A medical information processing apparatus according to an embodiment includes a processing circuit. The processing circuit is configured: to generate, on the basis of first data obtained in a first imaging process, second data equivalent to data obtained in an imaging process performed under an image taking condition different from that of the first imaging process; to generate, on the basis of quality of the second data, assistance information to assist reviewing related to a second imaging process scheduled to be executed later than the first imaging process under an image taking condition different from that of the first imaging process; and to cause a display circuit to display the assistance information prior to the execution of the second imaging process.

The disclosure provides a low-dose image reconstruction method and system based on prior anatomical structure difference. The method includes: determining the weights of different parts in the low-dose image based on prior information of anatomical structure differences; constructing a generative network being taking the low-dose image as input extract features, and integrating the weights of the different parts in the feature extraction process, outputting a predicted image; constructing a determining network being taking the predicted image and standard-dose image as input, to distinguish the authenticity of the predicted image and standard-dose image as the first optimization goal, and identifying different parts of the predicted image as the second optimization goal, collaboratively training the generative network and the determining network to obtain the mapping relationship between the low-dose image and the standard-dose image; and reconstructing the low-dose image by using the obtained mapping relationship. The disclosure can obtain more accurate high-definition images.

The present disclosure relates to systems and methods for image reconstruction. The system may obtain an initial image to be processed. The system may generate a reconstructed image by performing a plurality of iteration steps on the initial image. Each of the plurality of iteration steps may include a first optimization operation and at least one second optimization operation. The first optimization operation and the at least one second optimization operation may be executed sequentially. The first optimization operation may include receiving an image to be processed in the iteration step and determining an updated image by preliminarily optimizing the image to be processed. The at least one second optimization operation may include determining an optimized image by reducing interference information of the updated image and designating the optimized image as a next image to be processed in a next iteration step. The interference information may include noise information and/or artifact information.

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 disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.

A method for operating an X-ray facility for recording a three-dimensional (3D) image data set of a target area of a patient is provided. A recording arrangement including an X-ray detector and an X-ray source may be rotated about an axis of rotation for recording two-dimensional projection images based on the image data set. A model instance of a parameterizable patient model that is patient-specific and 3D is determined. Target area information describing the target area is determined in the model instance from default information. At least two at least partially different partial recording areas of the target area are determined from the target area information. The partial recording areas cover the target area along the axis of rotation. One projection image set is recorded for each of the partial recording areas, and the image data set is reconstructed from the projection image sets.

A system for inspecting an object, includes: a source of X-ray radiation; a horizontal array of detectors, wherein the source and the array of detectors are positioned substantially on a first plane; a platform configured to rotate as well as translate in a vertical trajectory, wherein the platform is positioned on a second plane between the source and the array of detectors, and wherein the object is disposed on the platform; and a computing device configured to: cause the source to fire a substantially horizontal fan beam in a third plane, wherein the third plane is above a top of the object; acquire calibration data from the array of detectors while the third plane is above the top of the object; cause the platform to simultaneously rotate and raise the object vertically upwards; acquire scan data of the object; and generate a three dimensional scan image of the object.

The present disclosure relates to systems and methods for imaging. The method may include obtaining a first image and topology data of an object, wherein the topology data may include first topology data and second topology data, and the first topology data may correspond to the second topology data. The method may also include determining a base material density image corresponding to the first image, determining a difference in the topology data based on the first topology data and the second topology data, and determining a second image corresponding to the first image based on the first image, the base material density image, and the difference in the topology data.

A low-dose imaging method includes receiving a sparse image set of a portion of a patient's anatomy; up-sampling the sparse image set, in the sinogram domain and using a first neural network, to yield an up-sampled sinogram; generating, from the up-sampled sinogram, an initial reconstruction; and removing, from the initial reconstruction and using a second neural network, one or more artifacts in the initial reconstruction to yield a final output volume.

An image processing method of the present disclosure may include receiving a scanned image, and processing the received image through an octave convolution-based neural network to output a high-quality image and an edge image for the received image. The octave convolution-based neural network may include a plurality of octave encoder blocks and a plurality of octave decoder blocks. Each octave encoder block may include an octave convolutional layer, and may be configured to output a high-frequency feature map and a low-frequency feature map for the image.