Bruce R. Rosen, M.D., Ph.D.

AIMBE College of Fellows Class of 2006
For extraordinary productivity, creativity, and leadership in the field of biomedical neuro-imaging.

Learning to see – New artificial intelligence technique dramatically improves the quality of medical imaging

Via EurekAlert | March 21, 2018

A radiologist’s ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost – increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning, enabling clinicians to acquire higher quality images without having to collect additional data. They describe the technique – dubbed AUTOMAP (automated transform by manifold approximation) – in a paper published today in the journal Nature.

“An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate,” says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper. “The conventional approach to image reconstruction uses a chain of handcrafted signal processing modules that require expert manual parameter tuning and often are unable to handle imperfections of the raw data, such as noise. We introduce a new paradigm in which the correct image reconstruction algorithm is automatically determined by deep learning artificial intelligence… Continue reading.