Anatomical fiducials and their relevance to stereotactic neurosurgery: quality control and improving surgical targeting

Ultra-high field Magnetic Resonance Imaging (UHF-MRI) allows for visualization of deep brain regions in exquisite detail, unlike any other imaging modality. In Canada, there are only a few MRI machines that can generate magnetic fields strong enough (7T or higher) to obtain UHF-MRI scans. The ability to visualize and locate deeper brain regions with millimetric accuracy is crucial to achieving optimal therapeutic outcomes during an invasive neurosurgical procedure called deep brain stimulation (DBS). DBS involves delivering electrical stimulation via electrodes to relieve symptoms of a wide variety of disorders like Parkinson’s disease, essential tremor, dystonia, depression, and addiction. Neurosurgeons often target deeper brain regions without seeing them due to limited access to UHF-MRI images.

The goal of my research is to develop a machine learning model (allowing computers to make decisions beyond their initial programming) to utilize the locations and distances between 32 points on brain images called anatomical fiducials (AFIDs) placed on UHF-MRI images to locate common DBS surgical targets with millimetric accuracy. I hypothesize that the 32 AFIDs survey deep brain anatomy enough to allow for predicting the location of multiple DBS targets.

Faculty Supervisor:

Jonathan C Lau;Ali Khan

Student:

Partner:

Parkinson Society Southwestern Ontario

Discipline:

Engineering

Sector:

Other services (except public administration)

University:

The University of Western Ontario

Program: