We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App

New Approach Combines Deep Learning and Physics to Fix Motion-Corrupted MRI Scans

By MedImaging International staff writers
Posted on 18 Aug 2023
Print article
Image: A deep learning model is capable of motion correction in brain MRI (Photo courtesy of MIT)
Image: A deep learning model is capable of motion correction in brain MRI (Photo courtesy of MIT)

MRI (Magnetic Resonance Imaging) is known for its superior ability to provide high-quality soft tissue contrast, making it a preferred choice over other imaging techniques like X-rays or CT scans. However, one of the challenges with MRI is its extreme sensitivity to even the smallest movements, leading to image artifacts. Such artifacts can obscure essential details, putting patients at risk of being misdiagnosed or receiving incorrect treatment. The process of an MRI scan varies in duration, ranging from a matter of minutes to an entire hour, depending on the specific images required. Even during the quickest scans, minor movements can greatly distort the resulting image. While in typical camera imaging, motion results in a localized blur, motion during an MRI scan can cause artifacts that corrupt the entire image. Some patients may be anesthetized or asked to control their breathing to minimize movement, but these solutions are not always feasible, especially in specific populations such as children or patients with psychiatric disorders.

Researchers at MIT (Cambridge, MA, USA) have risen to this challenge by creating a novel deep-learning model that can correct motion in brain MRI scans. This method combines physics-based modeling with deep learning techniques, enabling the construction of a motion-free image from corrupted data, without the need to alter the actual scanning process. The brilliance of this combined approach is its ability to ensure consistency between the image produced and the actual physical measurements of what is being depicted. If this balance is not maintained, the model could create "hallucinations"—images that look realistic but are physically and spatially incorrect. This could lead to even more misleading diagnoses. The application of an MRI free from motion artifacts would not only enhance patient outcomes but also has broad-reaching implications, especially for patients with neurological disorders causing involuntary movements, such as Alzheimer's or Parkinson's disease.

The impact of motion artifacts is not merely confined to patient diagnosis. An earlier study estimated that motion-related issues affect 15% of brain MRIs, leading to repeated scans or extended imaging sessions. This necessity for re-imaging translates into increased hospital costs per scanner. The researchers believe that their innovation could be further expanded. Future investigations might delve into more complex types of head movement or explore motion-related challenges in other parts of the body. For example, fetal MRI, which faces the problem of rapid and unpredictable motion, requires an approach that goes beyond simple translations and rotations. The development of a motion correction method for brain MRI scans marks a significant advancement in medical imaging that can improve diagnostic accuracy as well as reduce healthcare costs.

Related Links:

Gold Supplier
Conductive Gel
Gold Supplier
IMRT Thorax Phantom
CIRS Model 002LFC
Advanced Radiotherapy System
uRT-linac 306
Diagnostic Ultrasound System
DRE Crystal 4P

Print article
Sun Nuclear -    Mirion



view channel
Image: The AI model improves tumor removal accuracy during breast cancer surgery (Photo courtesy of UNC School of Medicine)

AI Model Analyzes Tumors Removed Surgically in Real-Time

During breast cancer surgery, the surgeon removes the tumor, also known as a specimen, along with a bit of the adjacent healthy tissue to ensure all cancerous cells are excised. This specimen is then X-rayed... Read more


view channel
Image: FloPatch is a revolutionary tool that facilitates real-time precision in IV fluid management in sepsis (Photo courtesy of Flosonics)

Wireless, Wearable Doppler Ultrasound Revolutionizes Precision Fluid Management in Sepsis Care

When a patient comes to the hospital with sepsis, administering intravenous (IV) fluids is usually the first course of action. However, too much IV fluid can do more harm than good, causing additional... Read more

Nuclear Medicine

view channel
Image: An AI model can evaluate brain tumors on PET (Photo courtesy of Freepik)

AI Model for PET Imaging Determines Patient Response to Brain Tumor Treatments

The assessment of changes in metabolic tumor volume (MTV) through PET scans using specific radiotracers like F-18 fluoroethyl tyrosine (FET) plays a vital role in evaluating the treatment response in patients... Read more

General/Advanced Imaging

view channel
Image: Annalise Enterprise CTB acts like a ‘second pair of eyes’ for radiologists (Photo courtesy of Annalise.ai)

Deep Learning System Boosts Radiologist Accuracy and Speed for Head CTs

Non-contrast computed tomography of the brain (NCCTB) is a commonly employed method for identifying intracranial pathology. Despite its frequent use, the complex scan outcomes are prone to being misunderstood.... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.