Canon Medical Systems Corporation

General Electric Company

Toshiba Medical Systems Corporation

The University of Chicago

The State University of New York

Joseph Manak

James Begelman

Victor Gorin

Jingwu Yao

John Baumgart

Yiemeng Hoi

Kazumasa Arakita

Amit Jain

Yu-Bing Chang

Bernhard Erich Hermann Claus

Haruki Iwai

Peter Michael Edic

Jeffrey Wayne Eberhard

David Allen Langan

Xiaochuan Pan

Hao Lai

Dan Xia

Zheng Zhang

Yi Hu

Takuya Sakaguchi

Robert John Filkins

Adrian F Warner

Brian Lee Lawrence

John Scott Price

Yves Lucien Trousset

Daniel Richard Schneidewend

James Vradenburg Miller

Matthew Christian Nielsen

Jens Rittscher

Hisato Takemoto

Andrew James Wheaton

Susanne Madeline Lee

Bruce Matthew Dunham

Forrest Frank Hopkins

Abdalmajeid Musa Alyassin

Cyril Riddell

Kedar Bhalchandra Khare

Stephen Rudin

Seiichirou Nagai

Daniel R Bednarek

William Eugene Powell, Iii

Bernhard Claus

Weifeng Shang

Rui Hua

Bradley Stephens

A system and method is provided for controlling an acquisition system to obtain, based on a currently acquired set of projection data, a next set of projection data that is to be acquired using a set of learned imaging conditions. The set of learned imaging conditions are, at least in part, based on training data including projection data and dose information. In one embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen at random. In another embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen based on a test scan and a trained neural network for outputting the initial dose and angle using a loss function.

Apparatus, method, and non-transitory computer-readable storage medium for generating a task-aware dynamic sparse-view scanning procedure

A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.

X-ray imaging restoration using deep learning algorithms

An apparatus for magnetic resonance imaging includes processing circuitry to obtain a set of sequence instructions for performing a magnetic resonance scan; partition the obtained set of sequence instructions into a plurality of kernels by determining partition time points defining boundaries of the plurality of kernels, wherein the partition time points are not equally spaced in time; convert a first kernel of the plurality of kernels into a first hardware instruction set; transmit the first hardware instruction set to a hardware board controller for execution; and reconstruct a magnetic resonance image from received data, including data obtained by executing the first kernel.

Backend sequence kernel

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires image data including image data of a blood vessel of a subject. The processing circuitry performs analysis related to the blood vessel by using the image data, and specifies a region of interest in the blood vessel based on a result of the analysis. The processing circuitry performs fluid analysis on a region other than the region of interest at a first accuracy, and performs fluid analysis on the region of interest at a second accuracy that is higher than the first accuracy.

Medical image processing apparatus, medical image processing method, and X-ray CT apparatus

A control method for controlling data processing acquired from medical imaging modalities by using multiple data processors connected to multiple medical imaging modalities via a network. The method includes obtaining image information for imaging to be performed with an imaging modality from the multiple imaging modalities. The method also includes obtaining load information of the multiple data processors before the imaging is completed. Allocating, based on graph information generated based on the obtained load information, at least a part of the multiple data processors to processing of data acquired in imaging based on the imaging information. The control method may conclude by performing processing of the acquired data with the allocated data processing resource.

Task allocation for use in medical imaging and diagnostics

A system and a method by which multiple regions or objects of interest can be indicated within an X-ray image, from which a user can select a primary region or object of interest and accordingly adjust the appropriate X-ray dose for obtaining a better quality image of the selected regions or objects of interest.

System and method for indication and selection of region of interest for x-ray dose adjustment

A method of imaging includes obtaining a first image including projection data representing an intensity of X-rays detected by a plurality of detectors at a first X-ray exposure setting, the X-rays being emitted from an X-ray source; based on a detection result of a first object in the first image: determining a background region of interest (ROI) around the first object, the background ROI including background ROI pixels having a first intensity value corresponding to the intensity of the X-rays; and converting, for each pixel of the background ROI pixels, the first intensity values of the background ROI pixels to a normalized X-ray attenuation factor; and determining a second X-ray exposure setting for use in obtaining a second image based on the background ROI pixels converted to the normalized X-ray attenuation factor.

Method and apparatus for automatic determination of object and background region of interest for real-time automatic dose rate control in dynamic imaging systems

Medical image processing apparatus, medical image processing method, and x-ray CT apparatus

A system, method and computer readable medium to render, as a single image, source data from a network of data providers, that provide source data of a respective data type. A composition space contains two-dimensional (2D) display surfaces to which the source data is provided from the data providers, and transformation operator surfaces, which are located at different positions in a depth direction than the 2D display surfaces. Each of the transformation operator surfaces represent a respective visual transformation operation on surfaces of the 2D display surfaces that are positioned at lesser depths than the transformation operator surface. Using data-centric communication, updates are published to the 2D display surfaces, the updates including the source data from the data providers, and, upon detecting an update to a subscribed 2D display surface, the subscribed 2D display surface is rendered as the single image by performing the visual transformation operations.

Network centric windowing system for integration and display of visual data from networked sources

An apparatus and method are provided for computed tomography (CT) imaging to reduce artifacts due to objects outside the field of view (FOV) of a reconstructed image. The artifacts are suppressed by using an iterative reconstruction method to minimize a cost function that includes a de-emphasis operator. The de-emphasis operator operates in the data domain, and minimizes the contributions of data inconsistencies arising from attenuation due to objects outside the FOV. This can be achieved by penalizing images that manifest indicia of artifacts due to outside objects especially those outside objects have high-attenuation densities and minimizing components of the data inconsistency likely attributable to the outside object.

Method and apparatus to reduce artifacts in a computed-tomography (CT) image by iterative reconstruction (IR) using a cost function with a de-emphasis operator

A method and apparatus is provided to generate two X-ray projection images, using different focal-spot sizes in the X-ray source. The large and small focal-spot images have different image qualities (e.g., different signal-to-noise rations (SNR) and different resolution). The two images are combined, in either the spatial or frequency domains, to generate a combined image, exhibiting the best attributes of the constitutive small and large focal-spot images. In the spatial domain, change regions and uniform regions are determined based on spatial variations within the images, and the superposition generating the combined image weights the small focal-spot image more in the change regions and the large focal-spot image more in the uniform regions. In the frequency domain, the combined image superimposes low-frequency components of the large focal-spot image with high-frequency components of the small focal-spot image.

Image combining using images with different focal-spot sizes

A method and apparatus is provided to generate a multiresolution image having at least two regions with different pixel pitches. The multiresolution image is reconstructed using projection data having various pixel pitches corresponding to the pixel pitches of the multiresolution image. By using a higher resolution inside regions of interest (ROIs) in both the image and projection domains and lower resolution outside the ROIs, fast image reconstruction can be performed while avoiding truncation artifacts, which result imaging is limited to an ROI excluding attenuation regions. Further, those regions of greater clinical relevance and greater structural variance within the reconstructed images can be selected to be within the ROIs to improve the clinical benefit of the multiresolution image. The multiresolution image can be reconstructed using an iterative reconstruction method in which the high- and low-resolution regions are uniquely evaluated.

Multiresolution iterative reconstruction for region of interest imaging in X-ray cone-beam computed tomography

Embodiments for aggregating multiple data sources on a single display device are provided. In one example, a computing device comprises at least one input configured to receive data from one or more data sources, a user interface to receive user input, and instructions to identify an event of a procedure based on one or more of data received from the one or more data sources and user input received via the user interface, and arrange one or more display information elements on a display device based on the identified event and a workflow protocol.

Macro-enabled display elements

Growing community of inventors

Albany, NY, United States of America

Joseph Manak