Disclosed herein is a method, comprising: for i=1, . . . , M, sending a pencil radiation beam (i) toward an image sensor, wherein the pencil radiation beam (i) is incident on an incident region (i) on the image sensor, wherein the pencil radiation beam (i) is aimed at a target region (i) on the image sensor, wherein M is a positive integer, wherein the image sensor comprises active areas spatially discontinuous from each other, and wherein the incident regions (i), i=1, . . . , M and the target regions (i), i=1, . . . , M are on the active areas; and for i=1, . . . , M, determining an offset (i) between the incident region (i) and the target region (i).

Methods and systems for identifying internal motion of a breast of a patient during an imaging procedure. The method may include compressing the breast of the patient in a mediolateral oblique (MLO) position. During compression of the breast, a first tomosynthesis MLO projection frame for a first angle with respect the breast is acquired and a second tomosynthesis MLO projection frame for a second angle with respect to the breast is acquired. Boundaries of the pectoral muscle are identified in the projection frames and boundary representations are generated. A difference between the first representation and the second representation is determined. A motion score is then generated based on at least the difference between the first representation and the second representation.

A processor generates a structure-highlighted synthesized two-dimensional image from a plurality of tomographic images and detects a structure of interest from the plurality of tomographic images and the structure-highlighted synthesized two-dimensional image. The processor sets at least some of the plurality of tomographic images as storage-required images or non-storage-required images according to a result of comparison between the structure of interest detected from the plurality of tomographic images and the structure of interest detected from the structure-highlighted synthesized two-dimensional image.

A processor is configured to set whether or not to generate a structure-highlighted synthesized two-dimensional image from a plurality of tomographic images and to set at least some of the plurality of tomographic images as storage-required images or non-storage-required images according to a result of the setting of whether or not to generate the structure-highlighted synthesized two-dimensional image.

An image interpretation support apparatus includes: an acquisition unit that acquires a plurality of projection images obtained by tomosynthesis imaging in which a radiation is irradiated to a breast from different irradiation angles by a radiation source and a projection image is captured at each irradiation angle by a radiation detector; a first generation unit that generates a plurality of tomographic images on each of a plurality of tomographic planes of the breast from the plurality of projection images; a second generation unit that generates a synthetic two-dimensional image from a plurality of images among the plurality of projection images and the plurality of tomographic images; a detection unit that detects an object of interest candidate region estimated to include an object of interest from the synthetic two-dimensional image; and a determination unit that determines whether or not the object of interest is included in the object of interest candidate region on the basis of the plurality of tomographic images.

Provided herein are diffractometer-based global in situ diagnostic systems and uses thereof. The systems may comprise one or more tissue diffractometers that are configured for acquiring in situ diffraction data for a subject, e.g., a patient, and that are operatively coupled to a computer database over a network. The one or more tissue diffractometers may be configured for transfer of data such as image data, diffraction pattern data, subject data, or any combination thereof to the computer database over the network. The systems may further comprise one or more computer processors operatively coupled to the tissue diffractometers, which computer processors may be configured to receive the data from the tissue diffractometers, transmit the data to the computer database, and process the data using a data analytics algorithm which may provide a computer-aided diagnostic indicator for the individual subject.

A method for processing breast tissue image data includes obtaining image data of a patient's breast tissue, processing the image data to generate a set of image slices, the image slices collectively depicting the patient's breast tissue; feeding image slices of the set through each of a plurality of object-recognizing modules, each of the object-recognizing modules being configured to recognize a respective type of object that may be present in the image slices; combining objects recognized by the respective object-recognizing modules to generate a synthesized image of the patient's breast tissue; and displaying the synthesized image.

Systems and methods for breast x-ray tomosynthesis that enhance spatial resolution in the direction in which the breast is flattened for examination. In addition to x-ray data acquisition of 2D projection tomosynthesis images ETp1 over a shorter source trajectory similar to known breast tomosynthesis, supplemental 2D images ETp2 are taken over a longer source trajectory and the two sets of projection images are processed into breast slice images ETr that exhibit enhanced spatial resolution, including in the thickness direction of the breast. Additional features include breast CT of an upright patient's flattened breast, multi-mode tomosynthesis, and shielding the patient from moving equipment.

One or more example embodiments provides a system and a method for differentiating a tissue of interest from another part of a medical scanner image, in particular pectoral muscle tissue from breast tissue in an X-ray mammography image. The method comprises providing a medical scanner image; inputting input data into a trained artificial neural network, the input data being based on the provided medical scanner image; generating, by the trained artificial neural network, output data based on the input data, the output data indicating a one-dimensional borderline between at least a part of the tissue of interest and the at least one other part of the medical scanner image; and outputting an output signal comprising or based on the generated output data.

A three-dimensional model for a patient fixation device that serves to immobilize at least a portion of a particular patient when capturing CT image information of that patient is accessed and then registered with the pixels that correspond to the patient fixation device in the CT image. The model can specify rules of movement for each of a plurality of structural elements that comprise the patient fixation device and that are capable of movement relative to one another. By one approach the aforementioned registration occurs on a part-by-part basis for each of the structural elements. Following registration, the CT image can be automatically segmented.