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.

Digital Breast Tomosynthesis allows for the acquisition of volumetric mammography images. The present invention allows for novel ways of viewing such images to detect microcalcifications and obstructions. In an embodiment a method for displaying volumetric images comprises computing a projection image using a viewing direction, displaying the projection image and then varying the projection image by varying the viewing direction. The viewing direction can be varied based on a periodic continuous mathematical function. A graphics processing unit can be used to compute the projection image and bricking can be used to accelerate the computation of the projection images.

An image acquisition unit acquires a plurality of projection images corresponding to a plurality of radiation source positions at the time of tomosynthesis imaging, the plurality of projection images being generated by causing an imaging apparatus to perform tomosynthesis imaging. A positional shift amount derivation unit derives a positional shift amount between the plurality of projection images based on body movement of the subject with a reference projection image generated at a radiation source position where an optical axis of the radiation emitted from the radiation source is perpendicular to a detection surface of the detection unit, among the plurality of projection images, as a reference. A reconstruction unit generates a tomographic image of at least one tomographic plane of the subject by reconstructing the plurality of projection images while correcting the positional shift amount.

A medical image processing apparatus and a medical image processing method are provided which are capable of clearly presenting a distal end of a medical device in a tomosynthesis image of an object under examination into which the medical device is inserted. The medical image processing apparatus handles a tomosynthesis image generated using a plurality of projection images obtained by imaging an object under examination in an angle range of less than 180 degrees, and includes: a storage section for pre-storing blur data at individual imaging space coordinates; and a correction section for correcting the tomosynthesis image using the blur data.

The present disclosure is directed to systems and methods of multiphase pseudo-continuous arterial spin labeling.

A processor detects a structure of interest from a plurality of tomographic images indicating a plurality of tomographic planes of an object. The processor selects a tomographic image from the plurality of tomographic images according to a frequency band in a region in which the structure of interest has been detected. The processor generates a composite two-dimensional image using the selected tomographic image in the region in which the structure of interest has been detected and using a predetermined tomographic image in a region in which the structure of interest has not been detected.

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.

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.

A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.