University of California

Corey W Arnold

Dieter R Enzmann

Fabien Scalzo

King Chung Ho

Matthew S Brown

Hyun J Kim

William Hsu

Mahesh B Nagarajan

Alex A T Bui

Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance ('MR') imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional ('4D') convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks

A probability map of prostate tumor location is generated and displayed in response to receiving anatomic diagnostic medical imaging, such as from magnetic resonance (MR) scanning. A registration process is performed on the images in relation to a model built of prostate anatomy across different subjects in an enhanced prostate template. A probability map is created of tumor locations followed by transforming the imaging to incorporate the probability map and output a resultant image.

Apparatus and method for determining the spatial probability of cancer within the prostate

A system is disclosed using a data-driven approach to objectively measure the diagnostic accuracy and value of diagnostic imaging reports using data captured routinely as part of the electronic health record. The system further utilizes the evaluation of the diagnostic accuracy of individual radiologists (imagers), subspecialty sections, modalities, and entire departments based on a comparison against a 'precision diagnosis' rendered by other clinical data sources such as pathology, surgery, laboratory tests, etc.

Automated quality control of diagnostic radiology

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Los Angeles, CA, United States of America

Corey W Arnold