Compared to more traditional imaging modalities like X-rays and CT scans, MRI scans provide better soft tissue contrast. Unfortunately, because MRI is so sensitive to motion, even minute movements might result in obvious distortions in the final image. Doctors run the danger of mistreating patients when these artifacts obscure critical information. A deep learning model has been created, though, to correct motion in MRI scans of the brain.
MRI procedure
An MRI session may vary from a few minutes to an hour, depending on the type of pictures being taken. Even quick scans can result in little positional changes that have a big effect on the final image. However, unlike the localized blur that is frequently seen in camera imaging, MRI motion frequently manifests as aberrations that can distort the entire image. To stay still, patients can be given anesthesia or instructed to breathe more quickly. Although people in these categories frequently lack access to these safety measures, children and people with mental illnesses are particularly susceptible to the impacts of motion.
By preventing the model from producing “hallucinations” or visuals that appear realistic but are physically and spatially wrong, this combined approach is essential for ensuring physical and spatial correctness in diagnostic conclusions.
Neurological conditions
An MRI without motion anomalies would be extremely helpful for patients with neurological diseases like Parkinson’s and Alzheimer’s, which cause uncontrollable movement. According to studies from the University of Washington Department of Radiology, motion affects 15% of brain MRIs. Any motion in an MRI requires repeated scans or imaging sessions to obtain images of sufficient quality for diagnosis, costing hospitals an average of $115,000 annually per scanner.
Evaluation
Although the evaluation is done using simulated data, the researchers demonstrate that their approach generalizes to k-space data seen in the actual world. Through-plane motion is a significant source of an artifact that the researchers disregard. Additionally, less-studied elements including intra-shot motion, spin history, and signal deterioration during the FSE echo train may have an impact on their technique.
Future research will additionally investigate, model, and correct these supplemental motion effects without compromising reconstruction quality to maintain data consistency in larger-scale, clinical kspace datasets. The researchers are hoping that their approach will work with MRI sequences that employ various kspace acquisition patterns and contrasts. Beyond magnetic resonance imaging, their method for gathering forward model uncertainty and creating physically consistent reconstructions has potential uses.
