Real Time Tomography, LLC

University of Pennsylvania

Johnny Kuo

Peter A Ringer

Susan Ng

Andrew D A Maidment

Raymond J Acciavatti

Predrag Bakic

Brian Lee

Albert Milani

Lucas Rodrigues Borges

Tristan Douglas Maidment

Vincent Yun Dong

Katherine Martling Hopkins

This document describes methods and systems for training a machine learning model to segment digital breast images into key regions of interest, and also for using the model on a new digital breast image to assess whether the breast image exhibits adequate image quality, report the image quality, and/or to use these data to reconstruct or process the new breast image.

Automated multi-class segmentation of digital mammogram

Systems and methods of enhanced display and viewing of three dimensional (3D) tomographic data acquired in tomosynthesis or tomography. A set of projection data is acquired with an image acquisition system and used to reconstruct enhanced 3D volume renderings that are viewed with motion, advanced image processing or stereotactically to assist in medical diagnosis. Various enhancements are provided for further processing the images, thereby providing additional features and benefits during image viewing.

Enhancements for displaying and viewing tomosynthesis images

A super-resolution digital tomosynthesis system for imaging an object including a source configured to emit penetrating particles toward an object; a detector configured to acquire a series of projection images of the object in response to the penetrating particles from the source; positioning apparatus configured to position the source and the detector; and an imaging system coupled to the source, the detector, and the positioning apparatus. The imaging system is configured to control the positioning apparatus to position the source in relation to the detector along a scan path and to change a distance between the source and the detector, control the source and the detector to acquire the series of projection images along the scan path with the distance change between the source and detector, and construct a tomographic volume exhibiting super-resolution from data representing the acquired series of projection images.

Super-resolution tomosynthesis imaging systems and methods

A contrast-enhanced digital tomosynthesis system with a source configured to emit penetrating particles toward an object, a detector configured to acquire a series of projection images of the object in response to the penetrating particles from the source, a positioning apparatus configured to position the source relative to the object and the detector, and an imaging system coupled to the source, the detector, and the positioning apparatus. The imaging system is configured to control the positioning apparatus to position the source and detector relative to the object, control the source and the detector to acquire the series of projection images, and construct a tomographic volume capable of exhibiting super-resolution morphology and contrast-enhancement arising from injection of an exogenous contrast agent from data representing the acquired series of projection images or a subset thereof.

Dynamic four-dimensional contrast enhanced tomosynthesis

Enhancements for displaying and viewing Tomosynthesis images

Tomosynthesis imaging methods, systems, and apparatus include a source to emit penetrating particles toward an object, a detector to acquire a series of projection images responsive to the penetrating particles, and a positioning system to position the source with respect to the object and the detector. An imaging system controls the positioning apparatus, source, and detector to acquire images along a primary scan direction with offsets in a secondary scan direction. An oscillatory velocity may be applied to the source to reduce focal spot blur. Tomographic volumes are constructed that are capable of exhibiting super-resolution from data representing the acquired series of projection images taking the primary scan direction and the offsets into consideration.

Growing community of inventors

Lancaster, PA, United States of America

Johnny Kuo