Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N.sup.4), where N is the size of the measurement data, to O(M.sup.4), where M is the size of an individual decimated measurement data array, wherein M<N.

An imaging method may include obtaining imaging data associated with a region of interest (ROI) of an object. The imaging data may correspond to a plurality of time-series images of the ROI. The imaging method may also include determining, based on the imaging data, a data set including a spatial basis and one or more temporal bases. The spatial basis may include spatial information of the imaging data. The one or more temporal bases may include temporal information of the imaging data. The imaging method may also include storing, in a storage medium, the spatial basis and the one or more temporal bases.

Methods and systems are provided for reconstructing images from measurement data using one or more deep neural networks according to a decimation strategy. In one embodiment, a method for reconstructing an image using measurement data comprises, receiving measurement data acquired by an imaging device, selecting a decimation strategy, producing a reconstructed image from the measurement data using the decimation strategy and one or more deep neural networks, and displaying the reconstructed image via a display device. By decimating measurement data to form one or more decimated measurement data arrays, a computational complexity of mapping the measurement data to image data may be reduced from O(N.sup.4), where N is the size of the measurement data, to O(M.sup.4), where M is the size of an individual decimated measurement data array, wherein M<N.

Disclosed herein are systems and methods for determining one or more cardiac arrhythmia targets for ablation. The method may include receiving one or more mappings, identifying an abnormality in the one or more mappings, combining the one or more mappings, and defining the one or more cardiac arrhythmia targets based on an overlap of the identified abnormality in the combined one or more mappings.

A system for MRI is provided. The system may obtain a plurality of sets of under-sampled k-space data corresponding to a plurality of frames. Each set of under-sampled k-space data may be acquired simultaneously from a plurality of slice locations of a subject in one of the frames using an MRI scanner. The system may reconstruct a plurality of reference slice images based on the sets of under-sampled k-space data of the plurality of frames. Each of the reference slice images may be representative of one of the slice locations in more than one frame of the frames. The system may further reconstruct a plurality of image series based on the sets of under-sampled k-space data and the reference slice images. Each image series may correspond to one of the slice locations and include a plurality of slice images of the corresponding slice location in the plurality of frames.

Disclosed herein are a method and a system for creating a registered image that integrates the information of CT and CBCT images. With the present method and system, medical practitioners can precisely transform the information of CT image-based treatment plan into the CBCT image so as to accurately control the dosage and location of a radiation therapy. Accordingly, also disclosed herein are methods of treating a cancer in the subject with the aid of the method and/or system of the present disclosure.

An embodiment of the invention relates to a scanning device. The scanning device includes a scanning unit to detect radiation received during a scanning operation on an object. An imaging unit is arranged to reconstruct an image for a location on the object based on the detected radiation. A texture analysis unit receives an indicated area of interest of a medical image and computes at least one texture metric for the area of interest. An image comparison unit receives a plurality of texture metrics for a common area of interest within respective medical images and outputs a change metric indicating a measure of variation over time for the area of interest based on a comparison of the plurality of texture metrics.

An information processing apparatus performs coordinate calibration between a two-dimensional (2D) coordinate system and a three-dimensional (3D) coordinate system. The apparatus includes a 2D camera interface, a 3D camera interface, and a processor. The processor is configured to receive, from the 2D camera interface, first 2D image data of a flat plate at a first position and second 2D image data of the flat plate at a second position different from the first position, receive, from the 3D camera interface, first 3D coordinate information of the flat plate at the first position and second 3D coordinate information of the flat plate at the second position, and generate a transformation matrix for transforming a 3D coordinate value to a 2D coordinate value, based on the first and second 2D image data and the first and second 3D coordinate information.

A system and method for the detection of ROIs in images obtained of a breast or other tissue of a patient significantly improves the speed and precision/accuracy of navigation between multimodality 2D and 3D images. In the system and method, images of the tissue are obtained in a DBT acquisition to generate a synthetic 2D image of the imaged tissue and in a 3D, e.g., ultrasound, image acquisition. The 2D image generation process creates a synthetic 2D image that embed a navigation map correlating pixels in the 2D images to sections of the 3D ultrasound volume, such as via a registration between the 3D ultrasound volume and a 3D volume created using the DBT image data. When a synthetic 2D image is reviewed, an ROI on the 2D image is selected and the system will additionally present the user with the section of the 3D volume containing that ROI.

A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.