公开(公告)号：US20230119427A1

公开(公告)日：2023-04-20

申请号：US17985236

申请日：2022-11-11

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian ZHOU ,  Zhou YU ,  Yan LIU

IPC: A61B5/00 ,  G06T11/00 ,  G16H30/40

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to reduce noise and artifacts in reconstructed medical images, such as images generated using computed tomography, positron emission tomography, and magnetic resonance imaging. The DL network can operate either on pre-reconstruction data or on a reconstructed image. The DL network can be an artificial neural network or a convolutional neural network (e.g., using a three-channel volumetric kernel architecture). Different neural networks can be trained depending on the noise level, scanning protocol, or the anatomic, diagnostic or clinical objective of the reconstructed image (e.g., by partitioning the training data into noise-level range and training respective DL networks for each range). Further, the DL networks can be trained to mitigate artifacts, such as the cone-beam artifact.

公开(公告)号：US20200323508A1

公开(公告)日：2020-10-15

申请号：US16915722

申请日：2020-06-29

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian ZHOU ,  Zhou YU ,  Yan LIU

IPC: A61B6/00 ,  A61B6/04 ,  A61B6/02

Abstract: An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). To calibrate the forward model, which represents each order of pileup using a respective pileup response matrix (PRM), an optimization search determines the elements of the PRMs that optimize an objective function measuring agreement between the spectra of recorded counts affected by pulse pileup and the estimated counts generated using forward model of pulse pileup. The spectrum of the recorded counts in the projection data is corrected using the calibrated forward model, by determining an argument value that optimizes the objective function, the argument being either a corrected X-ray spectrum or the projection lengths of a material decomposition. Images for material components of the material decomposition are then reconstructed using the corrected projection data.

公开(公告)号：US20210290193A1

公开(公告)日：2021-09-23

申请号：US17339093

申请日：2021-06-04

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Jian ZHOU ,  Ruoqiao ZHANG ,  Zhou YU ,  Yan LIU

IPC: A61B6/00 ,  G06T11/00 ,  G06T5/20 ,  G06T5/10 ,  G06N3/08 ,  G06N3/04 ,  G06T5/50

Abstract: A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.