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US 12,657,796 B2Granted
Convolutional neural network for dynamic pet frame clustering
AssigneeCanon Kabushiki Kaisha
InventorsChung Chan, Li Yang, Xiaoli Li, Wenyuan Qi, Evren Asma, Jeffrey Kolthammer
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Abstract
A dynamic frame reconstruction apparatus and method for medical image processing is disclosed which reduces the computationally expensive reconstruction of images but which retains the accuracy of the image reconstruction. A convolutional neural network is used to cluster the dynamic data into groups of frames, each group sharing similar radiotracer distribution. In one embodiment, groups of frames that have similar reconstruction parameters are determined, and scatter and random estimations are computed once and shared among each of the frames in the same frame group.
Claims18
An image processing apparatus, comprising:
- processing circuitry configured to
- receive list-mode data corresponding to a plurality of detection times,
- generate a plurality of frames based on the list-mode data,
- assign at least one frame of the plurality of frames into a first frame group based on a similarity of each frame within the first frame group,
- assign at least one frame of the plurality of frames into a second frame group based on a similarity of each frame within the second frame group,
- estimate first and second frame group-specific reconstruction parameters based on (1) frames in the first frame group, and (2) frames in the second frame group, respectively, wherein the first and second frame group-specific reconstruction parameters are different,
- reconstruct a first set of frame data from any frame of the first frame group based on the first frame group-specific reconstruction parameters, and
- reconstruct a second set of frame data from any frame of the second frame group based on the second frame group-specific reconstruction parameters,
- wherein the processing circuitry is further configured to perform clustering using a neural network, and
- the processing circuitry is further configured to produce a set of latent features from the plurality of frames the set of latent features being extracted from the neural network, assign the at least one frame of the plurality of frames into the first frame group based on a similarity of latent features of the set of latent features of each frame within the first frame group, and assign the at least one frame of the plurality of frames into the second frame group based on a similarity of latent features of the set of latent features of each frame within the second frame group.
7 more elements
An image processing method, comprising:
- receiving list-mode data corresponding to a plurality of detection times;
- generating a plurality of frames based on the list-mode data;
- assigning at least one frame of the plurality of frames into a first frame group based on a similarity of each frame within the first frame group;
- assigning at least one frame of the plurality of frames into a second frame group based on a similarity of each frame within the second frame group;
- estimating first and second frame group-specific reconstruction parameters based on (1) frames in the first frame group, and (2) frames in the second frame group, respectively, wherein the first and second frame group-specific reconstruction parameters are different;
- reconstructing a first set of frame data from any frame of the first frame group based on the first frame group-specific reconstruction parameters;
- and
- reconstructing a second set of frame data from any frame of the second frame group based on the second frame group-specific reconstruction parameters,
- wherein the method further comprises performing clustering using a neural network, and
- the method further comprises producing a set of latent features from the plurality of frames, the set of latent features being extracted from the neural network, assigning the at least one frame of the plurality of frames into the first frame group based on a similarity of latent features of the set of latent features of each frame within the first frame group, and assigning the at least one frame of the plurality of frames into the second frame group based on a similarity of latent features of the set of latent features of each frame within the second frame group.
7 more elements
A non-transitory computer-readable medium storing a program that, when executed by processing circuitry, causes the processing circuitry to perform an image processing method, comprising:
- receiving list-mode data corresponding to a plurality of detection times;
- generating a plurality of frames based on the list-mode data;
- assigning at least one frame of the plurality of frames into a first frame group based on a similarity of each frame within the first frame group;
- assigning at least one frame of the plurality of frames into a second frame group based on a similarity of each frame within the second frame group;
- estimating first and second frame group-specific reconstruction parameters based on (1) frames in the first frame group, and (2) frames in the second frame group, respectively, wherein the first and second frame group-specific reconstruction parameters are different;
- reconstructing a first set of frame data from any frame of the first frame group based on the first frame group-specific reconstruction parameters;
- and
- reconstructing a second set of frame data from any frame of the second frame group based on the second frame group-specific reconstruction parameters,
- wherein the image processing method further comprises performing clustering using a neural network, and
- the image processing method further comprises producing a set of latent features from the plurality of frames, the set of latent features being extracted from the neural network, assigning the at least one frame of the plurality of frames into the first frame group based on a similarity of latent features of the set of latent features of each frame within the first frame group, and assigning the at least one frame of the plurality of frames into the second frame group based on a similarity of latent features of the set of latent features of each frame within the second frame group.
7 more elements
Dependencies are inferred from the claim text.