Abstract: Disclosed is a CT image generation method for attenuation correction of PET images. According to the method, a CT image and a PET image at T1 and a PET image at T2 are acquired and input into a trained deep learning network to obtain a CT image at T2; the CT image can be applied to the attenuation correction of the PET image, thereby obtaining more an accurate PET AC (Attenuation Correction) image. According to the CT image generation method for attenuation correction of PET images, the dosage of X-rays received by a patient in the whole image acquisition stage can be reduced, and physiological and psychological pressure of the patient is relieved. In addition, the later image acquisition only needs a PET imaging device, without the need of PET/CT device, cost of imaging resource distribution can be reduced, and the imaging expense of the whole stage is reduced.

Abstract: Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Abstract: Disclosed is a CT image generation method for attenuation correction of PET images. According to the method, a CT image and a PET image at T1 and a PET image at T2 are acquired and input into a trained deep learning network to obtain a CT image at T2; the CT image can be applied to the attenuation correction of the PET image, thereby obtaining more an accurate PET AC (Attenuation Correction) image. According to the CT image generation method for attenuation correction of PET images, the dosage of X-rays received by a patient in the whole image acquisition stage can be reduced, and physiological and psychological pressure of the patient is relieved. In addition, the later image acquisition only needs a PET imaging device, without the need of PET/CT device, cost of imaging resource distribution can be reduced, and the imaging expense of the whole stage is reduced.

Abstract: Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Abstract: Disclosed is a three-dimensional low-dose PET reconstruction method based on deep learning. The method comprises the following steps: back projecting low-dose PET raw data to the image domain to maintain enough information from the raw data; selecting an appropriate three-dimensional deep neural network structure to fit the mapping between the back projection of the low-dose PET and a standard-dose PET image; after learning from the training samples the network parameters are fixed, realizing three-dimensional PET image reconstruction starting from low-dose PET raw data, thereby obtaining a low-dose PET reconstructed image which has a lower noise and a higher resolution compared with the traditional reconstruction algorithm and image domain noise reduction processing.

Abstract: A method of calculating a system matrix for time-of-flight (TOF) list-mode reconstruction of positron-emission tomography (PET) images, the method including determining a TOF geometric projection matrix G including effects of object attenuation; estimating an image-blurring matrix R in image space; obtaining a diagonal matrix D that includes TOF-based normalization factor; and calculating the system matrix H as H=DGR.

Abstract: A method and apparatus for generating crystal efficiency correction factors by performing a normalization calibration based on delayed data. The method and apparatus obtain delayed data from a scan of a patient using a Positron Emission Tomography (PET) scanner, generate a sinogram from the obtained delayed data, determine, using a processing circuit, mean fan and block line of response sensitivities from the generated sinogram, determine, using the processing circuit, mean detector efficiency based on the determined mean fan and block line of response sensitivities, determine, using the processing circuit, an individual crystal efficiency based on the determined mean fan and block line of response sensitivities and the mean detector efficiency for each module, and calculate the crystal efficiency correction factors based on the determined individual crystal efficiency of each module.

Abstract: A method and system for determining a position of a source including obtaining prompt data and related delayed data from a Positron Emission Tomography (PET) scanner, generating a sinogram from the prompt data, generating crystal efficiency correction factors by performing a normalization calibration based on the obtained delayed data, performing normalization correction on the generated sinogram based on the crystal efficiency correction factors to generate a corrected sinogram, rebinning the corrected sinogram to generate a plurality of two-dimensional sinogram slices, and determining a central axis for each of the plurality of two-dimensional sinogram slices using a center estimation process.

Abstract: A method is provided for estimating scatter in a positron emission tomography (PET) scan at multiple bed positions, the method comprising calculating a first scatter sinogram based on scatter data obtained at a first bed position, calculating a second scatter sinogram based on scatter data obtained at a second bed position, and deriving a third scatter sinogram for a third bed position between the first bed position and the second bed position, wherein the third scatter sinogram is derived from the first scatter sinogram according to a first percentage of overlap of the first bed position with the third bed position, and from the second scatter sinogram according to a second percentage of overlap of the second bed position with the third bed position.

Abstract: A method is provided for estimating scatter in a positron emission tomography (PET) scan at multiple bed positions, the method comprising calculating a first scatter sinogram based on scatter data obtained at a first bed position, calculating a second scatter sinogram based on scatter data obtained at a second bed position, and deriving a third scatter sinogram for a third bed position between the first bed position and the second bed position, wherein the third scatter sinogram is derived from the first scatter sinogram according to a first percentage of overlap of the first bed position with the third bed position, and from the second scatter sinogram according to a second percentage of overlap of the second bed position with the third bed position

Abstract: A method of calculating a system matrix for time-of-flight (TOF) list-mode reconstruction of positron-emission tomography (PET) images, the method including determining a TOF geometric projection matrix G including effects of object attenuation; estimating an image-blurring matrix R in image space; obtaining a diagonal matrix D that includes TOF-based normalization factor; and calculating the system matrix H as H=DGR.

Abstract: A method and system for determining a position of a source including obtaining prompt data and related delayed data from a Positron Emission Tomography (PET) scanner, generating a sinogram from the prompt data, generating crystal efficiency correction factors by performing a normalization calibration based on the obtained delayed data, performing normalization correction on the generated sinogram based on the crystal efficiency correction factors to generate a corrected sinogram, rebinning the corrected sinogram to generate a plurality of two-dimensional sinogram slices, and determining a central axis for each of the plurality of two-dimensional sinogram slices using a center estimation process.

Abstract: A method and apparatus for generating crystal efficiency correction factors by performing a normalization calibration based on delayed data. The method and apparatus obtain delayed data from a scan of a patient using a Positron Emission Tomography (PET) scanner, generate a sinogram from the obtained delayed data, determine, using a processing circuit, mean fan and block line of response sensitivities from the generated sinogram, determine, using the processing circuit, mean detector efficiency based on the determined mean fan and block line of response sensitivities, determine, using the processing circuit, an individual crystal efficiency based on the determined mean fan and block line of response sensitivities and the mean detector efficiency for each module, and calculate the crystal efficiency correction factors based on the determined individual crystal efficiency of each module.

Abstract: A method and device for calculating voxels defining a tube-of-response (TOR) within a reconstruction space of a Positron Emission Tomography (PET) apparatus having a plurality of crystals, the voxels within the reconstruction space having a predetermined size. The method includes selecting a center on each of two crystals defining a line of response, determining intersected voxels within the reconstruction space that intersect a straight line connecting the centers of the two crystals, calculating neighboring voxels of the intersected voxels, based on an expansion direction and an expansion distance, merging the intersected voxels and the neighboring voxels to form a merged set of voxels, and deleting duplicate voxels in the merged set of voxels to generate the voxels defining the TOR.

Abstract: A method, device, and system for calculating first and second distance ratios used to calculate a geometric probability between a voxel and a tube-of-response (TOR). The method includes determining a first-edge-line including a first-middle-point, determining a second-edge-line including a second-middle-point, determining a middle line of the TOR, projecting a first point of a first surface of the voxel to the middle line, projecting a second point of a second surface of the voxel to the middle line, calculating a first distance between one of the first and second middle-points and the first-projected-point, and a second distance between the one of the first and second middle-points and the second-projected-point, and determining a first distance ratio based on the first and second distances. The method calculates the second distance ratio similarly to the first distance ratio. The geometric probability is proportional to the product of the first and second distance ratios.

Abstract: A method, device, and system for calculating first and second distance ratios used to calculate a geometric probability between a voxel and a tube-of-response (TOR). The method includes determining a first-edge-line including a first-middle-point, determining a second-edge-line including a second-middle-point, determining a middle line of the TOR, projecting a first point of a first surface of the voxel to the middle line, projecting a second point of a second surface of the voxel to the middle line, calculating a first distance between one of the first and second middle-points and the first-projected-point, and a second distance between the one of the first and second middle-points and the second-projected-point, and determining a first distance ratio based on the first and second distances. The method calculates the second distance ratio similarly to the first distance ratio. The geometric probability is proportional to the product of the first and second distance ratios.

Abstract: A method and device for calculating voxels defining a tube-of-response (TOR) within a reconstruction space of a Positron Emission Tomography (PET) apparatus having a plurality of crystals, the voxels within the reconstruction space having a predetermined size. The method includes selecting a center on each of two crystals defining a line of response, determining intersected voxels within the reconstruction space that intersect a straight line connecting the centers of the two crystals, calculating neighboring voxels of the intersected voxels, based on an expansion direction and an expansion distance, merging the intersected voxels and the neighboring voxels to form a merged set of voxels, and deleting duplicate voxels in the merged set of voxels to generate the voxels defining the TOR.