A method is for generating image data of an examination object via a computed tomography system including a data processing unit; an X-ray radiation source and an X-ray radiation detector suspended on a support and mounted to be rotatable about a z-axis; and an examination table for supporting the examination object and a reference object arranged in a fixed position relative to the examination table. The method includes generating a raw data set by displacing the X-ray radiation source and the X-ray radiation detector relative to the examination object. During generation of the raw data set, at least one part of the examination object is sampled together with at least one part of the reference object. The sampling of the at least one part of the reference object is used to compensate at least in part for the influence of movement errors during the displacement.

A reconstruction unit generates a plurality of tomographic images representing a plurality of tomographic planes of a subject by reconstructing a plurality of projection images acquired by performing tomosynthesis imaging. A synthesis unit synthesizes the plurality of tomographic images to generate a composite two-dimensional image. A display control unit displays the composite two-dimensional image on a display, and in a case where one tissue of a first tissue and a second tissue that are present in the subject in association with each other is selected in the displayed composite two-dimensional image, emphasizes and displays the selected one tissue and the other tissue associated with the selected one tissue.

Proposed are a method and device for regularizing rapid three-dimensional tomographic imaging using a machine-learning algorithm. A method for regularizing three-dimensional tomographic imaging using a machine-learning algorithm according to an embodiment comprises the steps of: measuring a three-dimensional tomogram of a cell to acquire a raw tomogram of the cell; acquiring a regularized tomogram by using a regularization algorithm; and learning the relationship between the raw tomogram and the regularized tomogram through machine-learning.

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining a first set of imaging data, the first set of imaging data being sampled in multiple shots, each shot of the multiple shots corresponding to a plurality of echo times, the first set of imaging data including partially sampled data in a first k space; obtaining a second set of imaging data, the second set of imaging data including fully sampled data in a central region of a second k space; determining fitting data in the first k space based on the first set of imaging data and the second set of imaging data; and/or generating a target image based on the fitting data in the first k space and the first set of imaging data in the first k space.

A processor acquires a plurality of first projection images acquired by imaging an object at a plurality of radiation source positions and acquires a lesion image indicating a lesion. The processor combines the lesion image with the plurality of first projection images on the basis of a geometrical relationship between the plurality of radiation source positions and a position of the lesion virtually disposed in the object to derive a plurality of second projection images. The processor reconstructs the plurality of second projection images to generate a tomographic image including the lesion.

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.

A computer-implemented method is for providing a difference image data record. In an embodiment, the method includes a determination of a first real image data record of an examination volume in respect of a first X-ray energy, and a determination of a multi-energetic real image data record of the examination volume in respect of a first X-ray energy and a second X-ray energy, the second X-ray energy differing from the first X-ray energy. The method further includes the determination of the difference image data record of the examination volume by applying a trained function to input data, wherein the input data is based upon the first real image data record and the multi-energetic real image data record, as well as the provision of the difference image data record.

The invention relates to an assembly (10) comprising an OCT device (20) for scanning an object region volume (22) arranged in an object region (18) using an OCT scanning beam (21), an object (24) with a section, which can be arranged in the object region (18) and which can be located in the object region volume (22) by means of the OCT device (20), in the object region volume (22), and a calculating unit (60) which is connected to the OCT device (20) and contains a computer program for ascertaining a 3D reconstruction of the object region volume (22) and for ascertaining the position of the section of the object (24) in the object region volume (22) by processing OCT scanning information obtained by scanning the object region volume (22) using the OCT device (20). According to the invention, the computer program has a calculation routine for ascertaining a target area (90) in the 3D reconstruction of the object region volume (22), said calculation routine determining a reference variable for the object (24) relative to the target area (90). The object (24) is designed as a surgical instrument which has a capillary with an opening for discharging a medium. The calculation routine of the computer program is used to ascertain an actual value of the volume of the medium discharged through the opening of the capillary in the target area by comparing data of the target area in the 3D reconstruction of the object region volume (22) and/or by comparing scanning information of the target area obtained by scanning the object region volume (22) using the OCT device (20) prior to and while discharging the medium. The invention also relates to a computer program and to a method for determining the volume of a medium discharged in an object region (18) through an opening by means of a surgical instrument with a capillary.

Systems and methods for reconstructing medical images are disclosed. Measurement data, such as magnetic resonance (MR) data and positron emission tomography (PET) data, is received from an image scanning system. Attenuation maps are generated based on the PET data and a determined background level of radiation of the image scanning system. The background level of radiation can be caused by the radioactive decay of crystal material of the image scanning system. MR images are reconstructed based on the MR data. Further, a neural network, such as a deep learning neural network, is trained with the attenuation maps and the reconstructed MR images to determine attenuation map based on a reconstructed MR image. The trained neural network can be applied to MR data received for a patient to determine a corresponding attenuation map. A final image is generated based on PET data received for the patient and the determined attenuation map.

A list mode image reconstruction method includes a step of dividing list mode data into a plurality of subsets and a step of acquiring a subset balance coefficient based on the number of events in the plurality of subsets.