A combination unit generates a plurality of composite two-dimensional images from a plurality of tomographic images acquired by performing tomosynthesis imaging on an object using different generation methods. In this case, the combination unit generates a first composite two-dimensional image having a quality corresponding to a two-dimensional image acquired by simple imaging or a second composite two-dimensional image in which a structure included in the object has been highlighted as at least one of the plurality of composite two-dimensional images.

The disclosure relates to systems and methods for iterative reconstruction. Raw data detected from a plurality of angles by an imaging device may be obtained. A first seed image may be generated by performing a filtered back projection on the raw data. A first air mask may be determined by performing a minimum value back projection (BP) on the raw data. One or more images may be reconstructed by performing an iterative reconstruction based on the first seed image, the first air mask, and the raw data.

A method for establishing a three-dimensional tomosynthesis data record of a target volume from two-dimensional projection images recorded with a recording arrangement including an X-ray source and an X-ray detector in different recording geometries is provided. During or after a reconstruction step, a deconvolution technique is used for reducing image artifacts of the tomosynthesis data record occurring due to lacking information. The projection images are recorded along a linear recording trajectory of the X-ray source. The reconstruction and the use of the deconvolution technique take place in a plurality of different two-dimensional reconstruction planes that are spanned by the recording trajectory and, in each case, a definition point in the target volume.

A PET image reconstruction method, including: 1) injecting a PET radioactive tracer into a biological tissue, scanning by a PET device, and detecting and counting coincidence photons to obtain an original protection data matrix; 2) establishing a measurement equation model; 3) splitting the reconstruction problem into a first sub-problem and a second sub-problem; 4) solving the first sub-problem by a filtered back-projection layer, solving the second sub-problem by an improved denoising convolutional neural network, where the filtered back-projection layer and the improved denoising convolutional neural network are connected in series to form a filtered back-projection network (FBP-Net); 5) inputting original projection data into the FBP-Net, and using an image as a tag to adjust parameters of the FBP-Net to reduce an error between an output of the FBP-Net and the tag; and 6) inputting projection data to be reconstructed into the trained FBP-Net to obtain a desired reconstructed image.

A single-impulse panoramic photoacoustic computed tomography (SIP-PACT) system for small-animal whole-body imaging is disclosed. In addition, a dual-speed of sound image universal back-projection reconstruction method is disclosed. Further, a PACT system for imaging a breast of a subject is disclosed.

A system for monitored tomographic reconstruction, comprising: an x-ray generator configure to generate x-ray beams for scanning an object; detectors configured to capture a plurality of projections for each scan; at least one hardware processor; and one or more software modules that, when executed by the at least one hardware processor, receive the plurality of projections from the detectors and as each of the plurality of projections is received, generate a partial reconstruction, and make a stopping decision with respect to whether or not another projection should be obtained based on a stopping problem and that defines when a reconstructed image quality is sufficient with respect to the expended cost as determined by a stopping rule.

An attention mechanism-based low-dose dual-tracer PET reconstruction method. The method achieves low-dose dual-tracer PET image reconstruction by an attention mechanism-based convolution network model, and estimates the standard dose and separates dual-tracer PET signals in a sinogram. With the help of deep learning, a feature extraction tool, the method can reconstruct standard-dose single-tracer PET images in a PET Low-Dose Dual-Tracer Sinogram.

A method for presenting a mapping pseudo-hologram using individual video signal output of a real-time engine according to an embodiment of the present disclosure includes: (a) creating a partial viewpoint video including a character of a virtual reality content corresponding to a first user through a camera positioned at any one point in the virtual reality content; (b) creating a hologram video including at least one of objects around the character in the partial viewpoint video; and (c) projecting the hologram video onto a hologram screen placed between the first user and a second user different from the first user, and in the process (c), the hologram video is projected to be overlaid on the first user when the second user sees the first user.

An apparatus and method for obtaining a thick-slice image from tilted thin-slice computed-tomography (CT) projection data. Tilted CT projection data is obtained for a series of projection planes, wherein the projection planes are parallel for all scans, and the translation direction between CT scans is not orthogonal to the projection planes (i.e., the projection planes are tilted relative to the translation direction between CT scans). Thin-slice images are reconstructed from the respective CT scans, and then grouped into thick-slice groupings. An offset results among the thin-slice images within a thick-slice grouping due to the tilt of the projection planes. This offset is compensated by interpolating and resampling the thin-slice images onto non-offset pixel grids. The interpolated and resampled thin-slice images are then averaged pixel-by-pixel to obtain thick-slice images having the same tilt angle as the thin-slice images.

Methods and systems of using X-ray radiation to irradiate X-ray sensitive biomolecules to allow for specific control over the behavior of cells via the X-ray irradiation are provided. The systems and methods are influenced by the field of optogenetics, which uses visible light instead of X-ray radiation. X-ray stimulation penetrates both bone and soft tissue with very little attenuation and can be performed without any physical contact with the sample. Image reconstruction methods using deep learning are also provided. A deep learning algorithm can be used to obtain a reconstructed image from raw data obtained via medical imaging, either with or without first performing a conventional algorithm.