Compared to other imaging modalities like X-rays or CT scans, MRI scans provide high-quality soft tissue contrast. Unfortunately, MRI is highly sensitive to motion, with even the smallest of movements resulting in image artifacts. These artifacts put patients at risk of misdiagnoses or inappropriate treatment when critical details are obscured from the physician. But researchers at MIT may have developed a deep learning model capable of motion correction in brain MRI.
"Motion is a common problem in MRI," explains Nalini Singh, an Abdul Latif Jameel Clinic for Machine Learning in Health -affiliated Ph.D. student in the Harvard-MIT Program in Health Sciences and Technology and lead author of the paper."It's a pretty slow imaging modality."
However, these measures often cannot be taken in populations particularly susceptible to motion, including children and patients with psychiatric disorders.preprint server and titled"Data Consistent Deep Rigid MRI Motion Correction," was recently awarded best oral presentation at the Medical Imaging with Deep Learning conference in Nashville, Tennessee.
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