WO2012147471A1 - 医用画像処理装置、医用画像処理方法 - Google Patents
医用画像処理装置、医用画像処理方法 Download PDFInfo
- Publication number
- WO2012147471A1 WO2012147471A1 PCT/JP2012/059136 JP2012059136W WO2012147471A1 WO 2012147471 A1 WO2012147471 A1 WO 2012147471A1 JP 2012059136 W JP2012059136 W JP 2012059136W WO 2012147471 A1 WO2012147471 A1 WO 2012147471A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- penalty term
- weight
- true subset
- projection data
- term weight
- Prior art date
Links
- 238000012545 processing Methods 0.000 title claims description 54
- 238000003672 processing method Methods 0.000 title claims description 5
- 230000009467 reduction Effects 0.000 claims abstract description 57
- 230000000694 effects Effects 0.000 claims abstract description 28
- 238000003384 imaging method Methods 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims description 129
- 238000004364 calculation method Methods 0.000 claims description 63
- 230000008569 process Effects 0.000 claims description 62
- 238000012937 correction Methods 0.000 claims description 47
- 238000003860 storage Methods 0.000 claims description 34
- 238000011156 evaluation Methods 0.000 claims description 26
- 230000006870 function Effects 0.000 description 30
- 210000001015 abdomen Anatomy 0.000 description 16
- 238000004458 analytical method Methods 0.000 description 8
- 238000001514 detection method Methods 0.000 description 8
- 230000014509 gene expression Effects 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 210000004072 lung Anatomy 0.000 description 3
- 238000013178 mathematical model Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000012887 quadratic function Methods 0.000 description 2
- 230000007480 spreading Effects 0.000 description 2
- 238000003892 spreading Methods 0.000 description 2
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/032—Transmission computed tomography [CT]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5205—Devices using data or image processing specially adapted for radiation diagnosis involving processing of raw data to produce diagnostic data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/58—Testing, adjusting or calibrating thereof
- A61B6/582—Calibration
- A61B6/583—Calibration using calibration phantoms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
- G06T2211/424—Iterative
Definitions
- the present invention relates to a medical image processing apparatus or the like that performs correction processing of projection data and / or reconstruction processing of a reconstructed image by a successive approximation method including weights and penalty terms according to the output of a detector in an evaluation function. is there.
- the X-ray CT apparatus has an X-ray detector in which detection elements are arranged in the channel direction and the column direction.
- the projection data becomes a position of the discrete X-ray tube (opposing X It can also be said that the position of the line detector.)
- a unit for acquiring projection data at each X-ray tube position is referred to as a “view”.
- the arithmetic unit obtains the filter corrected projection data by superimposing the reconstruction filter in the channel direction of the projection data for each view and each column, and in the view direction with respect to the filter corrected projection data.
- a tomographic image is imaged non-destructively as a distribution map of the X-ray attenuation coefficient inside the subject.
- an image reconstruction method a method for imaging a tomographic image from projection data
- an image obtained by the image reconstruction method is referred to as a CT image.
- Image reconstruction methods are roughly divided into analysis methods and successive approximation methods.
- the analysis method is a method of solving a problem analytically based on the projected cutting plane theorem.
- the successive approximation method is a method of mathematically modeling an observation system that leads to acquisition of projection data and estimating the best image by an iterative method based on the mathematical model.
- the advantage of the analysis method is that the reconstructed image can be obtained directly from the projection data, so that the amount of calculation is overwhelmingly small.
- the advantage of the successive approximation method is that the physical process leading to the acquisition of projection data and the statistical fluctuations included in the projection data can be considered as mathematical models and statistical models, respectively. Artifacts) and quantum noise on the image can be reduced.
- the Feldkamp method which is an analysis method, or an improved method of the Feldkamp method has been mainly used because of the small amount of calculation.
- the practical use of the successive approximation method has begun to be studied.
- a technique for improving image quality by a successive approximation method has been proposed.
- the successive approximation method is roughly divided into the following two types depending on the estimated variable.
- (1) A method to construct an evaluation function using the projection value of projection data as a variable
- (2) Method for Constructing Evaluation Function Using Pixel Values of Image Data as Variables
- the method (1) is applied to projection data correction processing, which is preprocessing of image reconstruction processing.
- projection data correction processing to which the successive approximation method (1) is applied is referred to as “sequential approximation projection data correction processing”.
- Non-Patent Document 1 proposes a successive approximation projection data correction process using a weighted square error function with penalties expressed by the following equation as an evaluation function.
- 1,..., I,..., I are serial numbers that uniquely identify combinations of detector channels, detector columns, and views.
- p i and y i are provisional projection data and actual projection data specified by the serial number i, respectively.
- p ⁇ p 1 ,..., p i ,..., p I ⁇ is a vector of provisional projection data.
- ⁇ i is a set of detection elements adjacent to the detection element specified by the serial number i.
- ⁇ ik is a constant indicating the correlation between the detection element specified by the serial number i and the detection element specified by the serial number k.
- ⁇ ik is determined empirically.
- ⁇ (p i ⁇ p k ) is a potential function with the contrast between the provisional projection data p i and the provisional projection data p k as a variable.
- a quadratic function is used as the potential function.
- d i is a weighting factor weighted to the difference value between the actual projection data y i and the forward projection data.
- d i since is a value reflecting the detector output of the detection element identified by serial number i, and later, the d i is referred to as "detector output weight".
- the detector output is a value that depends on the number of detected photons.
- ⁇ is an arbitrary constant.
- Non-Patent Document 2 proposes a successive approximation reconstruction process using a weighted square error function with penalties expressed by the following equation as an evaluation function.
- Equation (2) 1,..., J,..., J are serial numbers that uniquely identify the pixels (two-dimensional coordinates) of the image data.
- X j is a pixel value in the pixel specified by the serial number j.
- X ⁇ X 1 ,... X j ,..., X J ⁇ is a vector representing image data.
- a ij is a matrix element that associates image data with projection data. This matrix is called a “system matrix” because it represents the characteristics of the imaging system of the X-ray CT apparatus through a mathematical model.
- X j is a set of pixels adjacent to the pixel specified by the serial number j.
- w jk is a constant indicating the correlation between the pixel specified by the serial number i and the pixel specified by the serial number k.
- w jk is the reciprocal of the distance between the center position of the pixel specified by the serial number j and the center position of the pixel specified by the serial number k.
- Non-Patent Document 2 proposes a quadratic function as the simplest potential function.
- Non-Patent Document 3 proposes a method of changing the shape of the potential function according to an arbitrary parameter.
- the provisional projection data p i in the update process is expressed as the actual projection data y i and the detector output weight d i according to the first term of the equation (1). While being restricted by the above, the penalty term of the second term is smoothed according to the contrast with the provisional projection data p k that is close. Therefore, it can be said that the arbitrary constant ⁇ in the equation (1) is a parameter for adjusting the degree of smoothing of the provisional projection data p i .
- Equation (2) can be said to be a parameter for adjusting the degree of smoothing of the image data Xj .
- Non-Patent Document 2 the arbitrary constant ⁇ is a constant value throughout the successive approximation reconstruction process.
- Non-Patent Document 4 proposes a method for changing ⁇ . If ⁇ remains constant, the CT image has non-uniform spatial resolution and noise reduction characteristics depending on the pixel position. Therefore, in Non-Patent Document 4, by changing ⁇ in each pixel, in addition to the contrast of adjacent pixels, the nonuniformity of spatial resolution and noise reduction characteristics is alleviated.
- the observer of the CT image has a quantum noise reduction effect and an artifact reduction effect (hereinafter both If they are not distinguished, they are referred to as “noise reduction effect”).
- the size of the detector output weight d i is responsible for the difference in noise reduction effect of the CT images between sites.
- the successive approximation method based on the equations (1) and (2) is applied from the chest to the abdomen. Lung small attenuation of X-rays to (organ that is a hollow) is present, the value is large element i of the detector output weight d i is compared with the number present chest, the value of the detector output weight d i In the abdomen where the element i having a large value is small, the restrictions on the detector output weights d i in the expressions (1) and (2) are weak. That is, if the same image processing is performed on the chest and abdomen, the noise reduction effect is different. Eventually, a CT image having a variation in noise reduction effect for each part, which is undesirable for an observer, is generated.
- Non-Patent Document 4 when ⁇ is changed in each pixel, it is possible to suppress variation in the noise reduction effect in the part. However, will be large and small equalization of the detector output weight d i, the noise reduction effect of the CT image is insufficient, i.e. the reduction of quantum noise and artifacts becomes insufficient. Furthermore, the method according to Non-Patent Document 4 has a problem that it takes a huge amount of time to determine ⁇ in each pixel.
- the present invention has been made in view of the above-described problems, and the object of the present invention is included in projection data without greatly increasing the amount of calculation for projection data over a plurality of parts. It is an object of the present invention to provide a medical image processing apparatus or the like that can create a medical image in which uniform quantum noise and artifact reduction effects are achieved in all parts.
- the first invention provides a correction process for projection data and / or by a successive approximation method including a detector output weight, which is a weight according to the detector output, and a penalty term in the evaluation function.
- a medical image processing apparatus for performing reconstruction processing of a reconstructed image, wherein information that uniquely identifies a combination of a channel of the detector, a column of the detector, and a view that is an acquisition unit of the projection data is a collective element
- a true subset determining unit for determining one or a plurality of true subsets based on imaging conditions and reconstruction conditions from the entire set, and the set elements included in the true subset for each true subset
- a penalty term weight calculation unit for calculating a penalty term weight that is a weight related to the penalty term based on the detector output weight corresponding to the penetrating term, and the successive approximation method using the penalty term weight for each true subset Run
- a medical image processing apparatus comprising a.
- the second invention performs the projection data correction process and / or the reconstructed image reconstruction process by the successive approximation method including the detector output weight, which is a weight according to the detector output, and the penalty term in the evaluation function.
- a medical image processing method to be performed wherein an imaging condition is obtained from an entire set having information that uniquely identifies a combination of a view of a channel of the detector, a row of the detector, and a view that is an acquisition unit of the projection data.
- a medical image processing method comprising: calculating a penalty term weight that is a weight related to the penalty term; and executing the successive approximation method using the penalty term weight for each true subset.
- a medical image in which uniform quantum noise and artifact reduction effects are achieved in all parts included in the projection data without significantly increasing the amount of calculation for the projection data over a plurality of parts. Can be created.
- the X-ray CT apparatus 1 performs processing of data obtained from the scanner 2 on which the X-ray tube 11 and the detector 12 are mounted, the bed 4 on which the subject 10 is placed, and the detector 12.
- An arithmetic device 5 an input device 6 such as a mouse, a trackball, a keyboard, and a touch panel, and a display device 7 for displaying a reconstructed image (CT image) and the like are included.
- the operator inputs shooting conditions and reconstruction conditions via the input device 6.
- the imaging conditions include, for example, a bed feeding speed, tube current, tube voltage, an imaging range (slice position range), and the number of imaging views per revolution.
- the reconstruction conditions are, for example, a region of interest, a reconstructed image size (reconstructed image size), a reconstruction filter function, and the like.
- the X-ray CT apparatus 1 is roughly composed of a scanner 2, an operation unit 3, and a bed 4.
- the scanner 2 includes an X-ray tube 11 (X-ray generator), a detector 12, a collimator 13, a drive device 14, a central controller 15, an X-ray controller 16, a high voltage generator 17, a scanner controller 18, and a bed control.
- the apparatus 19 includes a bed movement measuring apparatus 20, a collimator control apparatus 21, a preamplifier 22, an A / D converter 23, and the like.
- the central control device 15 inputs imaging conditions and reconstruction conditions from the input device 6 in the operation unit 3, and sends control signals necessary for imaging to the collimator control device 21, the X-ray control device 16, the scanner control device 18, and the bed control. Transmit to device 19.
- the collimator control device 21 controls the position of the collimator 13 based on the control signal.
- the X-ray control device 16 controls the high voltage generator 17 based on the control signal.
- the high voltage generator 17 applies a tube voltage and a tube current to the X-ray tube 11 (X-ray generator).
- X-ray generator In the X-ray tube 11, electrons with energy corresponding to the applied tube voltage are emitted from the cathode, and the emitted electrons collide with the target (anode), whereby X-rays with energy corresponding to the electron energy are Is irradiated.
- the scanner control device 18 controls the drive device 14 based on the control signal.
- the driving device 14 circulates around the subject 10 around a gantry portion on which the X-ray tube 11, the detector 12, the preamplifier 22, and the like are mounted.
- the couch controller 19 controls the couch 4 based on the control signal.
- the X-ray irradiated from the X-ray tube 11 is limited in the irradiation region by the collimator 13, is absorbed (attenuated) according to the X-ray attenuation coefficient in each tissue in the subject 10, passes through the subject 10, It is detected by a detector 12 arranged at a position facing the tube 11.
- the detector 12 includes a plurality of detection elements arranged in a two-dimensional direction (a channel direction and a column direction perpendicular to the channel direction). X-rays received by each detection element are converted into actual projection data.
- the X-rays detected by the detector 12 are converted into current, amplified by the preamplifier 22, converted into digital data by the A / D converter 23, LOG converted, calibrated, and used as actual projection data. Input to the arithmetic unit 5.
- the actual projection data can also be said to be a discrete X-ray tube position in the rotation direction (an opposing detector position). ).
- the unit of acquisition of actual projection data at each X-ray tube position is a “view”.
- the computing device 5 includes a reconstruction computing device 31, an image processing device 32, and the like.
- the input / output device 9 includes an input device 6 (input unit), a display device 7 (display unit), a storage device 8 (storage unit), and the like.
- the reconstruction calculation device 31 performs an image reconstruction process using the actual projection data, and generates a reconstructed image.
- the reconstruction calculation device 31 superimposes a reconstruction filter on the actual projection data of each view to generate filter-corrected projection data, and weights the view-corrected weight on the filter-corrected projection data to perform backprojection processing.
- a tomographic image is imaged non-destructively as a distribution map of the X-ray attenuation coefficient inside the subject 10.
- the reconstruction calculation device 31 stores the generated reconstruction image in the storage device 8. Further, the reconstruction calculation device 31 displays the CT image on the display device 7. Alternatively, the image processing device 32 performs image processing on the reconstructed image stored in the storage device 8 and displays it on the display device 7 as a CT image.
- the X-ray CT apparatus 1 is a multi-slice CT that uses a detector 12 in which detector elements are arranged in a two-dimensional direction, and a single that uses a detector 12 in which detector elements are arranged in one row, that is, in a one-dimensional direction (channel direction only). Broadly divided into slice CT.
- multi-slice CT an X-ray beam spreading in a cone shape or a pyramid shape is irradiated from an X-ray tube 11 as an X-ray source in accordance with the detector 12.
- an X-ray beam spreading in a fan shape is emitted from the X-ray tube 11.
- X-ray irradiation is performed while the gantry section circulates around the subject 10 placed on the bed 4 (however, scanogram imaging is excluded).
- the imaging mode in which the bed 4 is fixed during imaging and the X-ray tube 11 circulates around the subject 10 in a circular orbit is called an axial scan.
- a photographing mode in which photographing is performed with the bed 4 fixed and the bed 4 is moved to the next photographing position is called step-and-shoot scanning. Since the axial scan is considered as a step-and-shoot scan in which the bed 4 is moved to the photographing position only once, both are hereinafter collectively referred to as a step-and-shoot scan.
- the imaging mode in which the bed 4 continuously moves and the X-ray tube 11 circulates around the subject 10 in a spiral trajectory is called a spiral scan.
- the bed control device 20 keeps the bed 4 stationary while taking a picture.
- the bed control device 20 translates the bed 4 in the direction of the body axis during shooting according to the speed of the bed feeding as the shooting condition input via the input device 6.
- the X-ray CT apparatus 1 is, for example, a multi-slice CT. Further, the scan method of the X-ray CT apparatus 1 is, for example, a rotate-rotate method (third generation).
- FIG. 3 with reference to FIG. 4, the formula (1) will be described detector output weights d i in (2).
- FIG. 3 a cross-sectional image 41 (CT image) imaged by the X-ray CT apparatus 1 by a reconstruction process, a vertical view (a) and a horizontal view of the cross-sectional image 41 of the shoulder portion of the phantom simulating a human body
- CT image CT image
- a direction view (b) is shown schematically.
- the dimension of the column of the detector 12 is not considered here, but is limited to the two dimensions of the view and the channel 42.
- the horizontal view (b) there are many channels 42 in which the X-ray transmission length in the human body is long and the detector output shows a small value compared to the vertical view (a). That is, in the horizontal view (b), the detector output includes a large amount of noise. Note that this causes streak-like artifacts mainly in the horizontal direction in the CT image reconstructed by the image reconstruction method of
- the detector output weights d i in Equations (1) and (2) are values corresponding to the detector outputs, the detector output weights d i corresponding to the vertical view (a) are more in the horizontal direction.
- the detector output weights d i corresponding to the view (b) are smaller.
- the constraint by the first term in equations (1) and (2) is relatively weaker, and the second term (penalty term) ) Will be strongly affected by the smoothing effect. Therefore, in the successive approximation method using the evaluation functions such as Expressions (1) and (2), noise included in the detector output can be averaged, and streak-like artifacts generated in the CT image can be effectively reduced. Further, with the same principle, the successive approximation method can effectively reduce the quantum noise generated in the CT image.
- the magnitude of the detector output weight d i causes a difference in the noise reduction effect of the CT image between the parts.
- FIG. 4 for the chest and abdomen of a phantom simulating a human body, a coronal plane image 43 (CT image) imaged by the X-ray CT apparatus 1 by reconstruction processing and a view corresponding to the chest of the coronal plane image 43 are displayed.
- CT image coronal plane image
- the range (a) and the view range (b) corresponding to the abdomen are schematically shown.
- the channel dimensions of the detector 12 are not considered here, but are limited to the two dimensions of the view and column 44. 45 indicates the scanning direction, that is, the direction opposite to the direction in which the bed 4 is moved.
- the average detector output value in the chest is larger than the average detector output value in the abdomen. This is because the amount of attenuation of X-rays is relatively small compared to the abdomen and the like because the chest includes the lung (hollow part).
- the variation in noise reduction effect due to this part is made uniform by medical image processing described later.
- the medical image processing apparatus of the present invention may be the arithmetic device 5 included in the X-ray CT apparatus 1 or a general-purpose computer not included in the X-ray CT apparatus 1. Furthermore, the X-ray CT apparatus 1 and the medical image processing apparatus may not be connected via a network. In order to avoid confusion, the arithmetic device 5 will be described below as the medical image processing device of the present invention.
- the input unit, the display unit, and the storage unit included in the medical image processing apparatus of the present invention may be the input device 6, the display device 7, and the storage device 8 included in the X-ray CT apparatus 1, or the X-ray CT.
- a device provided in a general-purpose computer not included in the device 1 or an external device may be used.
- the input device 6, the display device 7, and the storage device 8 will be described below as an input unit, a display unit, and a storage unit included in the medical image processing apparatus of the present invention.
- the arithmetic device 5 performs projection data correction processing by a successive approximation method including a detector output weight (weight according to the output of the detector 12) and a penalty term in the evaluation function.
- the operator inputs imaging conditions and reconstruction conditions, and a desired calculation time and desired noise reduction rate of the successive approximation process via the input device 6 (step 1).
- the arithmetic unit 5 may automatically set the values stored in the storage unit 8 together with the projection data. For example, the arithmetic device 5 displays the value of the photographing condition stored in the storage device 8 on the display device 7, and the operator only needs to perform the confirmation work.
- the arithmetic device 5 displays default values, options, and the like on the display device 7 so as to support the operator's input work.
- the arithmetic device 5 may display options such as “high speed”, “medium speed”, and “low speed” on the display device 7. In this case, the arithmetic device 5 stores the calculation time for each option in the storage device 8. Then, the arithmetic device 5 sets a desired calculation time according to the option selected by the operator.
- the arithmetic device 5 may display options such as “high image quality”, “medium image quality”, and “low image quality” on the display device 7. In this case, the arithmetic device 5 stores the noise reduction rate for each option in the storage device 8. Then, the arithmetic device 5 sets a desired noise reduction rate in accordance with the option selected by the operator.
- the computing device 5 calculates one or more true subsets ⁇ S 1 ,..., S m ,..., S M ⁇ based on the imaging conditions and reconstruction conditions input in step 1. Determine (step 2).
- step-and-shoot scanning In the case of the step-and-shoot scan, it is assumed that the same part is imaged while the position of the bed 4 is fixed. Then, the arithmetic device 5 determines a set element of each true subset so that projection data collected while the position of the bed 4 is fixed belongs to the same true subset.
- the number of divisions of the entire set ⁇ that is, the number M of the true subset S m is equal to the number of times the position of the bed 4 is moved.
- number of views V 1 to be photographed at each position of the bed 4 ⁇ ⁇ ⁇ , V m, ⁇ ⁇ ⁇ , and V M.
- the number of views overall shooting V V 1 + ⁇ + V m + ⁇ + V M.
- the number of set elements of the subset S m is the view number V m to be photographed at each position of the bed 4, the number of channels C of the detector 12, the number obtained by multiplying the number of columns R of the detector 12 V m ⁇ C ⁇ R.
- the arithmetic unit 5 determines the number of set elements included in the true subset S m based on the number of views determined as the calculation unit of the back projection process in the successive approximation method, that is, the number of back projection views. To do. This is because the arithmetic unit 5 needs to determine each true subset S m so as to have projection data necessary for creating one cross-sectional image as a set element.
- the number of backprojection views corresponds to the phase width tw of the backprojection process (hereinafter referred to as “backprojection phase width tw”) as an imaging condition.
- the value is obtained by multiplying the set number of shooting views V d per round.
- the value indicating the phase is 2, etc.
- the backprojection phase width tw is an arbitrary parameter, and in Non-Patent Document 5, it is determined empirically according to the bed feeding speed and the reconstructed image size.
- the computing device 5 stores the back projection phase width tw in the storage device 8 in advance for each bed feeding speed and reconstructed image size. Then, the arithmetic device 5 acquires a single back projection phase width tw corresponding to the bed feeding speed set as the imaging condition and the reconstructed image size set as the reconstruction condition from the storage device 8. Next, the arithmetic unit 5 sets a value obtained by multiplying the number of photographic views V d per revolution set as the photographic condition by the back projection phase width tw acquired from the storage device 8 as the number of back projection views.
- the true subset may be determined after replacing the number of backprojection views with 1/2 round (minimum unit of backprojection). Note that the value of the reconstruction condition is applied to the back projection process regardless of this replacement.
- the number of divisions of the entire set ⁇ that is, the number M of the true subset S m is a quotient obtained by dividing the number of views V of the entire imaging by the number of backprojection views tw ⁇ V d .
- Last View entire photographing V is, if not divisible by back projection view number tw ⁇ V d, remainder view, included at the end of the subset S M.
- the number of set elements in each true subset S m is the same, and the number tw ⁇ V d ⁇ C multiplied by the backprojection phase width tw, the number of captured views V d per round, the number of channels C, and the number of columns R ⁇ R.
- the calculation unit 5 for each subset S m determined in step 2, based on the detector output weights d i corresponding to the set elements as fall within the true subset S m, according to the penalty term Weights (penal term weights) ⁇ 1 ,..., ⁇ m ,..., ⁇ M ⁇ are calculated (step 3). Details of the penalty term weight calculation processing will be described later with reference to FIGS.
- the arithmetic unit 5 executes a successive approximation method for i ⁇ S m using the penalty term weight ⁇ m for each true subset S m (step 4). That is, the arithmetic unit 5 executes the successive approximation projection data correction process while changing the penalty term weight ⁇ m for each part.
- the following expression is an example of an evaluation function.
- the technical idea of the present invention is to change the penalty term weight in the evaluation function for each part, if the evaluation function includes the detector output weight and the penalty term, the numerical analysis used for the optimization It can be applied regardless of the law.
- Equation (3) Deriving the update formula in the successive approximation method from Equation (3) can be realized by a known method.
- the update equation can be derived by using the GauSS-Seidel method.
- Arithmetic unit 5 derives the update equation in the successive approximation from Equation (3), a penalty term weight beta m of the calculated detector output weights d i and the true part each set S m, the derived updated formula Substituting and correcting the projection data by the successive approximation method.
- the operator can arbitrarily select one of the two in consideration of the desired calculation time and the accuracy of the penalty term weight estimation.
- the arithmetic unit 5 calculates a penalty term weight value (hereinafter referred to as ⁇ penalty '') that achieves a noise reduction effect equivalent to the noise reduction rate with respect to the reference phantom for each noise reduction rate.
- Term term reference value a penalty term weight value (hereinafter referred to as ⁇ penalty '') that achieves a noise reduction effect equivalent to the noise reduction rate with respect to the reference phantom for each noise reduction rate.
- Term term reference value is stored in the storage device 8 in advance.
- the arithmetic unit 5 registers penalties weight reference values ⁇ * 10 , ⁇ * 20 , ⁇ * 30 ,... For noise reduction rates of 10%, 20%, 30%,.
- the stored noise reduction rate table is stored in the storage device 8.
- the penalties weight reference values ⁇ * 10 , ⁇ * 20 , ⁇ * 30 , ... are taken as a circular phantom or a phantom simulating a human body as a reference phantom in advance. The value obtained by executing the configuration process.
- the arithmetic device 5 acquires a penalty term weight reference value corresponding to the input noise reduction rate from the storage device 8.
- the calculation unit 5, for each subset S m calculates a representative value based on the detector output weights d i corresponding to the set elements contained within each subset S m.
- the calculation unit 5 calculates a representative value based on the detector output weights d i corresponding to the set elements contained within each subset S m.
- the calculation device 5 each subset S m, the average and median of the detector output weight d i corresponding to the set elements as fall within the true subset S m, as well as the detector output weights d i It is desirable that any one of the three values of the class that divides the whole by a predetermined ratio in the histogram in which is a class is a representative value for each true subset S m .
- the arithmetic device 5 selects a table value corresponding to the desired noise reduction rate input by the operator from the noise reduction rate table stored in the storage device 8 (step 11).
- the computing device 5 creates a subset T m of the true subset S m from the set elements included in the true subset S m according to the desired calculation time input by the operator (step 12).
- the arithmetic device 5 creates the lower set T m by thinning out the set elements included in S m based on a predetermined thinning rule.
- the view is thinned out repeatedly. This is because when the view is skipped, the influence on the image quality of the finally created cross-sectional image is small.
- the number of captured views Vd per rotation is 360.
- the number of set elements of the sub-set T m is 4/5 of the number of set elements of the true subset S m. It becomes.
- the thinning amount can be adjusted according to the desired calculation time input by the operator. In this way, the calculation time can be adjusted in accordance with the intention of the operator by creating the subset T m of the true subset S m and executing the processing described later on the subset T m .
- the arithmetic unit 5 constructs a histogram using the detector output weights d i corresponding to the set elements of the lower set T m created in step 12 as a class (step 13).
- calculation method of the detector output weight d i may use any known technique.
- the arithmetic device 5 may perform calculation by multiplying the air data after performing inverse logarithmic transformation on the projection data.
- FIG. 7 shows an example of a histogram.
- the horizontal axis (class) is the detector output weight d i
- the vertical axis (frequency) is the number of set elements corresponding to the detector output weight d i equal to the value of each class.
- Histograms have different distributions depending on the size and part of the subject and can be said to be an index that well expresses their characteristics.
- the maximum peak exists where the class is a value close to zero. Further, the histogram of the shoulder portion 51, overall, a lower value of the detector output weights d i. This is because the shoulder 51 includes many bones with a large amount of X-ray attenuation.
- the maximum peak exists where the class is close to 1. Comparing the histograms of the chest 52 and the abdomen 53, the frequency of the class 1 to 3 is the histogram of the chest 52> the histogram of the abdomen 53, and the frequency of the class 5 or more is the histogram of the chest 52 ⁇ the histogram of the abdomen 53. ing. This is because the chest 52 includes a lung with a small amount of X-ray attenuation.
- the arithmetic unit 5 calculates the area of each histogram shown in FIG. 7, and specifies a class that divides the area of the histogram into a predetermined ratio as a representative value (step 14).
- the representative value may be a median value or an average value of the detector output weights d i included in i ⁇ S m . The operator can arbitrarily select these.
- the computing device 5 calculates the penalty term weight ⁇ m of the true subset S m by multiplying the table value selected in Step 11 by the class (representative value) specified in Step 14 ( Step 15).
- the penalty term weight ⁇ m of the true subset S m can be calculated without significantly increasing the amount of calculation.
- the second penalty term weight calculation process is based on the following two findings. (1) It is empirically clear that when the detector output weight is a constant and the successive approximation method is executed with an arbitrary penalty term weight, almost the same noise reduction rate can be obtained regardless of the subject and the part. (2) There is a high correlation between the correction amount of the projection value (pixel value) by the successive approximation method and the noise reduction rate.
- the arithmetic unit 5 calculates the true subset S for the correction amount calculation function for calculating the correction amount of the projection data when the successive approximation method is executed. Substitute the detector output weights d i and projection data corresponding to the set elements included in m .
- the process for determining the predetermined correction amount reference value is as follows. Similar to the first penalty term weight calculation process, the arithmetic device 5 stores the noise reduction rate table in the storage device 8, and from the storage device 8, the penalty term weight reference value corresponding to the input noise reduction rate is stored. get. Then, the calculation device 5 calculates the correction amount of the projection data when the successive approximation method is executed using the penalty term weight reference value acquired from the storage device 8 with the detector output weight as a constant, and the predetermined correction Use quantity reference value.
- FIG. 8 shows processing for calculating the penalty term weight ⁇ m for the true subset S m , but the same applies to other true subsets.
- the arithmetic device 5 selects a table value corresponding to the desired noise reduction rate input by the operator from the noise reduction rate table stored in the storage device 8 (step 21). Note that the stored table values are different between the noise reduction rate table used for the first penalty term weight calculation process and the noise reduction rate table used for the second penalty term weight calculation process due to the difference in processing contents.
- the computing device 5 creates a subset T m of the true subset S m from the set elements included in the true subset S m according to the desired calculation time input by the operator (step 22).
- the process of creating the subset T m is the same as in Step 12.
- the calculation unit 5 the selected table value at step 21, and, using the projection data corresponding to the set elements of subset T m created in step 22, the detector output weight d i any A projection value after application of the successive approximation projection data correction process is estimated with a constant.
- the approximate value is substituted. Assuming that the projected value of interest is calculated from only the adjacent channels, columns, and views, the approximate value can be easily calculated. Further, the approximate value may be calculated using a known method after replacing the inverse matrix necessary for calculating the projection value after the iteration with an approximate inverse matrix.
- the arithmetic unit 5 calculates an error between the approximate value and the projection value before application of the successive approximate projection data correction process, and sets the sum of the errors as a correction amount reference value (step 23).
- a known index such as an absolute error or a square error can be used for the error calculation process.
- the arithmetic device 5 substitutes the detector output weight di and the projection data corresponding to the set element of the sub-set T m created in Step 23 into the correction amount calculation function using the penalty term weight ⁇ m as a variable. . Then, the arithmetic unit 5 determines the penalty term weight so that the value of the correction amount calculation function is equal to the correction amount reference value calculated in step 23 (step 24).
- a known numerical analysis method for example, a bisection method
- the penalty term weight ⁇ m of the true subset S m can be calculated with high accuracy.
- the penalty term weight ⁇ m used for the successive approximation projection data correction process is set to a constant value in the true subset S m .
- the noise reduction effect of the CT image can be achieved.
- a second embodiment will be described.
- the arithmetic device 5 performs reconstruction processing of a reconstructed image by a successive approximation method that includes the detector output weight and the penalty term in the evaluation function.
- equation (4) as in equation (3), an update equation in the successive approximation method can be derived from equation (4).
- Arithmetic device 5 a penalty term weight beta m of the detector for each output weight d i and subset S m is calculated as in the first embodiment, it substitutes the derived updated formula, successive approximation Thus, the reconstructed image is reconstructed.
- the penalty term weight ⁇ m used for the successive approximation reconstruction process is set to a constant value in the true subset S m as in the first embodiment.
- the noise reduction effect of the CT image can be achieved as in the conventional case.
- ⁇ Third embodiment> A third embodiment will be described.
- the arithmetic device 5 performs the projection data correction process and the reconstructed image reconstruction process by the successive approximation method including the detector output weight and the penalty term in the evaluation function.
- the third embodiment is a combination of the first embodiment and the second embodiment. That is, the arithmetic device 5, as in the first embodiment, the formula (3) derives the update equation in the successive approximation from the detector output weights d i and calculated in the same manner as in the first embodiment The penalty term weight ⁇ m for each true subset S m is substituted into the derived update formula, and the projection data is corrected by the successive approximation method.
- the arithmetic device 5 as in the second embodiment, the formula (4) to derive the update equation in the successive approximation from the detector output weights d i and calculated in the same manner as in the first embodiment
- the penalty term weight ⁇ m for each true subset S m is substituted into the derived update formula, and the reconstructed image is reconstructed by the successive approximation method.
- the penalty term weight ⁇ m used for the successive approximation projection data correction process and the successive approximation reconstruction process is set to a constant value within the true subset S m , so that In the approximate projection data correction process and the successive approximation reconstruction process, the noise reduction effect of the CT image can be achieved as in the conventional case.
- the true subset S m is calculated between the regions by calculating the penalty term weight ⁇ m for each true subset S m based on the detector output weights d i reflecting the subject information and the projection values. Variations in the noise reduction effect can be suppressed.
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Radiology & Medical Imaging (AREA)
- General Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- General Physics & Mathematics (AREA)
- Heart & Thoracic Surgery (AREA)
- Public Health (AREA)
- Optics & Photonics (AREA)
- Biomedical Technology (AREA)
- High Energy & Nuclear Physics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Algebra (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Pulmonology (AREA)
- Quality & Reliability (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
Description
(1)投影データの投影値を変数として評価関数を構築する手法
(2)画像データの画素値を変数として評価関数を構築する手法
(1)の手法は、画像再構成処理の前処理である投影データの補正処理などに適用される。以下では、(1)の逐次近似法が適用される投影データの補正処理を「逐次近似投影データ補正処理」と呼ぶ。
κiは、通し番号iによって特定される検出素子と近接する検出素子の集合である。
すなわち、検出器12によって検出されるX線は、電流に変換され、プリアンプ22によって増幅され、A/Dコンバータ23によってデジタルデータに変換され、LOG変換され、キャリブレーションが行われて実投影データとして演算装置5に入力される。
)において収集される。各々のX線管位置における実投影データの取得単位が、「ビュー」である。
説明を分かり易くする為、ここでは、検出器12のチャネルの次元を考えず、ビュー及び列44の2つの次元に限定して考える。45は、スキャン方向、つまり寝台4を移動させる方向と正反対の方向を示している。
混乱を避ける為に、以下では、演算装置5を本発明の医用画像処理装置として説明する。
図5~図8を参照しながら、第1の実施の形態について説明する。第1の実施の形態では、本発明を逐次近似投影データ補正処理に適用する場合について説明する。第1の実施の形態では、演算装置5が、検出器出力重み(検出器12の出力に応じた重み)及び罰則項を評価関数に含む逐次近似法によって、投影データの補正処理を行う。
(1)検出器出力重みを定数とし、逐次近似法を任意の罰則項重みによって実行した場合、被写体及び部位によらず、ほぼ同一のノイズ低減率を得られることが経験的に明らかである。
(2)逐次近似法による投影値(画素値)の修正量とノイズ低減率との間には、高い相関がある。
第2の実施の形態について説明する。第2の実施の形態では、本発明を逐次近似再構成処理に適用する場合について説明する。つまり、第2の実施の形態では、演算装置5が、検出器出力重み及び罰則項を評価関数に含む逐次近似法によって、再構成画像の再構成処理を行う。
第3の実施の形態について説明する。第3の実施の形態では、本発明を逐次近似投影データ補正処理及び逐次近似再構成処理の両方に適用する場合について説明する。つまり、第3の実施の形態では、演算装置5が、検出器出力重み及び罰則項を評価関数に含む逐次近似法によって、投影データの補正処理及び再構成画像の再構成処理を行う。
Claims (8)
- 検出器の出力に応じた重みである検出器出力重み、及び罰則項を評価関数に含む逐次近似法によって、投影データの補正処理及び/又は再構成画像の再構成処理を行う医用画像処理装置であって、
前記検出器のチャネル、前記検出器の列、及び前記投影データの取得単位であるビューの組み合わせを一意に識別する情報を集合要素とする全体集合から、撮影条件及び再構成条件に基づいて1又は複数の真部分集合を決定する真部分集合決定部と、
前記真部分集合毎に、前記真部分集合内に含まれる前記集合要素に対応する前記検出器出力重みに基づいて、前記罰則項に係る重みである罰則項重みを算出する罰則項重み算出部と、
前記真部分集合毎の前記罰則項重みを用いて前記逐次近似法を実行する逐次近似法実行部と、
を備える医用画像処理装置。 - 前記真部分集合決定部は、らせんスキャンの場合、前記逐次近似法における逆投影処理の計算単位として定められる前記ビューの数である逆投影ビュー数に基づいて、前記真部分集合に含まれる前記集合要素の数を決定する
請求項1に記載の医用画像処理装置。 - 前記真部分集合決定部は、
寝台送り速度及び前記再構成画像の大きさごとに、前記逐次近似法における逆投影処理の位相幅である逆投影位相幅を予め記憶部に記憶し、
前記記憶部から、前記撮影条件として設定される寝台送り速度、及び前記再構成条件として設定される前記再構成画像の大きさに対応する前記逆投影位相幅を取得し、
前記撮影条件として設定される周回当たりの撮影ビュー数と、前記記憶部から取得される前記逆投影位相幅とを乗算した値を前記逆投影ビュー数とする
請求項2に記載の医用画像処理装置。 - 前記罰則項重み算出部は、
ノイズ低減率ごとに、基準ファントムに対して前記ノイズ低減率と同程度のノイズ低減が達成される前記罰則項重みである罰則項重み基準値を予め記憶部に記憶し、
前記記憶部から、入力される前記ノイズ低減率に対応する前記罰則項重み基準値を取得し、
前記真部分集合毎に、前記真部分集合内に含まれる前記集合要素に対応する前記検出器出力重みに基づいて代表値を算出し、
前記真部分集合毎の前記代表値と、前記記憶部から取得される前記罰則項重み基準値とを乗算した値を、前記真部分集合毎の前記罰則項重みとする
請求項1に記載の医用画像処理装置。 - 前記罰則項重み算出部は、前記真部分集合毎に、前記真部分集合内に含まれる前記集合要素に対応する前記検出器出力重みの平均値及び中央値、並びに、前記検出器出力重みを階級とするヒストグラムにおいて全体を所定の割合によって分割する階級の3つの値の中でいずれか1つを、前記真部分集合毎の前記代表値とする
請求項4に記載の医用画像処理装置。 - 前記罰則項重み算出部は、前記真部分集合毎の前記罰則項重みが変数であって、前記逐次近似法を実行した場合の前記投影データの修正量を算出する修正量算出関数に対して、前記真部分集合内に含まれる前記集合要素に対応する前記検出器出力重み及び前記投影データを代入して算出される前記修正量算出関数の値と修正量基準値との誤差が最小となるように、前記真部分集合毎の前記罰則項重みを決定する
請求項1に記載の医用画像処理装置。 - 前記罰則項重み算出部は、
ノイズ低減率ごとに、基準ファントムに対して前記ノイズ低減率と同程度のノイズ低減効果が達成される前記罰則項重みである罰則項重み基準値を予め記憶部に記憶し、
前記記憶部から、入力される前記ノイズ低減率に対応する前記罰則項重み基準値を取得し、
前記検出器出力重みを定数とし、前記記憶部から取得される前記罰則項重み基準値を用いて前記逐次近似法を実行した場合の前記投影データの修正量を算出し、前記修正量基準値とする
請求項6に記載の医用画像処理装置。 - 検出器の出力に応じた重みである検出器出力重み、及び罰則項を評価関数に含む逐次近似法によって、投影データの補正処理及び/又は再構成画像の再構成処理を行う医用画像処理方法であって、
前記検出器のチャネル、前記検出器の列、及び前記投影データの取得単位であるビューの組み合わせを一意に識別する情報を集合要素とする全体集合から、撮影条件及び再構成条件に基づいて1又は複数の真部分集合を決定するステップと、
前記真部分集合毎に、前記真部分集合内に含まれる前記集合要素に対応する前記検出器出力重みに基づいて、前記罰則項に係る重みである罰則項重みを算出するステップと、
前記真部分集合毎の前記罰則項重みを用いて前記逐次近似法を実行するステップと、
を含む医用画像処理方法。
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/112,276 US9123098B2 (en) | 2011-04-28 | 2012-04-04 | Medical image processing device and medical image processing method, applying weighted penalty term in iterative approximation |
CN201280020678.5A CN103501702B (zh) | 2011-04-28 | 2012-04-04 | 医用图像处理装置、医用图像处理方法 |
JP2013511984A JP5978429B2 (ja) | 2011-04-28 | 2012-04-04 | 医用画像処理装置、医用画像処理方法 |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2011-100571 | 2011-04-28 | ||
JP2011100571 | 2011-04-28 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2012147471A1 true WO2012147471A1 (ja) | 2012-11-01 |
Family
ID=47071996
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/JP2012/059136 WO2012147471A1 (ja) | 2011-04-28 | 2012-04-04 | 医用画像処理装置、医用画像処理方法 |
Country Status (4)
Country | Link |
---|---|
US (1) | US9123098B2 (ja) |
JP (1) | JP5978429B2 (ja) |
CN (1) | CN103501702B (ja) |
WO (1) | WO2012147471A1 (ja) |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2014128576A (ja) * | 2012-11-30 | 2014-07-10 | Canon Inc | 画像処理装置、画像処理方法、及びプログラム |
WO2014123041A1 (ja) * | 2013-02-05 | 2014-08-14 | 株式会社 日立メディコ | X線ct装置及び画像再構成方法 |
WO2015108097A1 (ja) * | 2014-01-20 | 2015-07-23 | 株式会社 日立メディコ | X線ct装置、画像処理装置、及び画像再構成方法 |
WO2015137011A1 (ja) * | 2014-03-14 | 2015-09-17 | 株式会社日立メディコ | X線ct装置、及び処理装置 |
CN105188543A (zh) * | 2013-04-08 | 2015-12-23 | 株式会社日立医疗器械 | X射线ct装置、重构运算装置以及重构运算方法 |
CN105451658A (zh) * | 2013-08-08 | 2016-03-30 | 株式会社日立医疗器械 | X射线ct装置以及校正处理装置 |
JP2016188786A (ja) * | 2015-03-30 | 2016-11-04 | 三菱電機株式会社 | 窓関数決定装置、パルス圧縮装置、レーダ信号解析装置、レーダ装置、窓関数決定方法およびプログラム |
WO2016199716A1 (ja) * | 2015-06-12 | 2016-12-15 | 株式会社日立製作所 | X線ct装置および逐次修正パラメータ決定方法 |
CN110070588A (zh) * | 2019-04-24 | 2019-07-30 | 上海联影医疗科技有限公司 | Pet图像重建方法、系统、可读存储介质和设备 |
Families Citing this family (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9123098B2 (en) * | 2011-04-28 | 2015-09-01 | Hitachi Medical Corporation | Medical image processing device and medical image processing method, applying weighted penalty term in iterative approximation |
WO2013008702A1 (ja) * | 2011-07-08 | 2013-01-17 | 株式会社 日立メディコ | 画像再構成装置及び画像再構成方法 |
IN2014DN05824A (ja) * | 2012-04-24 | 2015-05-15 | Hitachi Medical Corp | |
JP6218334B2 (ja) * | 2012-11-30 | 2017-10-25 | 株式会社日立製作所 | X線ct装置及びその断層画像撮影方法 |
CN104318536B (zh) | 2014-10-21 | 2018-03-20 | 沈阳东软医疗系统有限公司 | Ct图像的校正方法及装置 |
JPWO2016132880A1 (ja) * | 2015-02-16 | 2017-11-30 | 株式会社日立製作所 | 演算装置、x線ct装置、及び画像再構成方法 |
JP2017122705A (ja) * | 2016-01-06 | 2017-07-13 | 三菱電機株式会社 | 算出方法、判定方法、選別方法および選別装置 |
CN106994021B (zh) * | 2016-01-22 | 2022-10-14 | 通用电气公司 | 一种计算ct影像上的噪声的方法及装置 |
CN108780052B (zh) * | 2016-03-11 | 2020-11-17 | 株式会社岛津制作所 | 图像重构处理方法、图像重构处理程序以及安装有该程序的断层摄影装置 |
US10650621B1 (en) | 2016-09-13 | 2020-05-12 | Iocurrents, Inc. | Interfacing with a vehicular controller area network |
US10789738B2 (en) * | 2017-11-03 | 2020-09-29 | The University Of Chicago | Method and apparatus to reduce artifacts in a computed-tomography (CT) image by iterative reconstruction (IR) using a cost function with a de-emphasis operator |
JP7282487B2 (ja) * | 2018-06-07 | 2023-05-29 | キヤノンメディカルシステムズ株式会社 | 医用画像診断装置 |
JP7077208B2 (ja) | 2018-11-12 | 2022-05-30 | 富士フイルムヘルスケア株式会社 | 画像再構成装置および画像再構成方法 |
US10679385B1 (en) | 2018-12-17 | 2020-06-09 | General Electric Company | System and method for statistical iterative reconstruction and material decomposition |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS6017568A (ja) * | 1983-07-11 | 1985-01-29 | Hitachi Ltd | 画像処理方法および装置 |
JP2009540966A (ja) * | 2006-06-22 | 2009-11-26 | ゼネラル・エレクトリック・カンパニイ | 画像の分解能を高めるシステム及び方法 |
WO2010062956A1 (en) * | 2008-11-26 | 2010-06-03 | Wisconsin Alumni Research Foundation | Method for prior image constrained image reconstruction in cardiac cone beam computed tomography |
JP2010136958A (ja) * | 2008-12-13 | 2010-06-24 | Univ Of Tokushima | Ct装置、ct装置における画像再構成方法、及び電子回路部品 |
Family Cites Families (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5909476A (en) * | 1997-09-22 | 1999-06-01 | University Of Iowa Research Foundation | Iterative process for reconstructing cone-beam tomographic images |
US8204173B2 (en) * | 2003-04-25 | 2012-06-19 | Rapiscan Systems, Inc. | System and method for image reconstruction by using multi-sheet surface rebinning |
WO2005077278A1 (ja) * | 2004-02-16 | 2005-08-25 | Hitachi Medical Corporation | 断層撮影像の再構成方法及び断層撮影装置 |
EP1861825B1 (en) * | 2005-03-17 | 2008-10-08 | Philips Intellectual Property & Standards GmbH | Method and device for the iterative reconstruction of cardiac images |
CN102105106B (zh) * | 2008-08-07 | 2013-12-25 | 株式会社日立医疗器械 | X射线ct图像形成方法和应用了该方法的x射线ct装置 |
US20110052023A1 (en) * | 2009-08-28 | 2011-03-03 | International Business Machines Corporation | Reconstruction of Images Using Sparse Representation |
CN101901472B (zh) * | 2010-07-07 | 2012-12-19 | 清华大学 | 一种基于矩阵秩最小化的非刚性鲁棒批量图像对齐方法 |
CN103153192B (zh) * | 2010-10-14 | 2015-09-23 | 株式会社日立医疗器械 | X射线ct装置以及图像再构成方法 |
US9123098B2 (en) * | 2011-04-28 | 2015-09-01 | Hitachi Medical Corporation | Medical image processing device and medical image processing method, applying weighted penalty term in iterative approximation |
-
2012
- 2012-04-04 US US14/112,276 patent/US9123098B2/en active Active
- 2012-04-04 JP JP2013511984A patent/JP5978429B2/ja active Active
- 2012-04-04 CN CN201280020678.5A patent/CN103501702B/zh active Active
- 2012-04-04 WO PCT/JP2012/059136 patent/WO2012147471A1/ja active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS6017568A (ja) * | 1983-07-11 | 1985-01-29 | Hitachi Ltd | 画像処理方法および装置 |
JP2009540966A (ja) * | 2006-06-22 | 2009-11-26 | ゼネラル・エレクトリック・カンパニイ | 画像の分解能を高めるシステム及び方法 |
WO2010062956A1 (en) * | 2008-11-26 | 2010-06-03 | Wisconsin Alumni Research Foundation | Method for prior image constrained image reconstruction in cardiac cone beam computed tomography |
JP2010136958A (ja) * | 2008-12-13 | 2010-06-24 | Univ Of Tokushima | Ct装置、ct装置における画像再構成方法、及び電子回路部品 |
Non-Patent Citations (4)
Title |
---|
H.R.SHI ET AL.: "Quadratic Regularization Design for 2-D CT", IEEE TRANS.MED.IMG., vol. 28, no. 5, May 2009 (2009-05-01), pages 645 - 656 * |
J.WANG ET AL.: "Multiscale Penalized Weighted Least-Squares Sinogram Restoration for Low-Dose X-Ray Computed Tomography", CONF.PROC.IEEE ENG.MED.BIOL.SOC., vol. 55, no. 3, March 2008 (2008-03-01), pages 1022 - 1031 * |
TAIGA GOTO ET AL.: "Chikuji Kinji Saikosei Algorithm no Kaihatsu", JAPANESE SOCIETY OF RADIOLOGICAL TECHNOLOGY SOKAI GAKUJUTSU TAIKAI YOKOSHU, vol. 67, 25 February 2011 (2011-02-25), pages 184 * |
Z.TIAN ET AL.: "Low-dose CT reconstruction via edge-preserving total variation regularization.", PHYS.MED.BIOL., vol. 56, no. 18, September 2011 (2011-09-01), pages 5949 - 5967 * |
Cited By (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2014128576A (ja) * | 2012-11-30 | 2014-07-10 | Canon Inc | 画像処理装置、画像処理方法、及びプログラム |
JPWO2014123041A1 (ja) * | 2013-02-05 | 2017-02-02 | 株式会社日立製作所 | X線ct装置及び画像再構成方法 |
WO2014123041A1 (ja) * | 2013-02-05 | 2014-08-14 | 株式会社 日立メディコ | X線ct装置及び画像再構成方法 |
US9715745B2 (en) | 2013-02-05 | 2017-07-25 | Hitachi, Ltd. | X-ray CT apparatus and image reconstruction method |
CN104902818A (zh) * | 2013-02-05 | 2015-09-09 | 株式会社日立医疗器械 | X射线ct装置以及图像重构方法 |
CN105188543A (zh) * | 2013-04-08 | 2015-12-23 | 株式会社日立医疗器械 | X射线ct装置、重构运算装置以及重构运算方法 |
CN105451658A (zh) * | 2013-08-08 | 2016-03-30 | 株式会社日立医疗器械 | X射线ct装置以及校正处理装置 |
JPWO2015108097A1 (ja) * | 2014-01-20 | 2017-03-23 | 株式会社日立製作所 | X線ct装置、画像処理装置、及び画像再構成方法 |
WO2015108097A1 (ja) * | 2014-01-20 | 2015-07-23 | 株式会社 日立メディコ | X線ct装置、画像処理装置、及び画像再構成方法 |
US9974495B2 (en) | 2014-01-20 | 2018-05-22 | Hitachi, Ltd. | X-ray CT apparatus, image processing device, and image reconstruction method |
WO2015137011A1 (ja) * | 2014-03-14 | 2015-09-17 | 株式会社日立メディコ | X線ct装置、及び処理装置 |
JPWO2015137011A1 (ja) * | 2014-03-14 | 2017-04-06 | 株式会社日立製作所 | X線ct装置、及び処理装置 |
US20170119335A1 (en) * | 2014-03-14 | 2017-05-04 | Hitachi, Ltd. | X-ray ct device and processing device |
US10368824B2 (en) | 2014-03-14 | 2019-08-06 | Hitachi, Ltd. | X-ray CT device and processing device |
JP2016188786A (ja) * | 2015-03-30 | 2016-11-04 | 三菱電機株式会社 | 窓関数決定装置、パルス圧縮装置、レーダ信号解析装置、レーダ装置、窓関数決定方法およびプログラム |
WO2016199716A1 (ja) * | 2015-06-12 | 2016-12-15 | 株式会社日立製作所 | X線ct装置および逐次修正パラメータ決定方法 |
JPWO2016199716A1 (ja) * | 2015-06-12 | 2018-03-01 | 株式会社日立製作所 | X線ct装置および逐次修正パラメータ決定方法 |
US10210633B2 (en) | 2015-06-12 | 2019-02-19 | Hitachi, Ltd. | X-ray CT device and sequential correction parameter determination method |
CN110070588A (zh) * | 2019-04-24 | 2019-07-30 | 上海联影医疗科技有限公司 | Pet图像重建方法、系统、可读存储介质和设备 |
CN110070588B (zh) * | 2019-04-24 | 2023-01-31 | 上海联影医疗科技股份有限公司 | Pet图像重建方法、系统、可读存储介质和设备 |
Also Published As
Publication number | Publication date |
---|---|
US9123098B2 (en) | 2015-09-01 |
US20140226887A1 (en) | 2014-08-14 |
CN103501702A (zh) | 2014-01-08 |
JPWO2012147471A1 (ja) | 2014-07-28 |
JP5978429B2 (ja) | 2016-08-24 |
CN103501702B (zh) | 2015-09-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP5978429B2 (ja) | 医用画像処理装置、医用画像処理方法 | |
JP6937157B2 (ja) | 放射線画像診断装置及び医用画像処理装置 | |
Liu et al. | Total variation-stokes strategy for sparse-view X-ray CT image reconstruction | |
JP5530637B2 (ja) | 画像再構成の方法及びシステム | |
JP5828841B2 (ja) | X線ct装置及び画像再構成方法 | |
JP6492005B2 (ja) | X線ct装置、再構成演算装置、及び再構成演算方法 | |
JP6470837B2 (ja) | X線ct装置および逐次修正パラメータ決定方法 | |
JP5968316B2 (ja) | 画像再構成装置及び画像再構成方法 | |
US8885903B2 (en) | Method and apparatus for statistical iterative reconstruction | |
JP6215449B2 (ja) | X線ct装置、及び処理装置 | |
WO2014041889A1 (ja) | X線ct装置およびx線ct画像の処理方法 | |
US10813616B2 (en) | Variance reduction for monte carlo-based scatter estimation | |
JP2016152916A (ja) | X線コンピュータ断層撮像装置及び医用画像処理装置 | |
JPWO2014123041A1 (ja) | X線ct装置及び画像再構成方法 | |
JP6124868B2 (ja) | 画像処理装置及び画像処理方法 | |
JP5637768B2 (ja) | コンピュータ断層撮影画像の生成方法およびコンピュータ断層撮影装置 | |
JP2018110867A (ja) | 医用画像生成装置及び医用画像生成方法 | |
JP2015231528A (ja) | X線コンピュータ断層撮像装置及び医用画像処理装置 | |
WO2016132880A1 (ja) | 演算装置、x線ct装置、及び画像再構成方法 | |
US20220375038A1 (en) | Systems and methods for computed tomography image denoising with a bias-reducing loss function | |
US9508164B2 (en) | Fast iterative image reconstruction method for 3D computed tomography | |
US11353411B2 (en) | Methods and systems for multi-material decomposition |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 12776071 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2013511984 Country of ref document: JP Kind code of ref document: A |
|
WWE | Wipo information: entry into national phase |
Ref document number: 14112276 Country of ref document: US |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 12776071 Country of ref document: EP Kind code of ref document: A1 |