A tomographic imaging system including an extended source antenna, which produces a wavefield scattered by the internal structure of an object. A processor recursively reconstructs the internal structure by processing a current image of the internal structure of the object with a neural network operator trained to synthesize measurements of a point-source antenna corresponding to a wavefield scattered by the current image of the internal structure of the object, processing the synthesized measurements of the point-source antenna with a calibration neural network to estimate measurements of the extended-source antenna, and updating the current image of the internal structure of the object based on a difference between the measurements of the extended-source antenna and the estimation of the measurements produced by the calibration neural network.

A computer-implemented method includes where at least one projection mapping pair of an object under examination by a medical biplane imaging device is acquired. The at least one projection mapping pair contains a first and a second projection mapping of the object under examination, that map the object under examination simultaneously in a first and a second detection plane. The first and second detection planes are arranged non-parallel to one another. A correction model for the correction of an artifact and/or a movement is determined. The artifact or the movement is mapped simultaneously in the at least one first and the at least one second projection mapping. The at least one projection mapping pair specifies a consistency condition for the determination of the correction model. The result dataset is reconstructed at least from the at least one first projection mapping and on the basis of the correction model.

To reduce an unnatural distortion of an image that occurs in a case in which a high absorption body of X-rays is included in movement correction image reconstruction processing of a CT image. High absorption body presence or absence determination processing of determining whether or not a high absorption body of X-rays is included is added to movement correction image reconstruction processing, and processing of limiting pixel values of an image pair used for movement correction or processing of selecting an image pair used for calculating a movement vector is performed based on a result of the determination. An X-ray CT apparatus includes a high absorption body determination unit or a pixel value adjustment unit to perform these pieces of processing.

A method, apparatus, and computer instructions stored on a computer-readable medium perform latent image feature extraction by performing the functions of receiving image data acquired during an imaging of a patient, wherein the image data includes motion by the patient during the imaging; segmenting the image data to include M image data segments corresponding to at least N motion phases having shorter durations than a duration of the motion by the patient during the imaging, wherein M is a positive integer greater than or equal to a positive integer N; producing, from the M image data segments, at least N latent feature vectors corresponding to the motion by the patient during the imaging; and performing a gated reconstruction of the N motion phases by reconstructing the image data based on the at least N latent feature vectors.

Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Systems, methods, and devices for medical imaging are disclosed herein. In some embodiments, a method for imaging an anatomic region includes receiving, from a detector carried by an imaging arm of an x-ray imaging apparatus, a plurality of images of the anatomic region. The images can be obtained during manual rotation of the imaging arm. The imaging arm can be stabilized by a shim structure during the manual rotation. The method can also include receiving, from at least one sensor coupled to the imaging arm, pose data of the imaging arm during the manual rotation. The method can further include generating, based on the images and the pose data, a 3D representation of the anatomic region.

A display control unit displays a tomographic image of a specific tomographic plane in a first medical image on a display unit. A finding classification unit classifies each pixel of a partial region of the first medical image into at least one finding. A feature amount calculation unit calculates a first feature amount for each finding in the partial region. A weighting coefficient setting unit sets a weighting coefficient indicating a degree of weighting, which varies depending on a size of each finding, for each finding. A similarity derivation unit performs a weighting operation for the first feature amount for each finding calculated in the partial region and a second feature amount for each finding calculated in a second medical image on the basis of the weighting coefficient to derive a similarity between the first medical image and the second medical image.

A computer-implemented method, performed by a computing device, comprises: receiving raw multispectral medical imaging data, which was acquired via an energy-resolved medical imaging technique; determining a scope of analysis of the raw multispectral medical imaging data; selecting at least one spectral map based on the scope of analysis; and processing the raw multispectral medical imaging data to generate at least one medical image based on the at least one spectral map.

A computer-implemented method includes obtaining, via a processing system including one or more processors, multi-channel k-space data of a subject acquired with a magnetic resonance imaging scanner. The computer-implemented method also includes utilizing, via the processing system, subspace convolutional kernels on the multi-channel k-space data to combine information from local neighboring k-space locations and across multiple channels to generate subspace compressed k-space data having fewer channels than a number of channels utilized to acquire the multi-channel k-space data.