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:
Abstract systems and methods of biopsy control include reconstructing a 3D volume from a plurality of tomosynthesis projection images and producing a plurality of synthetic stereo images from the plurality of tomosynthesis projection images. At least the synthetic stereo images are presented on a graphical display to a clinician to facilitate at least one input of a biopsy location for biopsy control.
Abstract:
Method and system for obtaining images of an object of interest using a system comprising an x-ray source facing a detector. The method and system enable the acquiring of a plurality of 2D projection images of the object of interest in a plurality of orientations. A selected 2D projection image such as the zero projection of the plurality of projections can be enhanced by using at least a subset of the plurality of tomosynthesis projection images. The obtained enhanced 2D projection image is displayed for review.
Abstract:
A system and method for the detection of ROIs in images obtained of a breast or other tissue of a patient significantly improves the speed and precision/accuracy of navigation between multimodality 2D and 3D images. In the system and method, images of the tissue are obtained in a DBT acquisition to generate a synthetic 2D image of the imaged tissue and in a 3D, e.g., ultrasound, image acquisition. The 2D image generation process creates a synthetic 2D image that embed a navigation map correlating pixels in the 2D images to sections of the 3D ultrasound volume, such as via a registration between the 3D ultrasound volume and a 3D volume created using the DBT image data. When a synthetic 2D image is reviewed, an ROI on the 2D image is selected and the system will additionally present the user with the section of the 3D volume containing that ROI.
Abstract:
Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.
Abstract:
A system and method for the detection of ROIs in images obtained of a breast or other tissue of a patient significantly improves the speed and precision/accuracy of navigation between multimodality 2D and 3D images. In the system and method, images of the tissue are obtained in a DBT acquisition to generate a synthetic 2D image of the imaged tissue and in a 3D, e.g., ultrasound, image acquisition. The 2D image generation process creates a synthetic 2D image that embed a navigation map correlating pixels in the 2D images to sections of the 3D ultrasound volume, such as via a registration between the 3D ultrasound volume and a 3D volume created using the DBT image data. When a synthetic 2D image is reviewed, an ROI on the 2D image is selected and the system will additionally present the user with the section of the 3D volume containing that ROI.
Abstract:
Method and system for obtaining tomosynthesis and material decomposition images of an object of interest using a system comprising an x-ray source facing a detector. The method comprises generating a 2D material decomposition image of an object of interest from at least two sets of acquisitions. Each set is performed at a different energy spectrum and comprises at least one projection image or a plurality of projection images acquired at different x-ray source angulation positions, and the 2D material decomposition image can be generated for a predetermined orientation selected from one of said different x-ray source angulation positions or from a virtual orientation. At least one of the plurality of 3D volume portion images and/or the 2D contrast enhanced material decomposition image are displayed for review by a health care professional.
Abstract:
A method and system for obtaining images of an object of interest using a system comprising an X-ray source facing a detector. The method and system enable the acquiring of a plurality of 2D projection images of the object of interest in a plurality of orientations. A selected 2D projection image such as the zero projection of the plurality of projections can be enhanced by using at least a subset of the plurality of tomosynthesis projection images. The obtained enhanced 2D projection image is displayed for review.
Abstract:
A method for obtaining tomosynthesis images of an object of interest using an imaging system, wherein the imaging system comprises an X-ray source arranged to facing a detector on which the object of interest is positioned. The method comprises acquiring a plurality of projected 2D images of the object of interest in a plurality of orientations identified relative to a perpendicular to the detector, wherein a zero orientation is closest to the perpendicular and applying at least one filter to the acquired projected 2D images to obtain filtered, projection images of the object of interest. The method further comprises determining a reconstruction slice of the object of interest from the backprojection of at least two of the filtered projections, the set of reconstruction slices being the filtered, reconstructed volume of the object of interest, wherein a filter used on the 2D projection images is an adaptive filter.
Abstract:
Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.