Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction (PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.

The present disclosure includes: transforming a time-domain voltage signal collected by an MPI system device to a frequency domain; calculating a square root of a square sum of a real part and an imaginary part at each frequency point of a frequency domain signal; arranging acquired amplitudes in a descending order, and acquiring a screening threshold by an amplitude ratio method; screening an amplitude through the screening threshold and constructing frequency domain signal data; acquiring a row vector of a system matrix corresponding to each frequency point of the data, so as to construct an update system matrix; and solving, based on the frequency domain signal array and the update system matrix, an inverse problem in a form of a least square based on an L2 constraint to obtain a three-dimensional magnetic particle concentration distribution result, so as to achieve a fast reconstruction of the MPI system.

Disclosed are an apparatus and method for medical image processing according to pathologic lesion properties, the method including: recognizing a readout area different from an original readout area in a medical image by applying a previously trained deep learning model to the medical image, extracting properties, which include at least one of a location and a size of the readout area, from the medical image, and generating a readout image for the readout area, which is different from the original readout area corresponding to a purpose of taking the medical image, by reconstructing the medical image, thereby having an effect on generating a readout image for a different kind of pathologic lesion from a previously acquired medical image.

A method for diagnostic imaging reconstruction uses a prior image x.sup.pr from a scan of a subject to initialize parameters of a neural network which maps coordinates in image space to corresponding intensity values in the prior image. The parameters are initialized by minimizing an objective function representing a difference between intensity values of the prior image and predicted intensity values output from the neural network. The neural network is then trained using subsampled (sparse) measurements of the subject to learn a neural representation of a reconstructed image. The training includes minimizing an objective function representing a difference between the subsampled measurements and a forward model applied to predicted image intensity values output from the neural network. Image intensity values output from the trained neural network from coordinates in image space input to the trained neural network are computed to produce predicted image intensity values.

Various methods and systems are provided for a medical imaging system. In one embodiment, a method for a projection imaging system includes acquiring a first image of a region of interest (ROI) with the projection imaging system in a first position, determining a three-dimensional (3D) location of an annotation on the first image via a geometric transformation using planes, acquiring a second image of the ROI with the projection imaging system in a second position, determining a location of the annotation on the second image based on the 3D location of the annotation in the first position and a geometry of the second position, and displaying the annotation on the second image in response to an accuracy check being satisfied.

The disclosure relates to a system and method for image reconstruction. The method may include the steps of: obtaining raw data corresponding to radiation rays within a volume, determining a radiation ray passing a plurality of voxels, grouping the voxels into a plurality of subsets such that at least some subset of voxels are sequentially loaded into a memory, and performing a calculation relating to the sequentially loaded voxels. The radiation ray may be determined based on the raw data. The calculation may be performed by a plurality of processing threads in a parallel hardware architecture. A processing thread may correspond to a subset of voxels.

An X-ray imaging system using multiple puked X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple puked X-ray sources mounted on a structure in motion to form an array of sources. The multiple X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Electron beam inside each individual X-ray tube is deflected by magnetic or electrical field to move focal spot a small distance. When focal spot of an X-ray tube beam has a speed that is equal to group speed but with opposite moving direction, the X-ray source and X-ray flat panel detector are activated through an external exposure control unit so that source tube stay momentarily standstill equivalently. 3D scan can cover much wider sweep angle in much shorter time and image analysis can also be done in real-time.

Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

A method includes recording a plurality of projection recordings along a linear trajectory. An X-ray source and an X-ray detector move in parallel opposite to one another along the linear trajectory and the examination object is arranged between the X-ray source and the X-ray detector. The method includes reconstructing a tomosynthesis dataset, respective depth information of the examination object is respective determined along an X-ray beam bundle spanned by the motion along the linear trajectory and an X-ray beam fan of the X-ray source perpendicular to the linear trajectory so that different respective depth levels in the object parallel to a detection surface of the X-ray detector are respectively scanned differently. Finally, the method includes determining a first slice image with a first slice thickness in a depth level, among the respective depth levels, substantially parallel to the detection surface of the X-ray detector based on the tomosynthesis dataset.

A framework for quality-driven image processing. In accordance with one aspect, image data and anatomical data of a region of interest are received. Zonal information is generated based on the anatomical data. Image processing is performed based on the image data to generate an intermediate image. One or more image quality metrics may then be determined for the intermediate image data using the zonal information. A processing action may be performed based on the one or more image quality metrics to generate a final image.