公开(公告)号：US12008689B2

公开(公告)日：2024-06-11

申请号：US17542245

申请日：2021-12-03

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Yujie Lu ,  Tzu-Cheng Lee ,  Liang Cai ,  Jian Zhou ,  Zhou Yu

IPC: G06V10/00 ,  G06N3/08 ,  G06T11/00 ,  G16H30/40

CPC classification number: G06T11/005 ,  G06N3/08 ,  G16H30/40 ,  G06T2211/40

Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

公开(公告)号：US11759162B2

公开(公告)日：2023-09-19

申请号：US17345716

申请日：2021-06-11

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Wenyuan Qi ,  Yujie Lu ,  Ryo Okuda ,  Evren Asma ,  Manabu Teshigawara ,  Jeffrey Kolthammer

IPC: A61B6/00 ,  A61B6/03

CPC classification number: A61B6/5282 ,  A61B6/032 ,  A61B6/037 ,  A61B6/5205

Abstract: The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.

公开(公告)号：US20230177745A1

公开(公告)日：2023-06-08

申请号：US17542245

申请日：2021-12-03

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Yujie Lu ,  Tzu-Cheng Lee ,  Liang Cai ,  Jian Zhou ,  Zhou Yu

IPC: G06T11/00 ,  G16H30/40 ,  G06N3/08

CPC classification number: G06T11/005 ,  G16H30/40 ,  G06N3/08 ,  G06T2211/40

Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

公开(公告)号：US11276209B2

公开(公告)日：2022-03-15

申请号：US16860425

申请日：2020-04-28

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Wenyuan Qi ,  Yujie Lu ,  Evren Asma ,  Yi Qiang ,  Jeffrey Kolthammer ,  Zhou Yu

IPC: G06T11/00 ,  A61B6/03 ,  A61B6/00 ,  G06T7/00

Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

公开(公告)号：US10937206B2

公开(公告)日：2021-03-02

申请号：US16252392

申请日：2019-01-18

Applicant: Canon Medical Systems Corporation

Inventor： Yujie Lu ,  Zhou Yu ,  Jian Zhou ,  Tzu-Cheng Lee ,  Richard Thompson

IPC: G06T11/00 ,  G06T7/11

Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.