Intro Sentence:
UCLA researchers in the Department of Radiology have developed a novel method for obtaining simultaneous perfusion and functional MRI data from a single MRI acquisition to improve the diagnosis, monitoring, and management of brain tumors and stroke.
Background:
Advanced magnetic resonance imaging (MRI) techniques are crucial for the diagnosis and management of neurological conditions. In brain tumor and stroke, for example, dynamic susceptibility contrast (DSC) perfusion MRI is performed with the use of contrast agents to quantify brain vascularity, blood flow, and other measures of perfusion. Separately, resting-state functional MRI, based on the principles of blood oxygenation level dependent (BOLD)-based MR contrast mechanisms, is an emerging tool to understand insights into brain activity and function. While resting-state fMRI is a potentially valuable tool for patient care and understanding neurological function, it currently requires fMRI-trained neuroradiologists and MRI technologists, lengthy or complicated data processing, and additional scan time. These limitations render the imaging inaccessible to most patients and limit it to select cases at top-tier academic medical centers. There is a clear need for a method to estimate resting-state fMRI data, potentially using standard-of-care MRI techniques, to enable widespread availability.
Innovation:
Researchers led by Dr. Benjamin Ellingson have developed a method that allows technicians to obtain simultaneous perfusion and fMRI data in the same patients. This method operates by modeling and subsequently removing the perfusion signal created by the contrast agent. This improvement enables the collection of a “pseudo” resting-state fMRI signal simultaneously with standard-of-care perfusion MRI, potentially allowing for concurrent estimation of perfusion-based measurements (e.g. blood volume, blood flow, mean transit time, percent signal recovery) along with resting-state fMRI measurements that relate to neurological function (e.g. network-based functional connectivity, seed-to-voxel connectivity, BOLD asynchrony, etc.). Thus, thus method has the potential to provide a new standard of care for MRI exams involving patients with brain tumors, stroke, and other neurological diseases that require perfusion MRI evaluations.
Potential Applications:
• Neurological disorders (brain tumors, stroke, traumatic brain injury, cognitive impairment, Alzheimer’s disease)
• Cognitive neuroscience
Advantages:
• Cost effectiveness
• Shorter scan times
• Clinical integration
• Clinical accessibility
• Quantitative assessment
Development-To-Date:
Method has been validated in a study of 24 glioma survivors with resting-state functional MRI, dynamic susceptibility contrast perfusion MRI, and cognitive impairment status, which was accepted in the American Journal of Neuroradiology. The study found that resting-state network mapping of the clinically-relevant motor and language networks and the neuroscience research-relevant default mode network is possible using the pseudo-resting-state functional MRI technique, and these image maps have good agreement with actual resting-state functional MRI. The pseudo-resting-state functional MRI technique was also able to predict the cognitive impairment status of these patients with similar performance to resting-state functional MRI. Ongoing work has also involved using the pseudo-resting-state functional MRI technique for more advanced resting-state analyses techniques, including graph theory and BOLD asynchrony.
Cho NS, Wang C, Van Dyk K, Sanvito F, Oshima S, Yao J, Lai A, Salamon N, Cloughesy TF, Nghiemphu PL, Ellingson BM. Pseudo-resting-state functional MRI derived from dynamic susceptibility contrast perfusion MRI can predict cognitive impairment in glioma. AJNR Am J Neuroradiol. 2024 May 7:ajnr.A8327. doi: 10.3174/ajnr.A8327. Epub ahead of print. PMID: 38719607.
Related Papers:
US 10,973,433 Leakage correction for DSC-perfusion MRI by accounting for bidirectional contrast agent exchange
Interview Article:
Profile on Co-Inventor Nicholas Cho, MD/PhD Candidate: Cycling Ahead for Brain Tumor Breakthroughs
Reference:
UCLA Case No. 2023-126
Lead Inventor:
Benjamin Ellingson