WO2016002084A1 - 画像再構成処理方法 - Google Patents
画像再構成処理方法 Download PDFInfo
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- WO2016002084A1 WO2016002084A1 PCT/JP2014/067976 JP2014067976W WO2016002084A1 WO 2016002084 A1 WO2016002084 A1 WO 2016002084A1 JP 2014067976 W JP2014067976 W JP 2014067976W WO 2016002084 A1 WO2016002084 A1 WO 2016002084A1
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- 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
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- 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
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/032—Transmission computed tomography [CT]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/037—Emission tomography
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/42—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with arrangements for detecting radiation specially adapted for radiation diagnosis
- A61B6/4266—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with arrangements for detecting radiation specially adapted for radiation diagnosis characterised by using a plurality of detector units
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/502—Clinical applications involving diagnosis of breast, i.e. mammography
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
- G01T1/16—Measuring radiation intensity
- G01T1/17—Circuit arrangements not adapted to a particular type of detector
- G01T1/172—Circuit arrangements not adapted to a particular type of detector with coincidence circuit arrangements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
- G01T1/29—Measurement performed on radiation beams, e.g. position or section of the beam; Measurement of spatial distribution of radiation
- G01T1/2914—Measurement of spatial distribution of radiation
- G01T1/2985—In depth localisation, e.g. using positron emitters; Tomographic imaging (longitudinal and transverse section imaging; apparatus for radiation diagnosis sequentially in different planes, steroscopic radiation diagnosis)
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- 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
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- 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]
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- 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
- G06T2207/10104—Positron emission tomography [PET]
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- 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
- G06T2207/10108—Single photon emission computed tomography [SPECT]
Definitions
- the present invention performs image reconstruction processing for reconstructing the physical quantity distribution of the subject related to the generation factor of the measurement data set as a multi-dimensional digital image from the measurement data set of the subject obtained by the radiation detection apparatus.
- the present invention relates to a configuration processing method.
- This image reconstruction processing method is used for an image reconstruction technique of a general tomographic imaging apparatus (CT (Computed Tomography) apparatus) having a radiation detection apparatus.
- CT Computer Tomography
- Examples of the tomographic imaging apparatus having a radiation detection apparatus include a nuclear medicine diagnostic apparatus and an X-ray computed tomography apparatus (X-ray CT apparatus).
- X-ray CT apparatus X-ray computed tomography apparatus
- the physical quantity distribution of the subject related to the generation factor of the measurement data set is reconstructed as a multi-dimensional digital image (such as a tomographic image or a three-dimensional reconstructed image) from the measurement data set of the subject obtained by the radiation detection apparatus. Perform reconfiguration processing.
- a nuclear medicine diagnostic apparatus there are a positron emission tomography apparatus (PET (Positron Emission Tomography) apparatus) and a single photon emission tomography apparatus (SPECT (Single Photon Emission CT) apparatus).
- PET Positron Emission Tomography
- SPECT Single Photon Emission CT
- the PET device detects only multiple rays ( ⁇ rays) generated by the annihilation of positrons (positrons) and detects the radiation ( ⁇ rays) with multiple detectors simultaneously (that is, only when they are counted simultaneously).
- a detection signal is recorded, and a tomographic image of the subject is created by performing reconstruction processing on the detection signal (detection signals for a number of ⁇ rays).
- the SPECT apparatus detects a single radiation ( ⁇ -ray) and performs a reconstruction process to create a tomographic image of the subject.
- ML reconstruction method An emission CT image based on maximum likelihood estimation (ML: Maximum Likelihood) of Poisson distribution is used.
- a reconstruction processing method (ML reconstruction method) has been proposed (see, for example, Non-Patent Document 1).
- image reconstruction methods used in today's PET devices and SPECT devices although there are differences depending on the device manufacturer, the mathematical framework (the basis theory) of almost all methods is described in Non-Patent Document 1.
- ML reconstruction method is described in Non-Patent Document 1.
- Non-Patent Document 1 is a very famous academic paper on the ML reconstruction method, and almost all of today's image reconstruction methods are described in Non-Patent Document 1. It can be said that it is a derivation method of the method.
- the measurement data set (that is, measurement data) of the subject obtained by the radiation detection apparatus includes a statistical error, and the statistical error distribution (error distribution) follows a Poisson distribution.
- the ML reconstruction method described in Non-Patent Document 1 is a method for obtaining a solution (image) that maximizes the likelihood function derived from the Poisson property of measurement data as a likely radioactivity distribution image (physical quantity distribution).
- the likelihood function is generally called a “data function”.
- the likelihood function is maximized by using an iterative calculation algorithm (sequential approximation method).
- the likelihood function of the Poisson distribution is L (x)
- the likelihood function L (x) of the Poisson distribution is expressed by the following equation (1).
- x is a reconstructed image vector (where the pixel value is non-negative)
- I is the number of measurement data points
- a i is the sensitivity distribution function of the i-th measurement data point (i-th row of system matrix A)
- Y i is the prompt simultaneous clock value (count value) at the i-th measurement data point
- r i is a count other than the prompt simultaneous clock value (count value) at the i-th measurement data point ( It is an estimated value of a count value (count value) of accidental coincidence count and scattering coincidence count).
- Non-Patent Document 1 An image reconstruction method (ML reconstruction method) described in Non-Patent Document 1 and an image reconstruction method derived from the method have been established as mathematical theories. However, when these methods are applied directly to actual measurement data, (I) streak artifacts (streak artifacts) that are linear false images (linear noise) occur in the image; (Ii) the spatial resolution of the image is lower than the value predicted from the device parameters (mainly the size of the radiation detection element); Such a problem may occur. This is a problem (problem) that arises in a nuclear medicine diagnostic apparatus (emission CT apparatus), but even when it is extended to an X-ray computed tomography apparatus (X-ray CT apparatus), artifacts and degradation of the spatial resolution of the image may occur. It is thought to occur.
- X-ray computed tomography apparatus X-ray computed tomography apparatus
- the present invention has been made in view of such circumstances, and an object thereof is to provide an image reconstruction processing method capable of suppressing artifacts generated in an image or improving the spatial resolution of the image. .
- the present invention has the following configuration. That is, according to the image reconstruction processing method of the present invention, the physical quantity distribution of the subject related to the generation factor of the measurement data set is obtained as a multi-dimensional digital image from the measurement data set of the subject obtained by the radiation detection apparatus.
- An image reconstruction processing method for performing reconstruction processing for reconstruction wherein the first multivariable function having the digital image as an unknown is configured based on (1) an error distribution of element data constituting the measurement data set A data function represented by the sum of the partial functions generated, or (2) a data function represented by the sum of the partial functions configured based on the error distribution of the element data constituting the measurement data set, and the physical quantity distribution And a second multivariable function configured based on the prior information, and the image reconstruction processing method applies to the reconstructed image of the element data corresponding to the partial function.
- Weighting the partial function with a projection weighting factor, the weighted data function, or the first function comprising the sum of the weighted data function or the second multivariable function configured based on prior information of the physical quantity distribution A reconstruction processing step for performing the reconstruction processing based on optimization calculation of a variable function is provided.
- weighting is applied in the conventional reconstruction processing step. That is, weighting is performed when reconstruction processing is performed based on optimization calculation of a multivariable function having a digital image as an unknown, including a data function that generalizes the likelihood function of Poisson distribution of Non-Patent Document 1.
- a multivariable function having an unknown digital image is a “first multivariable function”
- the first multivariable function is expressed by the following (1) or (2). That is, the first multivariable function is (1) data represented by the sum of partial functions constructed based on the error distribution of element data constituting the measurement data set (of the subject obtained by the radiation detection apparatus). It is a function.
- the first multivariable function is a multivariable function configured based on (2) the data function described in (1) and the prior information on the physical quantity distribution (of the subject related to the generation factor of the measurement data set).
- the multivariable function configured based on the prior information of the physical quantity distribution is referred to as a “second multivariable function” in order to distinguish it from the first multivariable function.
- the partial function is weighted with a back projection weight coefficient for the reconstructed image of the element data corresponding to the partial function described above.
- This weighting coefficient is a non-negative coefficient (also called “influence adjustment coefficient”) that adjusts the degree of influence of the element data on the reconstructed image.
- the radiation detection apparatus includes a positron emission tomography apparatus (PET apparatus), a single photon emission tomography apparatus (SPECT apparatus), and an X-ray computed tomography apparatus (X-ray CT apparatus).
- PET apparatus positron emission tomography apparatus
- SPECT apparatus single photon emission tomography apparatus
- X-ray CT apparatus X-ray computed tomography apparatus
- the radiation detection apparatus is any one of a PET apparatus, a SPECT apparatus, and an X-ray CT apparatus
- the weight coefficient is not set as in the prior art. This can be attributed to the fact that reconstruction processing is performed using a function. Therefore, by setting the weighting factor based on the directivity of the linear noise generated in the reconstructed image when the weighting factor is a constant that does not depend on element data, linear noise (streak artifact) can be suppressed. .
- a weighting factor for element data along the running direction of linear noise (however, a weighting factor greater than 0) is used as a weighting factor for element data not along the running direction of linear noise (greater than 0).
- a weighting factor for element data not along the running direction of linear noise (greater than 0).
- linear noise is more likely to occur when a detector unit that is partially open is used rather than a full ring type detector unit.
- a detector unit that is partially opened radiation that passes through the opening (missing portion) is not detected. Therefore, noise having a strong spatial correlation is generated in the reconstructed image due to the loss of a part of the projection data.
- linear noise when a plurality of detector units separated from each other are used, it is considered that linear noise (streak artifact) occurs along a linear direction connecting detection elements in the same detector unit.
- the radiation detectors constituting the radiation detection apparatus are composed of a plurality of detector units separated from each other, in a linear direction connecting the detection elements in the same detector unit.
- the weighting factor for the element data along is smaller than the weighting factor (the weighting factor greater than 0) for the element data along the linear direction connecting the detection elements in different detector units.
- the radiation detection apparatus is either a positron emission tomography apparatus (PET apparatus) or a single photon emission tomography apparatus (SPECT apparatus).
- PET apparatus positron emission tomography apparatus
- SPECT apparatus single photon emission tomography apparatus
- the measurement data set (of the subject obtained by the radiation detection apparatus) described above is one of sinogram data, histogram data, and list mode data. is there.
- the radiation detection device is either a PET device or a SPECT device and the measurement data set is any of sinogram data, histogram data, and list mode data.
- the radiation detector constituting the radiation detection apparatus is configured to measure radiation detection depth position information, that is, each detection element is configured to be stacked in the radiation depth direction.
- a DOI detector When a DOI detector is used, the following phenomenon occurs. That is, the width of the sensitivity distribution function of the pair of detection elements (shallow DOI layer) closer to the measurement object and the pair of detection elements (deep DOI layer) on the far side becomes wider. The reliability is lower than the former. Therefore, the spatial resolution of the image can be improved by setting the weighting coefficient depending on the detected depth position information corresponding to the measurement data set.
- the radiation detector is configured to measure N-stage detection depth position information (that is, a DOI detector),
- the detection elements constituting the radiation detector have g, h (number of detection depth stage numbers of the two detection elements for which the coincidence count is measured so that the numbers increase from the shallow stage to the deep stage, respectively. 1 ⁇ g, h ⁇ N)
- the weighting coefficient is a two-dimensional function with the stage numbers g and h as discrete variables, and the two-dimensional function is for the other variable obtained when one variable is fixed.
- the one-dimensional function is a non-increasing function.
- the error distribution described above is either a Poisson distribution or a Gaussian distribution.
- the error distribution is a Poisson distribution, it is used for a nuclear medicine diagnostic apparatus (emission CT apparatus), and when the error distribution is a Gaussian distribution, it is used for an X-ray computed tomography apparatus (X-ray CT apparatus).
- weighting is performed when performing reconstruction processing based on optimization calculation of a multivariable function including a data function or the like with a digital image as an unknown number. Then, by weighting the partial function with a weighting factor of back projection for the reconstructed image of the element data corresponding to the partial function, artifacts generated in the image can be suppressed or the spatial resolution of the image can be improved.
- It is a schematic perspective view of a gamma ray detector. 3 is a flowchart of image reconstruction processing according to the first embodiment.
- (A) is a table showing an example of a likelihood weighting factor for a pair of DOI layers, and (b) is a weight for the other variable when one variable is fixed when the number of detection depth steps is a discrete variable. It is a graph of the non-increasing function of a coefficient. It is the side view and block diagram of the mammography apparatus which concern on a modification.
- (A), (b) is a schematic front view of the detector unit which concerns on the further modification.
- FIG. 1 is a schematic perspective view and a block diagram showing an embodiment of a gamma ray detector arrangement of a partial ring type PET apparatus according to each example
- FIG. 2 is a schematic perspective view of the gamma ray detector.
- PET apparatus positron emission tomography apparatus
- the partial ring type PET apparatus 1 includes detector units 2A and 2B.
- a plurality of ⁇ -ray detectors 3 are embedded in the detector units 2A and 2B.
- the partial ring type PET apparatus 1 corresponds to the radiation detection apparatus in the present invention, and also corresponds to the positron emission tomography apparatus in the present invention.
- the detector units 2A and 2B correspond to detector units, and the ⁇ -ray detector 3 corresponds to a radiation detector in the present invention.
- the detector units 2A and 2B are configured to be partially opened. That is, an open region (opening region) where the ⁇ -ray detector 3 does not exist exists between the detector units 2A and 2B.
- the detector units 2A and 2B have a vertically arranged geometry.
- the open region (opening region) is not limited to the YZ plane direction, and the ⁇ -ray detector 3 may be arranged so that the open region (opening region) exists along the XZ plane direction (in this case, detection is performed).
- the unit 2A, 2B has a left-right geometry. Moreover, you may arrange
- the partial ring type PET apparatus 1 includes a coincidence counting circuit 4 and an arithmetic circuit 5.
- a coincidence counting circuit 4 and an arithmetic circuit 5.
- PMT Photo Multiplier Tube
- a ⁇ -ray generated from a subject (not shown) to which a radiopharmaceutical has been administered is converted into light by a scintillator block 31 (see FIG. 2) of the ⁇ -ray detector 3, and the converted light is converted into a ⁇ -ray detector.
- 3 photomultiplier tube (PMT) 33 (see FIG. 2) is multiplied and converted into an electrical signal. The electric signal is sent to the coincidence counting circuit 4.
- the coincidence circuit 4 checks the position of the scintillator block 31 (see FIG. 2) and the incident timing of the ⁇ -ray, and sends it only when ⁇ -rays are simultaneously incident on the two scintillator blocks 31 on both sides of the subject. The determined electrical signal is determined as appropriate data.
- the coincidence counting circuit 4 rejects. That is, the coincidence counting circuit 4 detects that ⁇ rays are simultaneously observed (that is, coincidence counting) by the two ⁇ ray detectors 3 based on the above-described electrical signal.
- the electrical signal sent to the coincidence counting circuit 4 is sent to the arithmetic circuit 5.
- the arithmetic circuit 5 performs steps S1 to S6 (see FIG. 3) to be described later, and a measurement data set (here, measurement data of count values) of the subject (not shown) obtained by the partial ring type PET apparatus 1. That is, the physical quantity distribution (here, radioactivity distribution image) of the subject related to the generation factor of the measurement data set (measurement data) (here, generation of ⁇ -rays by administration of the radiopharmaceutical) from the count data in each embodiment.
- a reconstruction process is performed to reconstruct a multi-dimensional digital image (here, a reconstructed image). Specific functions of the arithmetic circuit 5 will be described later.
- the ⁇ -ray detector 3 includes a scintillator block 31, a light guide 32 optically coupled to the scintillator block 31, and photoelectrons optically coupled to the light guide 32.
- a multiplier (hereinafter simply abbreviated as “PMT”) 33 is provided.
- Each scintillator element constituting the scintillator block 31 converts ⁇ rays into light by emitting light with the incidence of ⁇ rays. By this conversion, the scintillator element detects ⁇ rays.
- Light emitted from the scintillator element is sufficiently diffused by the scintillator block 31 and input to the PMT 33 via the light guide 32.
- the PMT 33 multiplies the light converted by the scintillator block 31 and converts it into an electric signal.
- the electric signal is sent to the coincidence counting circuit 4 (see FIG. 1) as a pixel value.
- the ⁇ -ray detector 3 is a DOI detector composed of scintillator elements arranged three-dimensionally and composed of a plurality of layers in the depth direction.
- a four-layer DOI detector is illustrated, but the number of layers is not particularly limited as long as it is plural.
- the DOI detector is constructed by laminating the respective scintillator elements in the radiation depth direction, and the interaction depth (DOI: Depth of Interaction) direction and lateral direction (incident surface). Coordinate information with a direction parallel to the center of gravity).
- DOI Depth of Interaction
- the spatial resolution in the depth direction can be further improved. Therefore, the number of DOI detector layers is the number of scintillator element layers stacked in the depth direction.
- a weighting factor described later is set based on detection depth position information of a detection element (scintillator element) of the DOI detector.
- FIG. 3 is a flowchart of image reconstruction processing according to the first embodiment
- FIG. 4 is a schematic front view of a partial ring type detector unit
- FIG. 5 is a partial ring type detector unit and a weighting factor.
- FIG. 6 is a schematic diagram showing the relationship between streak artifacts and the setting of the weighting factor.
- the detector units 2A and 2B have a vertically arranged geometry, and as shown in FIG. 4, the relationship between the arrangement of the top plate and bed (both not shown) on which the subject M is placed.
- a subject M is arranged in proximity to the upper and upper detector units 2A.
- the ⁇ -ray detector 3 (not shown in FIG. 4) includes a plurality of detector units separated from each other (two detector units in FIGS. 1 and 4). 2A, 2B).
- the count data measured in the pair of detection elements (scintillator elements) in the same detector unit 2A the count data of the pairs of detection elements (scintillator elements) in the detector units 2A and 2B different from each other. Also, by applying a small weighting factor and performing steps S1 to S6 described later, streak artifacts can be suppressed.
- the element data constituting the measurement data set is the i-th measurement data point in the first embodiment including the second and third embodiments described later.
- the linear direction connecting the detection elements (scintillator elements) in the same detector unit 2A is LOR AA
- the element data (i-th measurement data point) along the linear direction LOR AA The weighting factor is w AA
- the linear direction connecting the detector elements (scintillator elements) in different detector units 2A and 2B is LOR AB
- element data (i-th measurement data point) along the linear direction LOR AB ) Is assumed to be w AB .
- the same detector weighting factors w AA for detecting element element data along a linear direction LOR AA connecting the (scintillator elements) (i th measurement data points) in unit 2A, different detector units 2A the weighting factor for sensing element element data along a linear direction LOR AB connecting the (scintillator elements) (i th measurement data points) in 2B is smaller than w AB.
- the linear direction LOR AB is not limited to one direction as long as the detection elements (scintillator elements) in different detector units 2A and 2B are connected to each other, and the detection elements in different detector units 2A and 2B ( Various linear directions corresponding to the linear direction connecting the scintillator elements become the linear direction LOR AB .
- the weighting factor w AA for the element data (i-th measurement data point) along the linear direction LOR AA connecting the detection elements (scintillator elements) in the same detector unit 2A is larger than 0,
- the value is set to a value smaller than 1 means that the weighting factor w AA on the linear direction LOR AA is set to a value larger than 0 and smaller than 1. . That is, even if it is parallel to the linear direction LOR AA (see circle in FIG. 5) as shown in FIG. 5, the detection elements in the detector units 2A and 2B are parallel to the linear direction LOR AA and are different from each other.
- the weighting coefficient w AB riding on the linear direction LOR AB is set to 1.
- the detector units are not limited to the detector units 2A and 2B that are separated from each other. Even if one detector unit is used, if it is a detector unit that is partially opened, it passes through the opening (missing portion). Gamma rays to be detected are not detected. Therefore, noise having a strong spatial correlation (for example, streak artifact) occurs in the reconstructed image due to the loss of a part of the projection data. Therefore, assuming that the streak artifact is SA as shown in FIG. 6, the streak artifact SA, which is a linear noise, is reconstructed using a partial function without setting a weighting coefficient as in the prior art. It is thought to be caused. Therefore, the weighting factor may be set based on the directivity of the streak artifact SA generated in the reconstructed image when the weighting factor is a constant that does not depend on the element data (i-th measurement data point).
- the solid line shown in FIG. 6 is the streak artifact SA
- the broken line shown in FIG. 6 is a straight line parallel to the streak artifact SA (see ⁇ in FIG. 6), and a straight line that does not correspond to the streak artifact SA ( ⁇ in FIG. 6). Reference).
- the weighting factor for the element data (i-th measurement data point) along the travel direction of the streak artifact SA is w SA
- w EX be the weighting factor for the measurement data point).
- the weight coefficient w SA for the element data (i-th measurement data point) along the travel direction of the streak artifact SA is used as the weight coefficient w SA for the element data (i-th measurement data point) not along the streak artifact SA.
- the weight coefficient w SA is set to a value larger than 0 and smaller than 1 (0 ⁇ w SA ⁇ 1).
- the weighting factor riding on the straight line is the streak artifact SA.
- a weighting factor w EX respect have not element data (i-th measured data points) that along, set to 1 for weighting coefficient w EX.
- the weighting coefficient set in this way is a non-negative coefficient (influence adjustment coefficient) that adjusts the degree of influence of element data (i-th measurement data point) on the reconstructed image, as is apparent from the above reason. Therefore, the weighting factor set in this way is also a weighting factor for back projection of the element data (i-th measurement data point) on the reconstructed image. Using this weighting coefficient, a partial function described later is weighted.
- measurement data When applied to a nuclear medicine diagnostic apparatus (emission CT apparatus) typified by a positron emission tomography apparatus (PET apparatus) as in the first embodiment, including later-described second and third embodiments, measurement data
- the error distribution of element data (i-th measurement data point) constituting the set (measurement data) is a Poisson distribution
- the data function is a likelihood function. Therefore, weighting is performed on the likelihood function of the Poisson distribution.
- the weighted likelihood function is referred to as a “weighted likelihood function”.
- the measurement data set (measurement data) is sinogram data or histogram data
- the weighted likelihood function of the Poisson distribution is L (x) as in the conventional case
- the weighted likelihood function L of the Poisson distribution is L (X) is represented by the following formula (2).
- x is a reconstructed image vector (where the pixel value is non-negative)
- I is the number of measurement data points
- a i is the sensitivity of the i-th measurement data point.
- Distribution function (i-th row vector of the system matrix A)
- y i is an immediate simultaneous clock value (count value) at the i-th measurement data point
- r i is an immediate coincidence at the i-th measurement data point This is an estimated value of a count value (count value) of a count (coincident coincidence count and scattered coincidence count) other than the clock value (count value).
- w i in the above equation (2) is a weighting coefficient (likelihood weighting coefficient) of the i-th measurement data point.
- each measurement data point is weighted by the weighting factor w i. It is a point. That is, the role of the likelihood weighting factor is to adjust the degree of influence of each measurement data point on the reconstructed image.
- a content of ⁇ in the right hand side of equation (2) "w i a i ⁇ x-w i y i log (w i a i + r i ) ”can be defined as a partial function constructed based on the error distribution of the element data (i-th measurement data point).
- the data function (likelihood function in each embodiment) is represented by the sum of partial functions configured based on the error distribution of the element data (i-th measurement data point). Further, when a multivariable function having an unknown number of digital images after reconstruction processing is referred to as a “first multivariable function”, the first multivariable function is a data function (likelihood function).
- the reconstruction process is performed based on the optimization calculation of the weighted likelihood function of the above-described weighted expression (2).
- the weighted likelihood function of the above equation (2) can be maximized by the following iterative calculation algorithm (sequential approximation method), and a digital image after reconstruction processing can be acquired. .
- the specific reconstruction process is as shown in FIG.
- Step S1 Setting of Weighting Factor
- Step S2 Setting of initial image A non-negative image is set as an initial image x (0) .
- a nuclear medicine diagnostic apparatus emission CT apparatus
- PET apparatus positron emission tomography apparatus
- 0 is set. Except for x (0) > 0.
- the initial image x (0) may be a reconstructed image having a uniform pixel value, for example.
- Step S3 Initialization of the counter variable of the number of iterations
- Step S4 Update Image Calculation
- the (k + 1) -th update image x (k + 1) is calculated using the following equation (3).
- J is the number of pixels of the reconstructed image.
- the likelihood weight coefficient w i is also applied to the following equation (3) in order to maximize the weighted likelihood function of the above equation (2).
- Step S5 Increment of counter variable of iteration count
- the counter variable k is incremented (k ⁇ k + 1).
- K ⁇ k + 1 means that (k + 1) on the right side is substituted for k on the left side.
- Step S6 k ⁇ N iter ? The number of iterations to terminate the iterative calculation algorithm and N iter, whether the counter variable k has reached the number of iterations N iter? Judge whether or not. Note that the user may set the number of iterations N iter in advance. If k ⁇ N iter , the process returns to step S4 to continue the iterative calculation algorithm. If k ⁇ N iter , the iterative calculation algorithm is completed and a series of calculations is terminated.
- the update image x (k + 1) obtained in this way is acquired as a reconstructed image.
- the user observes the updated image x (k + 1) obtained at each update, and the user interrupts the iterative calculation algorithm based on the observation result.
- the obtained update image x (k + 1) may be acquired as a reconstructed image.
- steps S1 to S6 correspond to the reconstruction processing step in the present invention.
- the image reconstruction processing method is characterized in that weighting is applied in the conventional reconstruction processing step. That is, weighting is performed when reconstruction processing is performed based on optimization calculation of a multivariable function having a digital image as an unknown, including a data function that generalizes the likelihood function of Poisson distribution of Non-Patent Document 1. .
- the first multivariable function is a radiation detector (partial ring type PET apparatus in each embodiment).
- Error distribution of element data (i-th measurement data point in each embodiment) constituting the measurement data set (here, measurement data of count values, that is, count data in each embodiment) obtained by 1) data functions (each embodiment is expressed by the sum of ( "w i a i ⁇ x-w i y i log (w i a i + r i) " in the above (2)) configured partial function on the basis of (Likelihood function). Then, the partial function is weighted with a weighting factor of back projection for the reconstructed image of the element data (i-th measurement data point) corresponding to the partial function described above.
- This weight coefficient is a non-negative coefficient (also referred to as “influence adjustment coefficient”) for adjusting the influence of the element data (i-th measurement data point) on the reconstructed image as described above. By weighting, artifacts generated in the image can be suppressed and the spatial resolution of the image can be improved.
- the radiation detection apparatus is a positron emission tomography apparatus (PET apparatus).
- PET apparatus positron emission tomography apparatus
- SPECT apparatus single photon emission tomography apparatus
- X-ray CT apparatus X-ray computed tomography apparatus
- linear noise is more likely to occur when a detector unit that is partially open is used rather than a full ring type detector unit.
- a detector unit that is partially opened radiation that passes through the opening (missing portion) is not detected. Therefore, noise having a strong spatial correlation is generated in the reconstructed image due to the loss of a part of the projection data.
- linear noise is generated along the linear direction connecting the detection elements in the same detector unit.
- the radiation detector ( ⁇ -ray detector 3 in each embodiment) constituting the radiation detection apparatus is changed into a plurality of detector units (two detectors) separated from each other.
- the linear noise smooth artifact
- the measurement data set (measurement data) of the subject M obtained by the radiation detection apparatus Is one of sinogram data, histogram data, and list mode data.
- the measurement data set (measurement data) is sinogram data or histogram data
- the weighted likelihood function L (x) of the Poisson distribution is expressed by the above equation (2) in the first embodiment.
- the error distribution of the element data (i-th measurement data point) constituting the measurement data set (measurement data) is a Poisson distribution.
- the error distribution is a Poisson distribution, it is used in a nuclear medicine diagnostic apparatus (emission CT apparatus) represented by a PET apparatus or the like.
- FIG. 7 is a schematic diagram showing a relationship between a four-layer (four-stage) DOI detector and a detection depth stage number
- FIG. 8A shows an example of a likelihood weighting coefficient for a pair of DOI layers
- FIG. 8B is a graph of a non-increasing function of the weighting coefficient for the other variable when one variable is fixed when the number of detection depth stage numbers is a discrete variable.
- FIG. 7 shows only the scintillator block 31 (see FIG. 2) for the ⁇ -ray detector 3 composed of the DOI detector for simplification of illustration, and the light guide 32 and the other configurations.
- the illustration of the PMT 33 (see FIG. 2 for both) is omitted, and only four scintillator blocks 31 in the horizontal direction are shown.
- the weighting factor is set based on the directivity of the linear noise generated in the reconstructed image when the weighting factor is a constant that does not depend on the element data (i-th measurement data point).
- the weighting coefficient is set based on the detection depth position information of the detection element (scintillator element) of the DOI detector.
- a PET apparatus DOI-PET apparatus having a DOI detector in which detection elements (scintillator elements) are stacked in multiple stages in the depth direction as shown in FIG.
- detection elements sintillator elements
- the latter sensitivity distribution function has a wider width, and the latter is less reliable than the former. . Therefore, the spatial resolution of the image is improved by setting the weighting coefficient depending on the detection depth position information corresponding to the measurement data set (measurement data).
- the ⁇ -ray detector 3 is configured to measure detection depth position information of four stages (ie, four layers). . That is, the ⁇ -ray detector 3 is composed of a four-layer DOI detector.
- the number of steps of the detection depth of the two detection elements (scintillator elements) for which the coincidence count was measured is g and h (1 ⁇ g, h ⁇ N), respectively, so that the number increases from the shallow level toward the deep level.
- the number of DOI detector stages that is, the number of layers
- the number of DOI detector stages is not particularly limited as long as it is a natural number (that is, a plurality) of 2 or more.
- the weighting coefficient w is expressed by a two-dimensional function having the stage number numbers g and h as discrete variables. As described above, the reliability of the count data measured for the deep DOI layer pair is lower than the reliability of the count data measured for the shallow DOI layer pair.
- the weighting coefficient w is set so that the coefficient w is smaller than the weighting coefficient w in the shallow DOI layer pair. As shown in FIG.
- FIG. 8A shows an example of the likelihood weighting factor for the pair of DOI layers.
- the two-dimensional function related to the weighting coefficient w is represented by a one-dimensional function for the other variable h as shown in FIG.
- the non-increasing function is 25.
- a one-dimensional function (non-increasing function) for the other variable obtained when one variable is fixed, and a one-dimensional function (non-increasing) for one variable obtained when the other variable is fixed. Function but not necessarily the same.
- a one-dimensional function (non-increasing function) with respect to may be set with different functions.
- the weighting factor set in this way is applied to the above equation (2) in the same manner as in the first embodiment.
- the weighted likelihood function of the above equation (2) can be maximized by the following iterative calculation algorithm (sequential approximation method) as in the first embodiment, and a digital image after reconstruction processing is acquired. can do.
- the specific reconstruction process is as shown in FIG. 3 as in the first embodiment. Note that the reference numerals of the steps in the second embodiment are the same as those in the first embodiment (S1 to S6).
- Step S1 Setting of Weighting Factor
- likelihood weighting factors w i for all data points are set.
- the weight coefficient is set based on the detected depth position information of the detection element (scintillator element) of the DOI detector as described above in the second embodiment. For example, as shown in FIGS. 2 and 7, in the case of a 4-layer (4-stage) DOI detector, the likelihood weighting coefficient shown in FIG. 8 is set for each pair of DOI layers.
- Step S2 to (Step S6) Steps S2 to S6 are the same as steps S2 to S6 of the first embodiment described above, and a description thereof will be omitted. As described above, steps S1 to S6 correspond to the reconstruction processing step in the present invention.
- the optimal multivariable function including a data function (likelihood function in each embodiment) and the like with a digital image as an unknown number is used. Weighting is performed when the reconstruction process is performed based on the calculation.
- the subfunction above (2) "w i a i ⁇ x-w i y i log (w i a i + r i) " in the equation) corresponding element data in the (i-th measured data points in each of the embodiments
- the spatial resolution of the image can be improved by weighting the partial function with the weighting factor of the back projection for the reconstructed image.
- the radiation detection apparatus is a positron emission tomography apparatus (PET apparatus) as in the first embodiment described above and the third embodiment described later.
- Measurement data of the subject M obtained by the radiation detection apparatus (partial ring type PET apparatus 1 in each embodiment) when the radiation detection apparatus is a PET apparatus as in each embodiment or a SPECT apparatus.
- the set here, count value measurement data, that is, count data in each embodiment
- the set is any of sinogram data, histogram data, and list mode data.
- the weighted likelihood function L (x) of the Poisson distribution in the second embodiment is It is represented by the above formula (2).
- the radiation detection apparatus is a PET apparatus as in each embodiment or a SPECT apparatus
- the partial function is not set without setting the weighting coefficient as in the conventional case.
- reconstruction processing is performed using
- the radiation detector ( ⁇ -ray detector 3 in each embodiment) constituting the radiation detection apparatus is configured to measure radiation detection depth position information, that is, each detection element is irradiated with radiation.
- a DOI detector constructed by laminating in the depth direction is used, the following phenomenon occurs. That is, the width of the sensitivity distribution function of the pair of detection elements (shallow DOI layer) closer to the measurement object and the pair of detection elements (deep DOI layer) on the far side becomes wider. The reliability is lower than the former. Therefore, the spatial resolution of the image can be improved by setting the weighting coefficient depending on the detection depth position information corresponding to the measurement data set (measurement data).
- the radiation detector ( ⁇ -ray detector 3) has N detection depth positions (four steps). It is configured to measure information (ie, configured with a DOI detector).
- the numbers are g and h (1 ⁇ g, h ⁇ N), respectively.
- the weighting factor is a two-dimensional function with the stage numbers g and h as discrete variables, and the two-dimensional function is a non-increasing function with respect to the other variable obtained when one variable is fixed. .
- the count data measured by the pair of deep DOI layers with low reliability is weighted by multiplying the count data measured by the pair of shallow DOI layers with high reliability by a weighting factor.
- the spatial resolution of the image can be improved.
- the error distribution of element data (i-th measurement data point) constituting the measurement data set (measurement data) is a Poisson distribution.
- the error distribution is a Poisson distribution, it is used in a nuclear medicine diagnostic apparatus (emission CT apparatus) represented by a PET apparatus or the like.
- the weighted likelihood function of the above equation (2) is the case where the measurement data is sinogram data or histogram data.
- a weighted likelihood function is set when the measurement data is list mode data.
- the list mode data is data in which detection event information (detector number, detection time, ⁇ -ray energy, etc.) obtained by the radiation detector of the PET apparatus is stored in time series.
- the weighted likelihood function L (x) of Poisson distribution is expressed by the following equation (4).
- N is the number of events (number of lists), and i (n) is the number of the measurement data point at which the nth event is measured (1 ⁇ i (n) ⁇ N).
- the weighting coefficient set in this way is applied to the above equation (4).
- the weighted likelihood function of the above equation (4) can be maximized by the iterative calculation algorithm (sequential approximation method) shown in FIG. 3 as in the first and second embodiments, and the digital image after the reconstruction process Can be obtained.
- steps S1 to S6 shown in FIG. 3 are the same as steps S1 to S6 of the first and second embodiments described above, description thereof will be omitted.
- step S4 updated image calculation
- the data format is list mode data
- steps S1 to S6 correspond to the reconstruction processing step in the present invention.
- the weighting coefficient in the above equation (4) is based on the directivity of linear noise generated in the reconstructed image when the weighting coefficient is a constant that does not depend on element data as in the first embodiment.
- a weighting factor may be set, or the weighting factor may be set based on the detection depth position information of the detection element of the DOI detector as in the second embodiment.
- the present invention is not limited to the above embodiment, and can be modified as follows.
- the positron emission tomography apparatus has been described as an example of the radiation detection apparatus.
- the apparatus is a device that acquires a measurement data set of a subject based on detection of radiation. If there is, it will not be specifically limited. You may apply to a single photon emission tomography apparatus (SPECT apparatus), an X-ray computed tomography apparatus (X-ray CT apparatus), etc.
- SPECT apparatus single photon emission tomography apparatus
- X-ray CT apparatus X-ray computed tomography apparatus
- the present invention may be applied to an apparatus that captures the entire body of the subject, an apparatus that captures the head of the subject, and an apparatus that captures the breast of the subject.
- the partial ring type PET apparatus 1 as shown in FIG. 1 is used. May be.
- the detector units 2A and 2B in FIG. 1 have the same configuration as that in FIG. 1 except that the detector units 2A and 2B are replaced with a breast examination unit 2C.
- the breast inspection unit 2C has a notch, and the breast is inspected by being sandwiched by this notch.
- a plurality of ⁇ -ray detectors 3 (not shown in FIG. 9) are arranged in parallel in the breast examination unit 2C in accordance with the notches.
- the DOI detector is used.
- the DOI detector may be applied to a radiation detector that does not distinguish the depth direction.
- the weighting factor is set based on the directivity of the linear noise generated in the reconstructed image when the weighting factor is a constant that does not depend on element data as in the first embodiment described above, the above-described implementation is performed.
- the weighting coefficient can be set without using the detection depth position information of the detection element of the DOI detector as in Example 2.
- the PET apparatus when the radiation detection apparatus is a positron emission tomography apparatus (PET apparatus), the PET apparatus (part in FIG. 1) includes a detector unit that is partially opened.
- the ring-type PET apparatus 1) is not necessarily a detector unit that is partially opened, but may be applied to a normal full-ring type PET apparatus.
- the weighting coefficient when the weighting coefficient is set based on the detection depth position information of the detection element of the DOI detector as in the second embodiment, the linear noise directivity as in the first embodiment described above is set. A weighting factor can be set without using it.
- the radiation detection apparatus is a positron emission tomography apparatus (PET apparatus)
- PET apparatus positron emission tomography apparatus
- two detector units 2A and 2B separated from each other see FIGS. 1, 4 and 5).
- one detector unit 2D having a part opened as shown in FIG. 10A may be used.
- the radiation detection apparatus is a positron emission tomography apparatus (PET apparatus)
- PET apparatus positron emission tomography apparatus
- two detector units 2A and 2B separated from each other see FIGS. 1, 4 and 5).
- the radiation detection apparatus is a positron emission tomography apparatus (PET apparatus)
- PET apparatus positron emission tomography apparatus
- detector units 2E, 2F, 2G, 2H separated from each other as shown in FIG. It may be a container unit.
- the first multivariable function constitutes a measurement data set (measurement data) of the subject M obtained by the radiation detection apparatus (partial ring type PET apparatus 1 in each embodiment).
- the data function (likelihood function in each embodiment) represented by the sum of partial functions configured based on the error distribution of element data (i-th measurement data point in each embodiment)
- the variable function may be the sum of the above-described data function (likelihood function) and the second multivariable function configured based on the prior information on the physical quantity distribution.
- the weighted likelihood function L (x) of the above equation (2) or (4) and the prior information on the physical quantity distribution An image reconstruction method based on maximization of L (x) + U (x) consisting of the sum of other functions U (x) consisting of the second multivariable function configured based on the above is also conceivable.
- L (x) is a function derived on the basis of statistical properties, whereas U (x) is based on prior information of a subject to be photographed (a deterministic property that the reconstructed image x may have). Is a function defined.
- U (x) is generally called “PenaltyalFunction”. An example of the successive approximation calculation formula when the penalty function is used is shown below (see the following formula (6)).
- c (k) j is the j-th pixel value of the approximate curvature image of the regularization function U (x) in the vicinity of the k-th estimated solution x (k) .
- the error distribution of the element data (i-th measurement data point) constituting the measurement data set (measurement data) is the Poisson distribution, but is not limited to the Poisson distribution.
- a Gaussian distribution may be used.
- the error distribution is a Gaussian distribution, it is used in an X-ray computed tomography apparatus (X-ray CT apparatus).
- the present invention has a radiation detection apparatus such as a positron emission tomography apparatus (PET apparatus), a single photon emission tomography apparatus (SPECT apparatus), and an X-ray computed tomography apparatus (X-ray CT apparatus). It is suitable for the image reconstruction technology of the tomographic imaging apparatus in general.
- PET apparatus positron emission tomography apparatus
- SPECT apparatus single photon emission tomography apparatus
- X-ray CT apparatus X-ray computed tomography apparatus
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Abstract
Description
(i)画像に直線状の偽像(直線状ノイズ)であるスジ状アーティファクト(ストリークアーティファクト)が生じる、
(ii)画像の空間分解能が装置パラメータ(主に放射線検出素子のサイズ)から予測される値よりも低い、
といった問題が生じる場合がある。これは、核医学診断装置(エミッションCT装置)において生じる課題(問題)であるが、X線コンピュータ断層撮影装置(X線CT装置)にまで拡げた場合においてもアーティファクトや画像の空間分解能の劣化が生じると考えられる。
すなわち、この発明の画像再構成処理方法は、放射線検出装置によって得られた被検体の計測データ集合から、前記計測データ集合の発生要因に関連する前記被検体の物理量分布を複数次元のデジタル画像として再構成する再構成処理を行う画像再構成処理方法であって、前記デジタル画像を未知数とする第1多変数関数は、(1)前記計測データ集合を構成する要素データの誤差分布に基づいて構成された部分関数の和で表されるデータ関数,または(2)前記計測データ集合を構成する要素データの誤差分布に基づいて構成された部分関数の和で表されるデータ関数と、前記物理量分布の事前情報に基づいて構成された第2多変数関数との和であり、前記画像再構成処理方法は、前記部分関数に対応する要素データの再構成画像に対する逆投影の重み係数で部分関数を重み付けて、重み付けされた前記データ関数,または前記データ関数と、前記物理量分布の事前情報に基づいて構成された第2多変数関数との和からなる前記第1多変数関数の最適化計算に基づいて前記再構成処理を行う再構成処理工程を備えることを特徴とするものである。
上記(2)式において、全てのデータ点に対する尤度重み係数wiを設定する。具体的には、本実施例1では同一の検出器ユニット2A内の検出素子(シンチレータ素子)のペアによるデータ点に対してはwi=α(0<α<1)、それ以外のデータ点に対してはwi=1とする。
非負画像を初期画像x(0)とする。後述する実施例2、3も含めて、本実施例1のような陽電子放射断層撮影装置(PET装置)などに代表される核医学診断装置(エミッションCT装置)に適用する場合には、0を除いてx(0)>0とする。初期画像x(0)については、例えば一様な画素値を有する再構成画像であればよい。
反復計算アルゴリズム(逐次近似法)における反復回数のカウンタ変数をkとして、反復回数のカウンタ変数kを初期化する(k=0)。
下記(3)式を用いて、(k+1)回目の更新画像x(k+1)を計算する。ただし、Jは再構成画像の画素数である。下記(3)式から明らかなように、上記(2)式の重み付き尤度関数を最大化するために、尤度重み係数wiが下記(3)式にも掛かる。
カウンタ変数kをインクリメントする(k←k+1)。なお、「k←k+1」とは右辺の(k+1)を左辺のkに代入するという意味である。
反復計算アルゴリズムを終了する反復回数をNiterとし、カウンタ変数kが反復回数Niterに達したか?否かを判断する。なお、反復回数Niterについてはユーザが予め設定すればよい。k<Niterの場合には反復計算アルゴリズムを継続するために、ステップS4に戻る。k<Niterの場合には反復計算アルゴリズムが終了したとして一連の計算を終了する。
上記(2)式において、全てのデータ点に対する尤度重み係数wiを設定する。上述した実施例1と相違するのは、本実施例2では上述したようにDOI検出器の検出素子(シンチレータ素子)の検出深さ位置情報に基づいて重み係数を設定する点である。例えば、図2および図7に示すように、4層(4段)のDOI検出器の場合には、各DOI層のペアに対して図8に示す尤度重み係数を設定する。
ステップS2~S6は、上述した実施例1のステップS2~S6と同じであるので、その説明については省略する。以上のように、ステップS1~S6は、この発明における再構成処理工程に相当する。
2A,2B … 検出器ユニット
3 … γ線検出器
L(x) … 重み付き尤度関数
wi … 重み係数(尤度重み係数)
SA … ストリークアーティファクト
h,g … 離散変数
U(x) … ペナルティ関数
M … 被検体
Claims (9)
- 放射線検出装置によって得られた被検体の計測データ集合から、前記計測データ集合の発生要因に関連する前記被検体の物理量分布を複数次元のデジタル画像として再構成する再構成処理を行う画像再構成処理方法であって、
前記デジタル画像を未知数とする第1多変数関数は、
(1)前記計測データ集合を構成する要素データの誤差分布に基づいて構成された部分関数の和で表されるデータ関数,
または(2)前記計測データ集合を構成する要素データの誤差分布に基づいて構成された部分関数の和で表されるデータ関数と、前記物理量分布の事前情報に基づいて構成された第2多変数関数との和
であり、
前記画像再構成処理方法は、
前記部分関数に対応する要素データの再構成画像に対する逆投影の重み係数で部分関数を重み付けて、重み付けされた前記データ関数,または前記データ関数と、前記物理量分布の事前情報に基づいて構成された第2多変数関数との和からなる前記第1多変数関数の最適化計算に基づいて前記再構成処理を行う再構成処理工程
を備えることを特徴とする画像再構成処理方法。 - 請求項1に記載の画像再構成処理方法において、
前記放射線検出装置は、陽電子放射断層撮影装置,単一光子放射断層撮影装置,X線コンピュータ断層撮影装置のいずれかであることを特徴とする画像再構成処理方法。 - 請求項2に記載の画像再構成処理方法において、
前記重み係数は、前記重み係数を前記要素データに依存しない定数とした場合に再構成画像に生じる直線状ノイズの指向性に基づいて設定されていることを特徴とする画像再構成処理方法。 - 請求項3に記載の画像再構成処理方法において、
前記直線状ノイズの走行方向に沿った要素データに対する重み係数は、前記直線状ノイズの走行方向に沿っていない要素データに対する重み係数よりも小さいことを特徴とする画像再構成処理方法。 - 請求項2に記載の画像再構成処理方法において、
前記放射線検出装置を構成する放射線検出器は、互いに分離した複数の検出器ユニットで構成されており、
同一の検出器ユニット内の検出素子を結んだ直線方向に沿った要素データに対する重み係数は、互いに異なる検出器ユニット内の検出素子を結んだ直線方向に沿った要素データに対する重み係数よりも小さいことを特徴とする画像再構成処理方法。 - 請求項1に記載の画像再構成処理方法において、
前記放射線検出装置は、陽電子放射断層撮影装置,単一光子放射断層撮影装置のいずれかであり、
前記計測データ集合は、サイノグラムデータ,ヒストグラムデータ,リストモードデータのいずれかであることを特徴とする画像再構成処理方法。 - 請求項6に記載の画像再構成処理方法において、
前記放射線検出装置を構成する放射線検出器は、放射線の検出深さ位置情報を計測するように構成されており、
前記重み係数は、前記計測データ集合に対応した前記検出深さ位置情報に依存することを特徴とする画像再構成処理方法。 - 請求項7に記載の画像再構成処理方法において、
Nを2以上の自然数としたときに、前記放射線検出器はN段の検出深さ位置情報を計測するように構成されており、
前記放射線検出器を構成する検出素子であって、浅い段から深い段に向かうにしたがって番号が大きくなるように、同時計数を計測した2つの検出素子の検出深さの段数番号をそれぞれg,h(1≦g,h≦N)としたときに、前記重み係数は前記段数番号g,hを離散変数とする2次元関数であり、前記2次元関数は一方の変数を固定したときに得られる他方の変数に対する1次元関数が非増加関数であることを特徴とする画像再構成処理方法。 - 請求項1から請求項8のいずれかに記載の画像再構成処理方法において、
前記誤差分布は、ポアソン分布,ガウス分布のいずれかであることを特徴とする画像再構成処理方法。
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