An object of the present invention is to provide an analysis method capable of analyzing time-series images by a method simpler than ever. A computer program that is an application example of the present invention is a computer program for an image processing device including a storage unit that stores therein image data including time-series computed tomography (CT) images in a plurality of frames, of an organ of a subject captured after a contrast medium has been administered causes the image processing device to execute: a first step of determining a change-over-time of a CT value on the basis of image data including CT images in the plurality of frames; a second step of determining a predetermined slope that is a slope of the CT value with respect to a predetermined time on the basis of a change-over-time of the CT value determined in the first step; and a third step of approximating a change-over-time of the CT value with a predetermined function on the basis of the predetermined slope determined in the second step.

A method for calculating coronary artery fractional flow reserve includes determining myocardial volume by extracting myocardial images; locating a coronary artery inlet and accurately segmenting coronary arteries; generating a grid model required for calculation by edge detection of coronary artery volume data; determining myocardial blood flow in a rest state and CFR by non-invasive measurement; calculating the total flow at the coronary artery inlet in a maximum hyperemia state; determining the flow in different blood vessels in the coronary artery tree in the maximum hyperemia state and then determining flow velocity V.sub.1 in the maximum hyperemia state; using V.sub.1 as the flow velocity at the coronary artery inlet and calculating a pressure drop ΔP from the coronary artery inlet to a distal end of a coronary stenosis, and a mean intracoronary pressure Pd at the distal end of the stenosis P.sub.d=P.sub.a−ΔP, and calculating fractional flow reserve.

The present disclosure is related to systems and methods for motion signal recalibration. The method includes obtaining a motion signal of a subject based on positron emission tomography (PET) data of the subject. The motion signal may represent a plurality of motion cycles. The method includes determining a distribution of the motion cycles. The distribution of the motion cycles may indicate a probability that each motion cycle of the plurality of motion cycles corresponds to an actual motion cycle. The method includes correcting the motion cycles of the motion signal based on the distribution of the motion cycles to obtain corrected motion cycles. The method includes reconstructing a PET image by gating the PET data based on the corrected motion cycles.

A method for processing an unmatched low-dose X-ray computed tomography (CT) image using a neural network and an apparatus therefor are provided. The method includes receiving a low-dose X-ray CT image and removing noise from the low-dose X-ray CT image using a unsupervised learning based neural network learned using unmatched data to reconstruct a routine-dose X-ray CT image corresponding to the low-dose X-ray CT image.

The invention relates to an imaging method for radiologically guiding an instrument during medical interventions on an object (106, 203, 302) comprising: a) providing a first image (501, 602, 706, 806, 906, 1006, 1106) of said object followed by b) providing updated images on-the-fly during the intervention to an operator by measuring an undersampled set of projections of said object (106, 203, 302) and reconstructing said updated image based on changes between said first image (501, 602, 706, 806, 906, 1006, 1106) or an update of said first image (602) and said undersampled set of projections. The invention further relates to particular uses of the method and a system for radiologically guiding medical interventions on an object (106, 203, 302) according to the method, comprising—means to provide a first image (501, 602, 706, 806, 906, 1006, 1106) of the object; —an imaging apparatus measuring undersampled sets of projections; —processing means in communication with the imaging apparatus for providing updated images on-the-fly during the intervention by reconstructing said updated image based on changes between said first image (501, 602, 706, 806, 906, 1006, 1106) or an update of said first image (602) and said undersampled set of projections.

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.

A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

The present disclosure relates to methods, systems, and non-transitory computer readable mediums for reconstructing an image. Image data may be obtained, wherein the image data may be generated by a detector array. A weighting window may be determined based on at least one parameter relating to the detector array. A first set of data may be determined based on the image data and the weighting window. An objective function associated with a target image may be determined based on the first set of data, wherein the objective function may include a first model, the first model may represent a difference between the target image and the first set of data, and the first model may be identified based on the first set of data. The target image may be reconstructed by performing a plurality of iterations based on the objective function.

Various systems and methods for generating images are provided. In some embodiments, the techniques can include acquiring a medical image and an associated motion characterization. The motion characterization can then be used to generate a plurality of gated image data sets, sorted by phase in the motion cycle. A new amplitude-based motion characterization curve is derived from the association of phases with amplitude-based characteristics in the phase gated images. This newly derived amplitude-based motion characterization curve can then be used to re-sort data according to amplitude-based gating techniques known in the field or with data driven optimization techniques.

A method for performing a multi-bed scan includes receiving scanner-specific information including scanner sensitivity and receiving patient-specific information including attenuation. An attenuation-weighted sensitivity profile is calculated based on the scanner sensitivity and the attenuation. Individual bed scan times for each bed in a multi-bed study is calculated based on the attenuation-weighted sensitivity profile and the multi-bed scan is performed using the calculated individual bed scan times.