The most appropriate image for a diagnostic target among a plurality of images is selected and accurate diagnosis support information is presented regardless of the type of a selected image, a modality, or the like. An image diagnosis support apparatus includes: a diagnostic information generation unit that generates diagnostic information based on a plurality of medical images; a reliability calculation unit that evaluates an image quality and calculates an image reliability for each of the plurality of medical images; and a degree-of-contribution calculation unit that calculates a degree of contribution of each of the plurality of medical images to the diagnostic information using an internal parameter indicating a degree of appropriateness of each medical image for a diagnostic target and the reliability calculated by the reliability calculation unit. An image for detection used by the diagnostic information generation unit is generated based on the degree of contribution.

The radiography apparatus is driven by power supplied from the battery. In the radiography apparatus, a radiation source that emits radiation to a subject, a radiation detector that receives the radiation transmitted through the subject and outputs a radiographic image, and an image processing device that performs image processing on the radiographic image are integrated. A CPU of the image processing device acquires a remaining level of the battery. The CPU performs control to operate both a first processing block that performs a noise suppression process and a visibility improvement process as first image processing and a second processing block that performs a density correction process as second image processing in a case in which the remaining level of the battery is equal to or greater than a threshold value. In contrast, in a case in which the remaining level of the battery is less than the threshold value, the CPU performs control to stop an operation of the first processing block and to operate only the second processing block.

A method of imaging a breast compressed with a foam paddle includes emitting an x-ray energy from an x-ray source towards the breast and the foam paddle having a plurality of upper markers and a plurality of lower markers, wherein the plurality of lower markers are movable relative to the upper markers. The x-ray energy is detected at a detector disposed opposite the breast from the x-ray source. An image of the compressed breast is generated based on the detected x-ray energy. At least one of the plurality of upper markers and at least one of the plurality of lower markers is identified in the image. A thickness of the compressed breast at a plurality of thickness locations is determined, wherein each of the plurality of thickness locations corresponds to at least one of the plurality of lower markers.

A computer-implemented method is provided for improving live video quality. The method comprises: (a) acquiring, using a medical imaging apparatus, a stream of consecutive image frames of a subject; (b) feeding the stream of consecutive image frames to a first set of denoising components, wherein each of the first set of denoising components is configured to denoise an image frame from the stream of consecutive image frames in a spatial domain to output an intermediate image frame; (c) feeding a plurality of the intermediate image frames to a second denoising component, wherein the second denoising component is configured to (i) denoise the plurality of the intermediate image frames in a temporal domain and (ii) generate a weight map; and outputting a final image frame with improved quality in both temporal domain and spatial domain based at least in part on the weight map.

Systems and methods are provided for a multi-scale deep learning-based digital breast tomosynthesis (DBT) image reconstruction that mitigates the superposition of breast tissue along with the limited angular artifacts, and improves in-depth resolution of the resulting images. A multi-scale deep neural network may be used where a first network may focus on a first parameter, such as limited angular artifacts reduction, and a second network may focus on a second parameter, such as image detail refinement. The output from the first neural network may be used as the input for the second neural network. The systems and methods may reduce the sparse-view artifacts in DBT via deep learning without losing image sharpness and contrast. A deep neural network may be trained in a way to reduce training-time computational cost. An ROI loss method may be used for further improvement on the resolution and contrast of the images.

The embodiment of the present disclosure discloses a method and a system for image acquisition, image quality evaluation, and medical image acquisition. The image acquisition method comprises obtaining an image of a target subject; obtaining, based on one or more evaluation models, one or more scores related to one or more evaluation indexes of the image; obtaining a quality evaluation result of the image based on the one or more scores related to the one or more evaluation indexes; and determining one or more acquisition parameters based on the quality evaluation result.

A device may receive an X-ray image captured by a C-arm CBCT device at a particular position defined by a six-degree of freedom pose relative to an anatomy, and may process the X-ray image, with a machine learning model, to determine a predicted quality of next possible X-ray images provided by the C-arm CBCT device. The device may utilize the machine learning model, to identify a particular X-ray image, of the next possible X-ray images, with a greatest predicted quality and to update the six-degree of freedom pose based on the particular X-ray image. The device may provide, to the C-arm CBCT device, data that identifies the updated six-degree of freedom pose to cause the C-arm CBCT device to adjust to a new position based on the updated six-degree of freedom pose.

Systems, methods, and devices for capturing a single image with multiple exposures is provided. An imaging device may be provided comprising a source configured to emit a wave for a time period and a detector configured to receive a signal indicative of the wave. A wave may be emitted for a time period and a signal may be received indicative of the emitted wave. A first image dataset may be saved with a first timestamp referencing a first time within the time period. A second image dataset may be saved with a second timestamp referencing a second time within the time period. The second time may occur after the first time.

Disclosed herein is a method comprising: capturing a first image of a tissue using radiation; selecting a region of the tissue based on the first image; capturing a second image of the tissue in the region using the radiation; wherein a signal-to-noise ratio of the second image is higher than a signal-to-noise ratio of the first image.

An image processing apparatus processes a dynamic image including a plurality of frames obtained from a radiographic imaging apparatus performing radiographic dynamic imaging, and includes a hardware processor that performs a process of reducing an unexpected image of a structural object other than a subject, when the structural object is unexpectedly captured in the dynamic image including the plurality of frames obtained by the dynamic imaging.