easily erasable programmable read only memory {EEPROM}, a disk, a carrier wave, and a memory stick. Implemented as computer-executable instructions and / or operations. It should be appreciated that the methodology can be implemented in software as that term is defined herein.

  In the flow diagram, rectangular blocks indicate “processing blocks” that may be implemented in software, for example. Similarly, diamond blocks also indicate “decision blocks” or “flow control blocks” that may be implemented in software, for example. Alternatively and / or additionally, the processing or decision block is functionally like a digital signal processor {DSP}, application specific integrated circuit (ASIC) and the like. It can be implemented with an equivalent circuit.

  The flow diagram does not depict the syntax for any particular programming language, methodology, or style {eg, procedural, object-oriented). Rather, the flow diagram illustrates functional information that may be used by those skilled in the art to program software, design circuits, and the like. It should be appreciated that in some instances program elements such as temporary variables, routine loops, etc. are not shown.

  Referring now to FIG. 11, a flowchart illustrates an example method 1100 for reducing the effect on rotational image quality of an object during MIR image acquisition. At 1110, point source data is acquired. This point source data can include measurements such as the projection angle used to characterize the point source and the final distance associated with the characterization of the point source.

At 1120, point source parameters are calculated. For example, the parameters R _res and Θ _res can be calculated from the point source data acquired at 1110. A unique sine wave generated by the point source during radial k-space MR eye at 1130 based at least in part on the parameters calculated at 1120 and / or the point source data acquired at 1110 Can be predicted. At 1140, a waveform associated with the point source is obtained from the object being imaged. Although blocks 1130 and 1140 illustrate predicting the sine wave and then acquiring the waveform data, it should be appreciated that such prediction and acquisition can occur substantially in parallel.

  At 1150, a determination is made as to whether the acquired waveform data satisfies a predetermined configurable tolerance when compared to the predicted sine wave data. For example, the sum of absolute values of the difference between the predicted point on the sine wave and the acquired point on the waveform is calculated. If the determination at 1150 is yes, processing proceeds to 1180 where the target image is reconstructed from the acquired data. However, if the determination at 1150 is no, at 1160, a determination is made as to whether to reorder one or more projection data points. If the 1160 decision is yes, the projection data is reordered. For example, data placed in a first location corresponding to a first, incorrect viewing angle may be moved to a second, correct viewing angle location. In addition and / or alternatively, the data may be averaged with data already stored at the correct viewing angle location, and may replace data already stored at the correct viewing angle location. Or it may be used to modify the data stored at the correct viewing angle location. If, for example, the difference between the predicted sine wave data and the acquired waveform data exceeds a predetermined, configurable threshold, indicating that the reordering does not make the desired decrease in signal-to-noise ratio. The determination at 1160 may be no. In such cases, the method 1100 may be extended to include restarting the image acquisition sequence.

  FIG. 12 illustrates an example method 1200 for reducing the effect on rotational image quality of an object during MIR image acquisition. Although the methods 1100 and 1200 will be discussed in conjunction with MIR, the systems and methods described herein include nuclear magnetic resonance imaging, x-ray imaging, sheet (CT), SPECT, optics It should be appreciated that the subject can be used where an object is imaged through techniques including, but not limited to, photographic imaging techniques and positron emission tomography. The optical imaging techniques can include, but are not limited to, techniques including bioluminescence and fluoresence.

  At 1210, point source data is acquired, and at 1220, point source parameters are calculated from the point source data. Thus, the location of the reference body associated with the object to be imaged can be calculated, which achieves at 1230 predicting a unique sine wave that will be created during imaging of the object. At 1240, observed waveform data from the reference mark is acquired. Thus, at 1250, a determination is made as to whether the acquired waveform data is within a predetermined, configurable tolerance of the sine wave predicted at 1230. If the decision at 1250 is yes, processing continues at 1270 where the subject image is played. However, if the decision at 1250 is no, for example, one or more points of the waveform data are obtained by repositioning the image forming means that selectively obtains one or more projection data from one or more viewing angles. A determination is made at 1260 as to whether to redo. If the 1260 decision is no, the target image is played at 1270. However, if the decision at 1260 is yes, processing returns to 1240. In one method example, the 1260 decision is at least partially determined by a predetermined, configurable number of attempts made to reacquire data and / or whether the method 1200 should be restarted. May be affected.

  FIG. 13 illustrates a computer 1300 having a processor 1302, a memory 1304, a disk 1306, an input / output port 1310, and a network interface 1312 operatively connected by a bus 1308. The executable components of the system described herein may be located on a computer such as computer 1300. Similarly, the computer-executable methods described herein may be executed on a computer such as computer 1300. It should be appreciated that other computers may be used with the systems and methods described herein. Furthermore, it should be appreciated that the computer 1300 can be located locally with respect to the MIR system, remotely with respect to the MIR system, and / or can be embedded within the MIR system. It is.

  The processor 1302 can be a variety of different processors including dual microprocessors and other multi-processor architectures. The memory 1304 can include volatile and / or nonvolatile memory. The nonvolatile memory includes a read only memory (ROM), a programmable read only memory (peelom), an electrically programmable read only memory (EPROM), and an electrically erasable programmable read only memory (EROM). EPROM, etc.) can be included, but is not limited thereto. The volatile memory is, for example, random access memory (ram), static RAM (esram (SRAM)), dynamic ram (dayram), synchronous DRAM (synchronous DRAM) {esdayram (SDRAM)}, double data rate. It can include double data rate SDRAM {DDR SDRAM}, and direct RAM bus RAM {DRRAM}. The disk 1306 includes, but is not limited to, devices such as magnetic disk drives, floppy disk drives, tape drives, Zip drives, flash memory cards, and / or memory sticks. In addition, the disk 1306 includes a compact disk chrome (CD), a CD recordable drive (CD-R drive), a CD rewritable drive (CD rewriteable drive). CD-RW drive)} and / or an optical drive such as a digital versatile ROM drive {Divide ROM (DVD ROM)}. The memory 1304 can store processing 1314 and / or data 1316, for example. The disk 1306 and / or memory 1304 can store operating systems that control and allocate resources of the computer 1300.

  The bus 1308 can be a single internal bus interconnect architecture and / or other bus architectures. The bus 1308 can be of various types including, but not limited to, a memory bus or memory controller, a peripheral or external bus, and / or a local bus. The local bus is an industrial standard architecture {Isa (ISA) bus, a microchannel architecture (MSA) bus, an extended ISA {EISA} bus , Peripheral component interconnect {PCI} bus, universal serial bus {USB (USB)}, and small computer system interface (small computer systems SC (Scalar system SC) )} It can be various, including but not limited to.

  Computer 1300 interacts with input / output device 1318 via input / output port 1310. Input / output devices 1318 can include, but are not limited to, keyboards, microphones, pointing and selection devices, cameras, video cards, displays, and the like. The input / output ports 1310 can include, but are not limited to, serial ports, parallel ports, and USB ports.

  The computer 1300 can operate in a network environment and is thus connected to the network 1320 by the network interface 1312. Through the network 1320, the computer 1300 may be logically connected to a remote computer 1322. The network 1320 may include, but is not limited to, a local area network {LAN}, a wide area network {WAN}, and other networks. The network interface 1312 is a fiber distributed data interface {FDDI}, a copper distributed data interface {CDI / Ethernet, CDDI}. Connect to local area network technologies including, but not limited to, EE802.3 (Ethernet / IEEE 802.3), Token Ring / IEEE 802.5 (token rings / IEEE 802.5) and the like I can do it. Similarly, the network interface 1312 includes point-to-point links and integrated services digital networks (ISDN), packet switching networks, and packet switching networks. It can be connected to wide area network technology including, but not limited to, digital switching lines {circuit switching networks such as DSL}.

  Referring now to FIG. 14, information may be transmitted between the various computer components associated with the systems and methods described herein via a data packet 1400. An exemplary data packet 1400 is shown. The data packet 1400 includes a header field 1410 having information such as the packet length and type. A source identifier 1420 follows the header field 1410, which includes, for example, the address of the computer component from which the packet 1400 originates. Following the source identifier 1420, the packet 1400 has a destination identifier 1430, which holds, for example, the address of the computer component to which the packet 1400 is ultimately delivered. Source and destination identifiers can be, for example, globally unique identifiers, URLS {uniform resource locators}, path names, and the like. A data field 1440 in the packet 1400 contains various information intended to receive computer components. For example, the data column 1440 can hold data including, but not limited to, acquired projection data, target data, reference data, modified projection data, requests for additional projection data, combinations thereof, and the like. The data packet 1400 terminates in an error detection and / or correcting field 1450, which allows a computer component to determine whether it has received the packet 1400 properly. Although six columns are illustrated in the data packet 1400, it should be appreciated that more and / or fewer columns can be present in the data packet.

  FIG. 15 is a schematic block diagram of an example system 1500 for improving the image quality of an object, where the image has been degraded by the rotational movement of the object during image acquisition. The system 1500 includes a data receiver 1520 that receives image data from the object. The data 1510 includes data relating to the object and data associated with the reference (eg, marked on the object, attached to the object, placed near the object). Yes, but not limited to them. The data receiver 1520 stores the data in one or more data stores 1530. In one example, the image data is stored in a set of data stores while the reference data is stored in a second set of data stores. In another example, the target data and the reference data are stored together. The data store 1530 can be, for example, a disk, tape, memory (eg, ram), and the like. The data store 1530 can store the image data in a data structure including, but not limited to, arrays, trees, lists, tables, and the like.

  The system 1500 includes a fiducial analyzer 1540 that determines reference sinusoidal fiducial trajectory data from initial point source data. Further, the reference analyzer 1540 examines the data from the data store 1530 and calculates actual reference trajectory data (actual fiducial trajectory data). The reference analyzer 1540 then compares the compliant sinusoidal reference trajectory data with the actual reference trajectory data and stores the comparison result.

  The system 1500 also includes an object data processor 1550, which is stored in the data store 1530 to provide improved quality of images degraded by rotational motion during image acquisition. Selectively manipulate one or more pieces of the target data. The subject data processor 1550 initiates this selective processing based at least in part on the comparison made by the reference analyzer 1540.

  In one example system 1500, the data 1510 comprises one or more projection data of radial k-space data obtained from an MR eye device. In another example, the data 1510 is sinogram data. In one example system 1500, the data 1510 includes reference data received from a high signal reference mark. The high signal reference mark may be, for example, a gadolinium filled tuned fiducial. Another example system 1500 has a low signal fiducial marker. Another example system 1500 includes a fiducial data filter that removes the reference data from the image data in order to realize that the artifact removes the artifact from the reproduced image associated with the reference. .

  The target data processor 1550 performs an action including, but not limited to, deleting projection data, rearranging the projection data in the data store 1530, and / or combining the projection data. Thus, the target data can be manipulated in order to improve the quality of the image deteriorated during the image acquisition due to the rotational motion. In general, the target data processor 1550 modifies the acquired image data so that the reference trajectory data is more similar or matches the compliant trajectory data. Since the system 1500 can generate modified target data, the extension of the system 1500 is, for example, an image processor that creates a viewable image of the target from the manipulated target data. (Not illustrated). Further, an extension to system 1500 is a signaler that generates a control signal indicating that the system 1500 wants one or more additional projection data from the radial k-space. (Not illustrated). When this additional data is acquired, a data integrator (not shown) can integrate the additionally acquired data with previously acquired and modified data. . The integration can include, but is not limited to, replacing previously acquired data, averaging previously acquired data with corrected data, and the like. The data receiver 1520, the reference analyzer 1540, the target data processor 1550, the image processor, the traffic light, and the data accumulator can be computer components whose terms are described herein.

  Referring now to FIG. 16, an application programming interface {API} 1600 is illustrated to provide access to a system 1610 that includes a data corrector. The API 1600 may be used, for example, by a programmer 1620 and / or processes 1630 to gain access to processing performed by the system 1610. For example, a programmer 1620 can write a program to access a data correcting computer component 1610 (eg, exercise the operation, monitor the operation, access the function) In the part, writing such a program is facilitated by the presence of the API 1600. Rather than the programmer 1620 having to understand the inside of the data modifier 1610, the programmer's task is simplified by only having to know the interface 1600 to the data modifier 1610. The This realizes summarizing the function of the data modifier 1610 while exposing its function. Similarly, the API 1600 can be used to provide data values to the system 1610 and / or retrieve data values from the system 1610. For example, a programmer 1620 may wish to present image data to the data modifier 1610 and thus the programmer 1620 may use the image data interface component 1640 of the API 1600. Similarly, the programmer 1620 may wish to present reference data to the data modifier 1610, and thus may use the reference data interface component 1640 of the API 1600. After receiving the image data and reference data, the data modifier 1610 may pass the modified data to the process 1630 via the modified data interface 1660 of the API 1600, for example.

  Thus, in one example of the API 1600, a set of application program interfaces is stored on a computer readable medium. The interface can be executed by a computer component to gain access to the data modifier. The interface includes a first interface that receives and / or returns image data associated with the image, a second interface that receives and / or returns reference data associated with the reference, and a modification generated from the image data and the reference data. A third interface for receiving and / or returning the processed image data may be included, but is not limited thereto.

  The systems, methods, and objects described herein may be stored, for example, on a computer readable medium. Media includes application-specific integrated circuits (ASIC), compact disks (seeds), digital versatile disks (devices), random access memories (rams), load-only memories (roms), programmable read-only memories (peoms), disks, Carrier waves, memory sticks, and the like can be included, but are not limited to these.

  Thus, an example computer-readable medium can store computer-executable instructions for a computer-implemented method to reduce the effect on image quality of a subject's rotational motion during image acquisition. The method includes receiving point source data associated with a reference object associated with an object to be imaged and calculating a point source location based on the point source data. The method then calculates predicted sine wave data based on the point source location and / or the point source data. The method includes receiving observed image data having a set of projection data associated with an object to be imaged and observed waveform data associated with predicted sine wave data. Once the observed image data is retrieved, the method compares the predicted sine wave data with the observed waveform data and reduces the effect of the observed image data to reduce the effects of rotational motion of the imaged object. The processed image data is selectively processed. This process includes, but is not limited to, removing projection data, reordering projection data, and obtaining additional projection data.

  Similarly, a computer readable medium can store computer-executable parts of the system to improve the image quality of the object degraded by the rotational movement of the object during image acquisition. The system has a data receiving component for receiving image data from the object, where the image data has object data and reference data. The system also has a data store for storing the target data and the reference data, either individually or collectively. The reference analyzer determines compliant sinusoidal reference trajectory data and actual reference trajectory data, and then compares the two trajectories. The target data processor selectively manipulates the target data to improve the quality of the target image degraded by the target-based rotational motion.

  FIG. 17 illustrates an example system 1700 in which the MR eye system 1710 interacts with a data modifier 1715. The data modifier 1715 can include, for example, a data receiver 1720, a data store 1730, a reference analyzer 1740, and a target data processor 1750, which are substantially similar to the computer components described in FIG. The MIR system 1710 may be operatively connected to the data receiver 1720 by techniques including, but not limited to, direct connection, local area network, wide area network, satellite communication, cellular communication, and the like.

  FIG. 18 illustrates an example MR eye system 1800 having computer components that form a data modifier. The data modifier components include a data receiver 1820, a data store 1830, a reference analyzer 1840, and a subject data processor 1850, which are substantially similar to the computer components described above in connection with FIG. It is not limited to.

  What has been described above includes several examples. All possible combinations of parts or methodologies for the purpose of explaining the methods, systems, computer readable media etc. used to improve the quality of the MR eye image affected by the rotational movement of the object being imaged. Of course it is impossible to explain. However, those skilled in the art may recognize that further combinations and permutations are possible. Accordingly, this application is intended to embrace alterations, modifications, and variations that fall within the scope of the appended claims. Further, to the extent that the term “includes” is used in the examples and claims, the term “comprising” is used so that the term is interpreted when used as a transitional term in the claim. Intended to be comprehensive.

Illustrates examples of projection angles used in radial k-space MIR and objects before and after rotational movement. 2 illustrates an example data structure for storing a set of projection data. Illustrates an example relationship between viewing angle and rotation correction. The example of the MR eye image before and after correction about rotational motion is illustrated. The example of the MR eye image before and after correction about rotational motion is illustrated. Illustrates a measurement example used to correct for rotational motion. Illustrate an example point source, image space, and sinogram space. Illustrates an example sinogram representing a rotational motion result on a compliant waveform Illustrate two waveform examples, one collected during MR eye and one predicted from point source data. Illustrate two example waveforms, one predicted from point source data and one collected during MR eye, where the collected waveforms are of rotational motion during image acquisition. Represents the result. Illustrates an example method for reducing the effect of rotational motion of an object during image acquisition on image quality. Illustrates an example method for reducing the effect of rotational motion of an object during image acquisition on image quality. 1 is a schematic block diagram of an exemplary computing environment in which the systems, methods, APIs, data packets, and computer readable media described herein interact. Fig. 3 illustrates an example data packet used in one aspect of the present invention. FIG. 2 is a schematic block diagram of an example system for improving the quality of an MR eye image degraded by rotational movement during image acquisition. 2 illustrates an example of an eye eye used in one aspect of the present invention. Figure 3 illustrates an example of an MR eye system interacting with a data modifier. Figure 3 illustrates an example MR eye system including a data modifier.

Claims (43)

A computer-implemented method for reducing the effect on rotational image quality of an object during image acquisition, the method receiving point source data associated with a reference object associated with the object;
Calculating a point source location based at least in part on the point source data;
Calculating predicted sine wave data based at least in part on at least one of the point source location and the point source data;
Receiving observed image data, the observed image data comprising one or more projection data in radial k-space associated with the object, and further associated with the object and further the predicted sine wave Waveform data relating to the location of the reference body associated with the data, and the method also includes
Based on at least in part, the process of comparing the predicted sine wave data and the observed waveform data, and comparing the predicted sine wave data and the observed waveform data, to reduce the effects of the rotational motion of the object And the step of selectively processing the observed image data.   The object is at least one of nuclear magnetic resonance imaging (MIR), x-ray imaging, positron emission tomography, single photon emission computerized tomography, and visible light imaging. The method of claim 1, wherein the method is imaged through one.   The method of claim 1, wherein the point source data is obtained from at least first projection data and second projection data. 4. The method according to claim 3, further comprising the step of acquiring the first projection data (S ₁ ) at a first time T ₁ from a first viewing angle Θ ₁ resulting in a first projection distance r ₁ . The method. 5. The method according to claim 4, further comprising obtaining the second projection data (S ₂ ) at a second time T ₂ from the first viewing angle Θ ₂ resulting in the second projection distance r ₂ . The method. The method of claim 5, characterized in that the theta ₁ is perpendicular to the theta _2.   The method of claim 1, wherein the point source data comprises three or more projection data. The process of calculating the point source location is as follows:
Includes a process of determining the R _res, R _res is the displacement component of the polar coordinate of the point source and the process of the calculation includes a process of determining or theta _res, theta _res is of the point source 6. The method of claim 5, wherein the polar component is an angular component. R _res is determined by R _res = (r ₁ ² + r ₂ ² ) ^1/2 , where r ₁ is the first projected distance of orthogonal coordinates for the first projection data of the point source, and r ₂ is 9. The method of claim 8, wherein the second projection distance of the point source is a second projected distance in orthogonal coordinates. 10. The method of claim 9, wherein Θ _res is determined by Θ _res = tan ⁻¹ (r ₂ / r ₁ ). The method of claim 10, wherein calculating the predicted sine wave comprises calculating the predicted sine wave from R _res and Θ _res . 12. The method of claim 11, wherein the predicted sine wave is calculated by sin _res = (R _res ) cos (| Θ _res −Θ |), where Θ varies between viewing angles.   Furthermore, the first projection data and the second projection data are repeatedly acquired until it can be mathematically verified that no rotational motion has occurred between the acquisition of the first projection data and the second projection data. 4. The method of claim 3, comprising the steps of:   14. The method of claim 13, comprising creating an image of the object based on the selectively processed observed image data. Generating a signal indicating that one or more additional projection data is to be acquired;
Receiving the additional projection data;
Updating the observed waveform data based at least in part on the additional projection data;
Based on at least in part, comparing the predicted sine wave data with the updated observed waveform data, and comparing the predicted sine wave data with the updated observed waveform data, 14. The method of claim 13, comprising selectively reprocessing.   4. The method of claim 3, further comprising the step of obtaining the first projection data and the second projection data before projection data for reproducing the target image.   Selectively processing the observed image data includes removing one or more projection data from the one or more projection data; reordering the projection data within the one or more projection data; The method of claim 1, comprising at least one of mathematically combining one or more projection data.   18. The process of repeating the process of claim 17 until the comparison between the predicted sine wave data and the updated observed waveform data falls within a predetermined configurable tolerance. Of the method.   19. The method of claim 18, comprising creating an image of the object based on the selectively processed observed image data.   The method of claim 1, further comprising the step of processing the observed image data to remove the observed waveform data.   The method of claim 1, comprising the step of creating an image of the object based on the selectively processed observed image data.   Before the process of selectively processing the observed image data to reduce the influence of the rotational movement of the object, the process of selectively processing the observed image data to reduce the influence of the translational movement of the object. The method of claim 1, comprising:   A computer-readable medium storing computer-executable instructions operable to perform the method of claim 1.   The method of claim 1, wherein there are two or more reference bodies associated with the object. A system for improving the quality of an image of an object, wherein the image is degraded by rotational movement of the object during image acquisition,
A data receiver for receiving image data from the object, the image data comprising object data and reference body data, the system also comprising:
One or more data stores for storing the target data and the reference body data;
For determining compliant sinusoidal reference trajectory data, actual reference trajectory data, and comparison trajectory data for storing a result of comparison between the compliant sine wave type reference trajectory data and the actual reference trajectory data; A reference analyzer, and a target data processor for selectively manipulating the target data to improve the quality of the image where the image is degraded by rotational movement of the target during image acquisition. The system to do.   26. The system of claim 25, wherein the object data comprises one or more projection data of radial k-space data acquired from an MR device.   27. The system of claim 26, wherein the object data and the reference body data are stored separately.   27. The system of claim 26, wherein the image data is sinogram data.   27. The system of claim 26, wherein the reference body data is received from a high signal reference mark.   30. The system of claim 29, wherein the high signal reference mark is a gadolinium filled tuning reference marker.   27. The system of claim 26, wherein the reference data is received from a low signal reference mark.   27. The system of claim 26, further comprising a reference data filter for removing the reference data from the image data.   The subject data processor comprises logic to delete one or more projection data, reposition one or more projection data in the one or more data stores, and combine the one or more projection data 27. The system of claim 26.   27. The system of claim 26, comprising an image processor for creating a viewable image of the object from the selectively manipulated object data.   27. The system of claim 26, further comprising a traffic light that generates a control signal indicating that the system is seeking one or more additional projection data from the radial k-space.   36. The system of claim 35, further comprising a data integrator for integrating one or more received additional projection data with one or more previously received projection data.   The system of claim 25, wherein the system is embodied on a computer readable medium. Radial k-space MR data, but in a data packet for transmitting the MR data modified to minimize the effect of subject rotational motion on the radial k-space MR data. A first column in which the data packet stores radial projection data obtained from the object;
A second field for storing reference data obtained from a reference marker associated with the object; and a third field for storing modified projection data, wherein the modified projection data is in the radial direction. The data packet derived from projection data and the reference data. A set of application programming interfaces embodied on a computer readable medium for execution by a computer component in conjunction with an application program to detect and reduce errors due to rotational motion in the MR eye projection data. A first interface for receiving and returning image data from an object to be imaged by the MIR;
A second interface for receiving and returning reference data from a reference marker associated with the object, and a third interface for receiving and returning modified projection data, wherein the modified projection data comprises: The set of application programming interfaces derived from the image data and the reference data. In a system for reducing artifacts caused by rotational movement of an object in an MR eye, the system receives a reference signal from a reference marker associated with the object to be imaged;
Means for receiving a target signal from the imaged target, the target being associated with the reference marker, and the system also comprising at least one of the reference signal and the target signal Means for detecting the rotational motion of the object imaged through one analysis, and the detected rotational motion, the reference signal, and the target signal based on at least in part And means for correcting rotational motion. In a system for producing an MR eye image, the system reduces the effect on the MR eye image of a nuclear magnetic resonance imager for acquiring the MR eye image and the rotational motion of the object being imaged. And a data corrector. A magnetic field generator for polarization for generating a magnetic field for the nuclear magnetic resonance imager to polarize in the examination region;
An RF generator for generating an exciting magnetic field that provides transverse magnetization in the nucleus provided for the polarizing magnetic field;
A sensor for detecting a nuclear magnetic resonance signal produced by the transverse magnetization;
A tilt generator for generating a magnetic field gradient to provide a lead component in the nuclear magnetic resonance signal, the lead component being along a first projection axis of a transversely magnetized nucleus The tilt generator is generating the next magnetic field gradient to provide the next lead component in the nuclear magnetic resonance signal, the nuclear magnetic resonance signal being the transversely magnetized nucleus The imager also includes a pulse controller operatively connected to the RF generator, the tilt generator, and the sensor. The pulse controller is performing a scan in which a series of data points are acquired at the lead points along the radial axis to form a nuclear magnetic resonance data view, and the next nuclear magnetic resonance The data view defines a nuclear magnetic resonance data set and the image The generator further comprises a data store for storing the nuclear magnetic resonance data set, and a processor for reproducing a display image array from the stored nuclear magnetic resonance data set. Item 42. The system according to Item 41.   43. The system of claim 42, wherein the data modifier is physically located within the nuclear magnetic resonance imager.

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