An apparatus and method for reducing the blurriness of tomographic (3D) images constructed from a gamma camera system with one or more VASH (variable-angle slant-hole) collimators. A conventional gamma camera with a VASH collimator exhibits a loss of spatial resolution from the fact that the gamma-ray is entering the detector element at an angle other than normal to the surface. This depth dependence of the spatial localization causes a blurring of the spatial resolution, which is dependent on the incident angle relative to the normal, on the thickness of the detector element and on the stopping length of the gamma-ray in the detector element material. The invention provides an apparatus and method for correcting the spatial location where the gamma ray is recorded to improve the spatial resolution of the system.

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.

Methods and systems are provided for reconstruction error assessment for an interventional tool utilized in an image guided interventional procedure. In one example, an error model based on a target lesion position within a tissue, one or more interventional tool parameters, and imaging system parameters may be utilized to estimate an expected reconstruction error for the interventional tool. In another example, when the interventional tool is within the tissue, the expected reconstruction error may be utilized along with observed tool shape and size to infer an actual tool position and shape within the tissue.

An image processing device includes at least one processor. The processor detects a specific structural pattern indicating a lesion candidate structure for a breast in a series of a plurality of projection images obtained by performing tomosynthesis imaging on the breast or in a plurality of tomographic images obtained from the plurality of projection images, synthesizes the plurality of tomographic images to generate a synthesized two-dimensional image, specifies a priority target region, in which the specific structural pattern is present, in the synthesized two-dimensional image, and performs determination regarding a diagnosis of a lesion on the basis of the synthesized two-dimensional image and the priority target region.

Various systems are provided for non-uniform thickness and/or sampling of slabs of the breast to present DBT acquisitions. A method for generating a patient image as a set of slabs representing an imaged object, the method comprising acquiring a tomosynthesis projection, reconstructing a series of slab images, each slab representing a portion of a breast, and a plurality of slabs of non-uniform thickness and/or non-uniform sampling in a 3D reconstructed domain defined by x-, y-, and z-axes.

A method for processing breast tomosynthesis images includes: obtaining multiple breast images, performing binarization operation and pooling operation to at least one marked breast image to obtain a lesion mask; training a convolutional neural network (CNN) according to the breast images and the lesion mask; obtaining multiple cropped images from the breast images of different layers according to a particular two-dimensional position, and inputting each cropped image into the CNN to obtain cropped heat maps; inputting the cropped heat maps into a recurrent neural network (RNN) in an order of the levels to obtain output heat maps, and combining the output heat maps to obtain a integrated heat map, and training the RNN according to the integrated heat map and the lesion mask.

The present disclosure provides a computerized method for a likelihood of malignancy in breast tissue of a patient. The method includes receiving, with a computer processor, an image of the breast tissue, providing the image of the breast tissue to a model including a trained neural network; the trained neural network being previously trained by training a first neural network, initializing a second neural network based on the first neural network, training the second neural network, and outputting the second neural network as the trained neural network, receiving an indicator from the model, and outputting a report including the indicator to at least one of a memory or a display.

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.

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.