Canon Medical Systems Corporation

Liang Cai

Zhou Yu

Jian Zhou

Tzu-Cheng Lee

Xiaohui Zhan

Qiulin Tang

Masakazu Matsuura

Hiroki Taguchi

Yujie Lu

Kevin Christopher Zimmerman

Ting Xia

Xiaochun Lai

Takuya Nemoto

Yi Qiang

Hiroaki Miyazaki

Wenyuan Qi

Peng Peng

Richard Thompson

Thomas Labno

Chih-chieh Liu

Xi Chen

A method, system, and computer readable medium to compensate for consecutive missing views in Computed Tomography (CT) reconstruction. By utilizing at least one complementary ray from a previous or subsequent view, the missing view(s) can be filled in. When plural complementary rays exist, a linear or non-linear combination of rays can be used to fill in the missing views, and the weights used in the combination may be smoothed to prevent over-emphasis of the replacement views.

Method and system to compensate for consecutive missing views in computed tomography (CT) reconstruction

A method of denoising a plurality of input images, including grouping the plurality of input images into a plurality of groups; determining, for each group of the plurality of groups, an initial representative image for the group; performing, for each group, a sequence of T1 denoising sampling steps using a diffusion-based probabilistic model, starting with the initial representative image for the group, to generate a corresponding sequence of representative images for the group, wherein T1 is an integer greater than or equal to 1; and for each input image in each group, performing a second sequence of T2 denoising sampling steps using the model to generate a final image that is a restored image corresponding to the input image, wherein T2 is an integer greater than or equal to 1, and T=T1+T2.

Fast diffusion-based image restoration workflow via sharing of initial diffusion steps

A medical data processing method according to an embodiment includes inputting first medical data relating to a subject imaged with a medical image capture apparatus to a learned model to configured to generate second medical data having lower noise than that of the first medical data and having a super resolution compared with the first medical data based on the first medical data to output the second medical data.

Medical data processing method, model generation method, medical data processing apparatus, and computer-readable non-transitory storage medium storing medical data processing program

A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.

Medical image processing method, medical image processing apparatus, and computer readable non-volatile storage medium storing medical image processing program

An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

Information processing method, medical image diagnostic apparatus, and information processing system

An information processing method controls a CT scanner such that the method includes, but is not limited to, determining an X-ray irradiation period from an electrocardiogram acquired from an electrocardiography device attached to a living object to be imaged, by processing the electrocardiogram at multiple different cardiac phases; performing, by controlling a CT gantry including and rotatably supporting an X-ray source and an X-ray detector, a diagnostic CT scan in the determined X-ray irradiation period, of at least a part of the heart region, to obtain a CT image; and causing a display unit to display the obtained CT image. The method can be performed at least by an information processing apparatus including processing circuitry and/or computer instructions stored in a non-transitory computer readable storage medium for performing the method.

Information processing method, medical image diagnostic apparatus, and information processing system for detecting a quiescent cardiac phase

A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.

Apparatus and methods for image quality improvement based on noise and dose optimization

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.

Devices, systems, and methods for deep-learning kernel-based scatter estimation and correction

The present disclosure relates to a spatially-variant model of a point spread function and its role in enhancing medical image resolution. For instance, a method of the present disclosure comprises receiving a first medical image having a first resolution, applying a neural network to the first medical image, the neural network including a first subset of layers and, subsequently, a second subset of layers, the first subset of layers of the neural network generating, from the first medical image, a second medical image having a second resolution and the second subset of layers of the neural network generating, from the second medical image, a third medical image having a third resolution, and outputting the third medical image, wherein the first resolution is lower than the second resolution and the second resolution is lower than the third resolution.

Apparatus, method, and non-transitory computer-readable storage medium for enhancing computed tomography image resolution

Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction (PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.

Devices, systems, and methods for motion-corrected medical imaging

Multiple interactions, such as Compton scattering, inside a PET detector are used to predict an incident photon's direction for identifying true coincidence events versus scatter/random coincidence events by creating a cone shaped shell projection defining a range of possible flight directions for the incident photon. The disclosed techniques can be used as prior information to improve the image reconstruction process. The disclosed techniques can be implemented in a LYSO/SiPM-based layer stacked detector, which can precisely register multiple interactions' 3D position.

Scatter and random coincidence rejection

An embodiment of a computed tomography apparatus includes an x-ray source and scan control circuitry configured to control the x-ray source to expose a subject with x-rays over a scan having a plurality of views. A detector is disposed to receive x-rays from the x-ray source, has a plurality of anodes arranged in groups, and a common conductive strip between the anodes. Photon counting circuits are respectively provided for each of the anodes and have adjustable operating parameters. Connection circuitry is configured to adaptively connect, in a first mode, each anode to one of the photon counting circuits and, in a second mode, each anode in a group to a same one of the photon counting circuits. Processing circuitry, connected to the connection circuitry and the photon counting circuits, is configured to, for each of the views, select the first mode or the second mode and adjust the operating parameters based upon exposure data obtained from exposing the subject with the x-rays from the x-ray source.

X-ray CT apparatus with adaptive photon counting detectors

According to one embodiment, a photon-counting detector (PCD) includes a plurality of macro-pixels. The plurality of macro-pixels arranged on a semiconductor crystal has a first face and a second face. The first face and the second face are parallel. Each macro-pixel from the plurality of macro-pixels is configured to acquire projection data for generating a reconstructed image. The plurality of macro-pixels each includes at least one large micro-pixel is disposed within the each macro-pixel and at least two small micro-pixels is disposed within the each macro-pixel. Each of the at least two small micro-pixels has a surface area that is less than a surface area of the at least one large micro-pixel.

Photon counting detector and x-ray computed tomography apparatus

A method and apparatuses are provided to identify and correct partial volume errors (PVEs) in material decomposition of a spectral computed tomography (CT) scan, due to different X-ray trajectories incident on a same macro-pixel passing through different material components (e.g., bone and water). Macro-pixels are virtual crystals generated by aggregating the signals/counts from several smaller actual pixels (i.e., micro-pixels) of a detector array. Thus, when a PVE is identified within a macro-pixel, the separate signals/counts from the micro-pixels can be used for material decomposition, instead of the aggregated signals/counts of the macro-pixel, thereby providing improved spatial resolution of the material components and, at least partial, overcoming the PVE. A measure of the difference between spectrally-resolved counts based a material projection lengths (e.g., from a calibrated lookup table) and the measured counts of the macro-pixel can be used to identify PVEs, e.g., when the difference measure exceeds a predefined threshold.

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Vernon Hills, IL, United States of America

Liang Cai