The results of the research of the MRI physics and fundamental neuroscience pillars of the BRAIN-TO group will be translated into the clinical workflows in collaboration with clinical research groups within UHN. In particular, fast MRI acquisition and novel contrasts will enable to provide unprecedented information about the vasculature (Collaborator: Drs. Timo Krings, Paula Alcaide Leon), a highly relevant topic given that pathological vasculature is associated with many neurodegenerative diseases and tumors. We are working on novel perfusion MRI techniques to identify tumor mimickers, which can provide information to avoid unnecessary brain surgeries. Preoperative perfusion characterization of brain masses can assist in differential diagnosis and, hence, be very useful to inform surgical management in the context of this diagnostic dilemma.
In addition, we are developing analysis and experimental approaches to assess the response of blood vessels to quantitative vasodilatory stimuli, called cerebrovascular reactivity (CVR) (Collaborator: Dr. David Mikulis). CVR is an essential function of healthy cerebral vessels that is altered by aging, various brain diseases and angiogenesis in brain tumors and radiotherapy. Our recent work has also identified a novel needle-free method for measuring tissue perfusion that uses endogenous hemoglobin, eliminating the need for injections of contrast agents, and utilize it for characterizing brain tumor tissue and vascular diseases routinely in patients at UHN.
Finally, we are setting-up an innovative framework in which technological developments and processing pipelines can be readily translated into clinical practice. The computational infrastructure required will allow radiologists and clinical specialists to incorporate novel MRI contrasts, typically not available in the clinical workflow, to their assessments of brain patients.
- Paula Alcaide Leon
- Sriranga Kashyap
- Angelica Manalac
- Jacob Schulman
- Neuro-Oncology (Angelica, Paula, Timo)
- Combined Spin- and Gradient-Echo Perfusion Weighted Imaging for Characterizing Vascular Architecture of Brain Lesions (Angelica)
- Cerebrovascular Reactivity (Jacob, David)
- Quantitative Perfusion Imaging with Dynamic Susceptibility Hypoxia Contrast (Jacob)
- Computational Psychiatry (Cognitive Network Modeling, Model-based fMRI) (Andreea)
Quantitative Perfusion Imaging with Dynamic Susceptibility Hypoxia Contrast (Jacob)
Background: Visualizing blood flow in the brain is essential in the effective diagnosis, prognosis, and surgical mapping of neurological disorders, including cancer, neurovascular complication, and neurodegenerative disease. Dynamic susceptibility contrast (DSC) has proven to be an effective magnetic resonance imaging (MRI) technique for gaining qualitative and quantitative insight into blood flow in the brain (perfusion). A limitation to the DSC method is the use of an exogenous contrast agent, gadolinium, which has been known to accumulate in the brain and bones, and is associated with the development of nephrogenic systemic fibrosis in individuals with renal disease; thus, a further limitation to gadolinium contrast is an inability to image repeatedly within a relatively short time frame in such patients. Successive imaging is often critical for characterizing neurovascular impairment. A potential method to bypass gadolinium’s limitations comes in the form of a ‘gas challenge,’ a novel method that yields contrast by exploiting the inherent magnetic properties of oxygen’s carrier protein, hemoglobin. The gas challenge generates contrast by briefly and safely decreasing the oxygen content (temporary hypoxia), thereby increasing the concentration of deoxygenated hemoglobin (dHb). Since dHb is a paramagnetic substance capable of strengthening the local magnetic field in and around the blood vessels, the hypoxia model may produce a contrast for perfusion imaging.
Hypothesis: Given the magnetic properties of dHb, the hypoxia model is capable of generating contrast that can accurately capture perfusion information in the brain.
Aim One: Theoretical Hypoxia and Gadolinium Comparisons via Simulations
Gadolinium and dHb are different contrast agents with a variety of differing physical and physiological properties (size, interaction with the magnetic field, localization, etc.). In order to evaluate the efficacy of the hypoxia paradigm in comparison to gadolinium, it is first necessary to simulate and determine the effect that the aforementioned differences might have on simulated perfusion data. These differences can then be used to guide any adjustments required for interpreting clinical data. This work also aims to simulate a variety of theoretical hypoxic conditions, governed by hypoxic duration, intensity, etc. In doing so, the work aims to define a range of conditions where hypoxia provides perfusion metrics that are least susceptible to error, most reflective of true tissue perfusion, and safe. I am currently working on this aim of the project.
Aim Two: Evaluating Efficacy of Hypoxia versus Gadolinium on 3T Clinical MRI Data
3T clinical MRI data for both gadolinium and hypoxia are currently being collected. Data will be pre-processed with brain extraction, motion correction, smoothening, and registration, using the FMRIB software library (FSL). VERBENA, a DSC processing program from FSL, will then be used to generate cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) perfusion maps and metrics for each contrast model. VERBENA was selected as a data processing tool for its robustness to various physiological conditions. Following processing, statistical analysis (regression and Bland-Altman analysis) can be conducted on perfusion metrics to compare the hypoxia and gadolinium models in accurately capturing perfusion data from various brain tissues. I will begin working on this aim following the collection of MRI data.
Conclusion: The ultimate goal of this work is to determine the clinical efficacy of the hypoxia model as a safe alternative to gadolinium in DSC-MRI—to my knowledge, the first study of its kind. If validated, future work has the potential to benefit from and further explore this hypoxic model in applications of safer clinical imaging research and practice.