A method for immersively displaying a scanned environment of a region to a set of users in a training environment wearing augmented reality head display units. The training environment includes a pseudo-GPS system, which allows position tracking over time. This enables rehearsing military operations before they occur.

The present disclosure provides a method for determining a malignancy likelihood score for breast tissue of a patient. The method includes receiving a plurality of two-dimensional images of the breast tissue, the two-dimensional images being derived from a three-dimensional image of the breast tissue, for each two-dimensional image, providing the two-dimensional image to a first model including a first trained neural network, and receiving a number of indicators from the first model, each indicator being associated with a two-dimensional image included in the plurality of two-dimensional images, generating a synthetic two-dimensional image based on the number of indicators and at least one of the plurality of two-dimensional images, providing the synthetic two-dimensional image to a second model including a second trained neural network, receiving a malignancy likelihood score from the second model, and outputting a report including the malignancy likelihood score to at least one of a memory or a display.

Embodiments discussed herein facilitate determination of whether lesions are benign or malignant. One example embodiment is a method, comprising: accessing medical imaging scan(s) that are each associated with distinct angle(s) and each comprise a segmented region of interest (ROI) of that medical imaging scan comprising a lesion associated with a first region and a second region; providing the first region(s) of the medical imaging scan(s) to trained first deep learning (DL) model(s) of an ensemble and the second region(s) of the medical imaging scan(s) to trained second DL model(s) of the ensemble; and receiving, from the ensemble of DL models, an indication of whether the lesion is a benign architectural distortion (AD) or a malignant AD.

Methods and systems for identifying a region of interest in breast tissue use artificial intelligence to confirm that a target lesion identified during initial imaging the breast tissue has been identified in a subsequent imaging session. A computing system operating a lesion matching engine uses a machine learning classifier algorithm trained on cases of initial and subsequent two-dimensional or three-dimensional images of lesions. The lesion matching engine analyzes a target lesion identified with initial and a potential lesion identified with current imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

An object detection method for a 3D mammogram is disclosed. The object detection method comprises steps of: controlling N filters to execute a filtering computation in the 3D mammogram respectively to generate N 3D filtering images; computing a difference variation among the plurality of voxels to obtain a blurriness value of the plurality of voxels; using the blurriness value of the plurality of voxels in a decision module to execute a plurality of first decision operators to generate a plurality of first decision results, and using one of the plurality of first decision results to execute the plurality of second decision operators to generate a plurality of second decision results; and executing a final decision operator by using the plurality of first decision results and the plurality of second decision results to generate a detection object of the 3D mammogram.

A method of generating a template image includes: receiving an input from a user representing identifications of an object in different respective slices of a volumetric image; using the input to determine a volume-of-interest (VOI) that includes voxels in a subset of the volumetric image; and determining the template image using at least some of the voxels in the VOI, wherein the act of determining the template image comprises performing a forward projection of the at least some of the voxels in the VOI using a processor. An image processing method includes: obtaining a volumetric image; performing forward projection of voxels in the volumetric image from different positions onto a first plane using a processor; and summing projections on the first plane resulted from the forward projection from the different positions to create a first image slice in the first plane.

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.

The information processing device includes a processor. The processor performs: a reconstruction process of reconstructing a plurality of tomographic images from a plurality of projection images obtained by tomosynthesis imaging that irradiates an object with radiation from a radiation source at a plurality of radiation source positions; a synthesis process of synthesizing the plurality of tomographic images to generate a synthesized two-dimensional image as a projection image based on a virtual radiation source position; an anatomical structure extraction process of extracting anatomical structures from a projection image corresponding to a radiation source position closest to the virtual radiation source position among the plurality of radiation source positions; and a display process of displaying an extraction result of the anatomical structures on a display device together with the synthesized two-dimensional image.

Embodiments of the present disclosure may disclose a method for improving CT image quality. The method for improving the CT image quality may include obtaining SFS data. The SFS data may include SFS scan data or an SFS image. The SFS scan data may be acquired by a CT device in an SFS state. The SFS image may be generated by reconstructing scan data acquired by the CT device via scanning in the SFS state. The method may include generating a corresponding optimized image by processing the SFS data based on an image quality optimization model. The image quality optimization model may be a machine learning model. The present disclosure may simulate the SFS image as an FFS image using a deep neural network model, thereby improving a resolution of the SFS image and reduce artifacts in the SFS image.

A method and image generating unit are for capturing at least two tomosynthesis images of an object undergoing examination offset by an angle of examination. In an embodiment the method includes a first and second capture of a plurality of first projection images along the linear trajectory, via X-ray source and the X-ray detector capturing the object undergoing examination in a first plane of capture and in a second capture plane different from the first capture plane, the first and second capture planes forming the angle of examination; determination of a first slice image dataset based upon the first projection images and of a second slice image dataset based upon the second projection images; and registration of the first slice image dataset and the second slice image dataset.