Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: An imaging system, such as a DBT system, capable of providing an operator of the system with information concerning the location, magnitude and direction of motion detected by the system during performance of the scan to enhance image processing. The imaging system provides the motion information to the operator directly in conjunction with the images processed by the imaging system thereby providing the operator with sufficient information for decisions regarding the need for additional images for completing the scan with the imaging system before the patient is discharged, or even before the breast is decompressed.

Abstract: Methods and systems for deep learning based image reconstruction are disclosed herein. An example method includes receiving a set of imaging projections data, identifying a voxel to reconstruct, receiving a trained regression model, and reconstructing the voxel. The voxel is reconstructed by: projecting the voxel on each imaging projection in the set of imaging projections according to an acquisition geometry, extracting adjacent pixels around each projected voxel, feeding the regression model with the extracted adjacent pixel data to produce a reconstructed value of the voxel, and repeating the reconstruction for each voxel to be reconstructed to produce a reconstructed image.

Abstract: 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.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: 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.

Abstract: The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Abstract: An imaging system, such as a DBT system, capable of providing an operator of the system with information concerning the location, magnitude and direction of motion detected by the system during performance of the scan to enhance image processing. The imaging system provides the motion information to the operator directly in conjunction with the images processed by the imaging system thereby providing the operator with sufficient information for decisions regarding the need for additional images for completing the scan with the imaging system before the patient is discharged, or even before the breast is decompressed.

Abstract: An imaging system, such as a DBT system, capable of providing an operator of the system with information concerning the location, magnitude and direction of motion detected by the system during performance of the scan to enhance image processing. The imaging system provides the motion information to the operator directly in conjunction with the images processed by the imaging system thereby providing the operator with sufficient information for decisions regarding the need for additional images for completing the scan with the imaging system before the patient is discharged, or even before the breast is decompressed.

Abstract: An apparatus for controlling medical equipment is provided. The apparatus includes patient positioning equipment, and a control system coupled to the patient positioning equipment. The control system is configured to control a function of the medical equipment.

Abstract: A system for imaging biopsy samples obtained from a patient includes: a radiation source; a radiation detector having a surface that defines a first imaging region, a second imaging region, and a third imaging region; and a collimator having a body defining an opening and selectively adjustable between a first imaging position and a second imaging position. The first imaging position allows radiation rays to pass from the radiation source, through the opening, and into the second imaging region while restricting the radiation rays from passing into the first imaging region when the radiation source is in a first scanning position. The second imaging position allows radiation rays to pass from the radiation source, through the opening, and into the third imaging region while restricting the radiation rays from passing into the first imaging region when the radiation source is in a second scanning position.

Abstract: An imaging system, such as a DBT system, is provided that is capable of providing processing of an image from initial generation/reconstruction of the image through the review of the image by a radiologist or other practitioner that produces images optimized for the particular task/process performed using the images. The task/review that the radiologist is performing on the processed or reconstructed image can be modeled with regard to the initial generation of the processed or reconstructed images from the raw images or the model considerations can be retroactively applied and back propagated through the image processing performed by the system. These modeling considerations that can take the form of an indicator of clinical performance is/are used by the system to optimize and produce an image(s) that best represents the items in the image necessary for an accurate diagnosis of the patient when reviewed by the radiologist.

Abstract: A mammography apparatus includes a support plate for supporting a breast of a patient, a compression plate movable toward and away from the support plate for compressing the breast against the support plate, and a controller configured to control movement of the compression plate toward and away from the support plate. The controller is configured to adjust at least one of a rate of compression and a pressure applied to the breast based on a measurement of at least one of a diastolic pressure and a systolic pressure of the patient taken during at least one of a compression phase and a clamping phase of the mammography apparatus.

Abstract: Methods and systems for deep learning based image reconstruction are disclosed herein. An example method includes receiving a set of imaging projections data, identifying a voxel to reconstruct, receiving a trained regression model, and reconstructing the voxel. The voxel is reconstructed by: projecting the voxel on each imaging projection in the set of imaging projections according to an acquisition geometry, extracting adjacent pixels around each projected voxel, feeding the regression model with the extracted adjacent pixel data to produce a reconstructed value of the voxel, and repeating the reconstruction for each voxel to be reconstructed to produce a reconstructed image.

Abstract: Methods and systems for deep learning based image reconstruction are disclosed herein. An example method includes receiving a set of imaging projections data, identifying a voxel to reconstruct, receiving a trained regression model, and reconstructing the voxel. The voxel is reconstructed by: projecting the voxel on each imaging projection in the set of imaging projections according to an acquisition geometry, extracting adjacent pixels around each projected voxel, feeding the regression model with the extracted adjacent pixel data to produce a reconstructed value of the voxel, and repeating the reconstruction for each voxel to be reconstructed to produce a reconstructed image.

Abstract: A method of obtaining x-ray images includes controlling operation of an automatic injector to inject a contrast agent into a patient at a predetermined time, synchronizing operations of an x-ray imaging system with the operation of the automatic injector, and obtaining images of a region of interest of the patient during arterial and diffusion phases of the contrast agent.

Abstract: Apparatus, systems, and methods for computer-aided detection are disclosed and described. An example apparatus includes at least one processor and a memory. The example memory includes instructions which, when executed, cause the at least one processor to at least: associate first patient data and first outcome data according to a set of association rules; train a processing model using machine learning and the associated first patient data and first outcome data; generate a computer-aided decision processing algorithm using the processing model; update the computer-aided decision processing algorithm based on at least one of second patient data or second outcome data received from a cloud infrastructure; and deploy the updated computer-aided decision processing algorithm to be applied to third patient data.

Abstract: A mammography apparatus includes a support plate for supporting a breast of a patient, a compression plate movable toward and away from the support plate for compressing the breast against the support plate, and a controller configured to control movement of the compression plate toward and away from the support plate. The controller is configured to adjust at least one of a rate of compression and a pressure applied to the breast based on a measurement of at least one of a diastolic pressure and a systolic pressure of the patient taken during at least one of a compression phase and a clamping phase of the mammography apparatus.

Abstract: An apparatus for controlling medical equipment is provided. The apparatus includes patient positioning equipment, and a control system coupled to the patient positioning equipment. The control system is configured to control a function of the medical equipment.