A C-Arm X-ray imaging system using multiple pulsed X-ray sources in motion to perform efficient and ultrafast 3D radiography is presented. X-ray sources mounted on a structure in motion to form an array. X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Each individual source can also move rapidly around its static position in a small distance. When a source has a speed that is equal to group speed but with opposite moving direction, the source at one C-arm end and X-ray flat panel detector at other C-arm end are activated through an external exposure control unit so that source stay momentarily standstill. The C-arm provides 3D X-ray scan imaging over a wide sweep angle and in different position by rotation. The X-ray image can be analyzed by an artificial intelligence module for real-time diagnosis.

An X-ray imaging system using multiple pulsed X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple pulsed X-ray sources mounted on a structure in motion to form an array of sources. The multiple X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Electron beam inside each individual X-ray tube is deflected by magnetic or electrical field to move focal spot a small distance. When focal spot of an X-ray tube beam has a speed that is equal to group speed but with opposite moving direction, the X-ray source and X-ray flat panel detector are activated through an external exposure control unit so that source tube stay momentarily standstill equivalently. 3D scan can cover much wider sweep angle in much shorter time and image analysis can also be done in real-time.

An X-ray imaging system using multiple pulsed X-ray sources in motion to perform high efficient and ultrafast 3D radiography using an X-ray flexible curved panel detector is presented. There are multiple pulsed X-ray sources mounted on a structure in motion to form an array of sources. The sources move simultaneously relative to an object on a predefined arc track at a constant speed as a group. Each individual X-ray source can move around its static position at a small distance. When an individual source has a speed equal to group speed, but with opposite moving direction, the individual source and detector are activated. This allows source to stay relatively standstill during activation. The operation results in reduced source travel distance for each individual source. 3D radiography image data can be acquired with much wider sweep angle in much shorter time, and image analysis can also be done in real-time.

A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Embodiments discussed herein facilitate determination of whether lesions are benign or malignant. One example embodiment is a method, comprising: accessing medical imaging scan(s) that are each associated with distinct angle(s) and each comprise a segmented region of interest (ROI) of that medical imaging scan comprising a lesion associated with a first region and a second region; providing the first region(s) of the medical imaging scan(s) to trained first deep learning (DL) model(s) of an ensemble and the second region(s) of the medical imaging scan(s) to trained second DL model(s) of the ensemble; and receiving, from the ensemble of DL models, an indication of whether the lesion is a benign architectural distortion (AD) or a malignant AD.

A method and system for image feature classification using a NN-based learning algorithms to make a decision about a feature in a medical image or image part. In particular, embodiments may make use of a phase of a multi-phasic image to improve classification accuracy. For instance, embodiments may combine different phases of multiphasic images as training data.

An information processing apparatus includes an image feature acquiring unit configured to acquire first image features and second image features from a medical image and a deriving unit configured to derive image findings of a plurality of items belonging to a first finding type based on the first image features and deriving image findings of a plurality of items belonging to a second finding type different from the first finding type based on the second image features that at least partly differ from the first image features.

Provided are a learning apparatus, a learning method, a trained model, and a program capable of efficiently performing learning for disease detection with high accuracy while suppressing a cost. A learning apparatus (100) includes a processor (129), a memory (114) that stores a data set of a medical image and lesion information included in the medical image, and a learning model (126) with an attention mechanism (128) that estimates a disease from an input medical image. The processor performs processing of specifying a position of a region of interest indicated by an attention map (208) in organ labeling information (206), and outputting a specification result (210), processing of calculating an error by comparing an estimation result (212) with lesion information (204), processing of setting the error on the basis of the specification result (210), and processing of causing the learning model (126) to perform learning by using the set error.

An X-ray imaging system with multiple pulsed X-ray source tubes in motion to perform highly efficient and ultrafast 3D radiography is presented. There are multiple X-ray tubes from pulsed sources mounted on a structure in motion to form an array of X-ray tubes. The tubes move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Each individual X-ray tube in each individual source can also move rapidly around its static position in a small distance. When a tube has a speed that is equal to group speed but with opposite moving direction, the tube and X-ray flat panel detector are activated through an external exposure control unit so that the tube stay momentarily standstill. It results in much reduced travel distance for each X-ray source tube and much lighter load for motion system. 3D X-ray scan can cover much wider sweeping angle in much shorter time and image analysis can also be done in real time.

A computer-implemented method for determining and evaluating an objective tumor response to an anti-cancer therapy using cross-sectional images includes accessing an identification of a target lesion in one or more cross-sectional images at a computing system, determining a computer segmentation of the target lesion, and, based upon the computer segmentation, automatically determining one or more lesion metrics for the target lesion. The one or more lesion metrics includes at least a target lesion length. The target lesion length includes a short axis measurement when the target lesion is a lymph node, and the target lesion length includes a long axis measurement when the target lesion is not a lymph node. The computer-implemented method further includes generating a summary display including at least the target lesion length.