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.

Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.

A method is for reconstructing an image from recording data. In an embodiment, the method includes providing or measuring recording parameters with which the recording data was created; generating a preprocessing filter for the recording data based on the recording parameters; prefiltering the recording data with the preprocessing filter, to create prefiltered recording data; and reconstructing the image from the prefiltered recording data. A prefilter-generating unit, an apparatus corresponding to the method, a control facility and an image-taking system are also disclosed.

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.

Methods and systems for signal processing in molecular imaging. The system may include at least one storage device including a set of instructions and at least one processor in communication with the storage device. The at least one processor may obtain a first signal that is acquired by sampling, according to a first sampling frequency, an electrical signal of a detector. The at least one processor may also generate, based on the first signal and a target machine learning model, a second signal, the second signal corresponding to a second sampling frequency that is different from the first sampling frequency. The target machine learning model may specify a target mapping between the first signal and the second signal. The at least one processor may further generate an image based on the second signal.

The present technology relates to an imaging system. The imaging system can comprise at least one processor configured to apply a projection precomputation algorithm and an x-ray tomography image reconstruction system. The projection precomputation algorithm can be configured to: generate a projection operator matrix that can be used to calculate a plurality of voxels from a plurality of projection measurements before the plurality of projection measurements is acquired and store the projection operator matrix in memory. The projection operator matrix can be at least one of: a compressed matrix, a multi-iteration projection operator matrix, and a combination thereof. The x-ray tomography image reconstruction system can be configured to apply the projection operator matrix to generate a reconstructed three-dimensional image of at least an internal portion of a selected object under a surface of the selected object when the plurality of projection measurements is acquired.

Systems and methods for image reconstruction and quantitative MRI. Generative models such as diffusion models are used to reconstruct MR images and generative models and constrained mathematical models fit to estimate quantitative maps from the reconstructed MR images.

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.

The present disclosure provides a system and method for image processing. The method may include obtaining multiple projection images of a subject acquired by an imaging device from multiple view angles; generating an initial slice image of the subject by image reconstruction based on the multiple projection images; determining, based on the multiple projection images, a target out-of-plane artifact of the initial slice image; and generating a corrected slice image by correcting the initial slice image with respect to the target out-of-plane artifact.