Abstract: While performing a tomosynthesis procedure, the breast of a patient is compressed between two compression elements to create an imaging condition. Foam is secured to the rigid substrate of a one of the compression elements. The patient's chest wall is aligned with the leading edge surface of the foam. The inner side of the breast is disposed proximate the lateral edge surface of the foam and the outer side of the breast is disposed proximate the outer lateral edge surface of the foam. A mid-plane is disposed between the inner and outer lateral edge surfaces of the foam. An interface connects a leading edge surface of the foam and compressive surfaces. A portion of the leading edge surface which is aligned with the mid-plane is incompletely compressed.

Abstract: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

Abstract: 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.

Abstract: While performing a tomosynthesis procedure, the breast of a patient is compressed between two compression elements to create an imaging condition. Foam is secured to the rigid substrate of a one of the compression elements. The patient's chest wall is aligned with the leading edge surface of the foam. The inner side of the breast is disposed proximate the lateral edge surface of the foam and the outer side of the breast is disposed proximate the outer lateral edge surface of the foam. A mid-plane is disposed between the inner and outer lateral edge surfaces of the foam. An interface connects a leading edge surface of the foam and compressive surfaces. A portion of the leading edge surface which is aligned with the mid-plane is incompletely compressed.

Abstract: An x-ray breast imaging system includes a breast support platform including an x-ray receptor, and an x-ray tube head. The x-ray tube head includes an x-ray source configured to emit an x-ray beam in a direction towards the x-ray receptor, and a collimator. A filter assembly including a plurality of filter slots selectively positionable adjacent to the collimator, and a specimen imaging filter disposed within a slot of the plurality of filter slots. The specimen imaging filter includes at least one aperture defined therein. The specimen imaging filter also blocks a portion of the emitted x-ray beam so that the at least one aperture defines a path of the emitted x-ray beam towards the x-ray receptor.

Abstract: A method of imaging a breast compressed with a foam paddle includes emitting an x-ray energy from an x-ray source towards the breast and the foam paddle having a plurality of upper markers and a plurality of lower markers, wherein the plurality of lower markers are movable relative to the upper markers. The x-ray energy is detected at a detector disposed opposite the breast from the x-ray source. An image of the compressed breast is generated based on the detected x-ray energy. At least one of the plurality of upper markers and at least one of the plurality of lower markers is identified in the image. A thickness of the compressed breast at a plurality of thickness locations is determined, wherein each of the plurality of thickness locations corresponds to at least one of the plurality of lower markers.

Abstract: 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.

Abstract: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

Abstract: Systems and methods for x-ray imaging a patient's breast in combinations of dual-energy, single-energy, mammography and tomosynthesis modes that facilitate screening for and diagnosis of breast abnormalities, particularly breast abnormalities characterized by abnormal vascularity.

Abstract: Techniques for patient positioning quality assurance prior to mammographic image acquisition are described. A system may include an x-ray source configured to expose human tissue to radiation. An x-ray detector module may be configured to detect the radiation and generate a pre-diagnostic image of the human tissue. A position analysis module may be configured to receive the pre-diagnostic image and determine whether the human tissue is positioned correctly based upon one or more predefined positioning criteria. A non-transitory computer-readable storage medium may be configured to store the results of the positional analysis module determination. A display may be configured to display the determination of the position analysis module within a user interface. Other embodiments are described and claimed.

Abstract: While performing a tomosynthesis procedure, the breast of a patient is compressed between two compression elements to create an imaging condition. Foam is secured to the rigid substrate of a one of the compression elements. The patient's chest wall is aligned with the leading edge surface of the foam. The inner side of the breast is disposed proximate the lateral edge surface of the foam and the outer side of the breast is disposed proximate the outer lateral edge surface of the foam. A mid-plane is disposed between the inner and outer lateral edge surfaces of the foam. An interface connects a leading edge surface of the foam and compressive surfaces. A portion of the leading edge surface which is aligned with the mid-plane is incompletely compressed.

Abstract: 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.

Abstract: A method and an apparatus for estimating a geometric thickness of a breast in mammography/tomosynthesis or in other x-ray procedures, by imaging markers that are in the path of x-rays passing through the imaged object. The markings can be selected to be visible or to be invisible when the composite markings/breast image is viewed in clinical settings. If desired, the contribution of the markers to the image can be removed through further processing. The resulting information can be used determining the geometric thickness of the body being x-rayed and thus setting imaging parameters that are thickness-related, and for other purposes. The method and apparatus also have application in other types of x-ray imaging.

Abstract: While performing a tomosynthesis procedure, the breast of a patient is compressed between two compression elements to create an imaging condition. Foam is secured to the rigid substrate of a one of the compression elements. The patient's chest wall is aligned with the leading edge surface of the foam. The inner side of the breast is disposed proximate the lateral edge surface of the foam and the outer side of the breast is disposed proximate the outer lateral edge surface of the foam. A mid-plane is disposed between the inner and outer lateral edge surfaces of the foam. An interface connects a leading edge surface of the foam and compressive surfaces. A portion of the leading edge surface which is aligned with the mid-plane is incompletely compressed.

Abstract: Systems and methods for x-ray imaging a patient's breast in combinations of dual-energy, single-energy, mammography and tomosynthesis modes that facilitate screening for and diagnosis of breast abnormalities, particularly breast abnormalities characterized by abnormal vascularity.

Abstract: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

Abstract: Techniques for patient positioning quality assurance prior to mammographic image acquisition are described. A system may include an x-ray source configured to expose human tissue to radiation. An x-ray detector module may be configured to detect the radiation and generate a pre-diagnostic image of the human tissue. A position analysis module may be configured to receive the pre-diagnostic image and determine whether the human tissue is positioned correctly based upon one or more predefined positioning criteria. A non-transitory computer-readable storage medium may be configured to store the results of the positional analysis module determination. A display may be configured to display the determination of the position analysis module within a user interface. Other embodiments are described and claimed.

Abstract: Examples of the present disclosure describe systems and methods for using AI to identify regions of interest (ROI) in medical images. In aspects, medical reports and images may be provided to a second environment. The second environment may use the medical report data/medical images to train a natural language processing (NLP)-based algorithm to identify the location in images of ROI described in the medical report data. The output of the NLP-based algorithm may be stored in an ROI repository in the second environment. After the NLP-based algorithm has been trained, a request to train a user-specific model may be received in a first environment. Data objects for the requested user-specific model may be provided to the second environment, which uses the ROI repository to train the model. The trained model may be provided to the first environment, where the trained user-specific model/algorithm may be tested and stored.

Abstract: Examples of the present disclosure describe systems and methods for using AI to identify regions of interest (ROI) in medical images. In aspects, medical reports and images may be provided to a first environment. The first environment may use the medical report data/medical images to train a natural language processing (NLP)-based algorithm to identify the location in images of ROI described in the medical report data. The output of the NLP-based algorithm may be stored in an ROI repository in the first environment. After the NLP-based algorithm has been trained, a request to train a user-specific model may be received in a second environment. Data objects for the requested user-specific model may be provided to the first environment, which uses the ROI repository to train the model. The trained model may be provided to the second environment, where the trained user-specific model/algorithm may be tested and stored.

Abstract: A multi-mode system and method for imaging a patient's breast with x-rays in one or more of a CT mode, a narrow-angle tomosynthesis mode, a wide angle tomosynthesis mode, and a mammography mode, using essentially the same equipment, on one or more compressions or immobilizations of the breast.