Techniques for compensating magnetic resonance imaging (MRI) data for artefacts caused by motion of a subject being imaged. The techniques include obtaining spatial frequency data obtained by using a magnetic resonance imaging (MRI) system to perform MRI on a patient, the spatial frequency data including first spatial frequency data and second spatial frequency data; determining a transformation using a first image obtained using the first spatial frequency data and a second image obtained using the second spatial frequency data; determining a residual spatial phase; correcting, using the transformation, second spatial frequency data and the residual spatial phase, to obtain corrected second spatial frequency data and a corrected residual spatial phase; and generating a magnetic resonance (MR) image using the corrected second spatial frequency data and the corrected residual spatial phase.

A method for rendering metal obscured regions in a volume radiographic image reconstructs a first 3D image using a plurality of 2D projection images obtained over a scan angle range relative to the subject and identifies metal in the first 3D image or metal shadows in the plurality of 2D projection images. Then, metal obscured regions are determined in a reconstructed 3D image of the object, and an alternative reconstruction being a limited angle reconstruction is performed for the metal obscured regions and displayed to the user with an indication of the spatial relationship to a corresponding metal obscured region.

A method for reconstructing an image may include obtaining scan data relating to a subject. The method may also include determining a first field of view (FOV) and determining a second FOV. The method may further include reconstructing a first image based on a first portion of the scan data corresponding to the first field of view, and reconstructing a second image based on a second portion of the scan data corresponding to the second field of view. The method may also include generating a third image based on the first image and the second image.

A method for reconstructing an image data set from magnetic resonance data is provided. First measurement data is captured using an image capturing device. The first measurement data is captured using temporal and/or spatial subsampling and is used for reconstructing the image data set with a compressed sensing algorithm in which a boundary condition that provided agreement with the measurement data and a target function that is used in an iterative optimization. The compressed sensing algorithm evaluates candidate data sets for the image data set are used. In the reconstruction using the compressed sensing algorithm, in addition to the first measurement data, second measurement data that is captured by a second imaging modality that is different from the first imaging modality of the first measurement data but by the same image capturing device. The second measurement data is registered to the first measurement data, by a modification of the boundary condition and/or target function.

A system and method for constructing fluoroscopic-based three dimensional volumetric data from two dimensional fluoroscopic images including a computing device configured to facilitate navigation of a medical device to a target area within a patient and a fluoroscopic imaging device configured to acquire a fluoroscopic video of the target area about a plurality of angles relative to the target area. The computing device is configured to determine a pose of the fluoroscopic imaging device for each frame of the fluoroscopic video and to construct fluoroscopic-based three dimensional volumetric data of the target area in which soft tissue objects are visible using a fast iterative three dimensional construction algorithm.

Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

The present disclosure relates to methods, systems, and non-transitory computer readable mediums for reconstructing an image. Image data may be obtained, wherein the image data may be generated by a detector array. A weighting window may be determined based on at least one parameter relating to the detector array. A first set of data may be determined based on the image data and the weighting window. An objective function associated with a target image may be determined based on the first set of data, wherein the objective function may include a first model, the first model may represent a difference between the target image and the first set of data, and the first model may be identified based on the first set of data. The target image may be reconstructed by performing a plurality of iterations based on the objective function.

Techniques of prospectively compensating for motion of a subject being imaged by an MRI system, the MRI system comprising a plurality of magnetics components including at least one gradient coil and at least one radio-frequency (RF) coil, the techniques comprising: obtaining first spatial frequency data and second spatial frequency data by operating the MRI system in accordance with a pulse sequence, wherein the pulse sequence is associated with a sampling path that includes at least two non-contiguous portions each for sampling a central region of k-space; determining a transformation using a first image obtained using the first spatial frequency data and a second image obtained using the second spatial frequency data; correcting the pulse sequence using the determined transformation to obtain a corrected pulse sequence; and obtaining additional spatial frequency data in accordance with the corrected pulse sequence.

A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.