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

Many patients with severe coronavirus disease 2019 (COVID-19) remain unresponsive after surviving critical illness. Investigators led by a team at Massachusetts General Hospital (MGH) now describe a patient with severe COVID-19 who, despite prolonged unresponsiveness and structural brain abnormalities, demonstrated functionally intact brain connections and weeks later he recovered the ability to follow commands. The case, which is published in the Annals of Neurology, suggests that unresponsive patients with COVID-19 may have a better chance of recovery than expected.

In addition to performing standard brain imaging tests, the team took images of the patient’s brain with a technique called resting-state functional magnetic resonance imaging (rs-fMRI), which evaluates the connectivity of brain networks by measuring spontaneous oscillations of brain activity. The patient was a 47-year-old man who developed progressive respiratory failure, and despite intensive treatment, he fluctuated between coma and a minimally conscious state for several weeks… Continue reading.

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.