PROJECT SUMMARY: This proposal is devoted to the development, clinical implementation and evaluation of a novel, methodology for generating brain sodium images of unparalleled resolution and signal-to-noise-ratio. This methodology is a result or recent developments in Bayesian image reconstruction and manifold mapping techniques that together make it possible to robustly utilize anatomical information from high-resolution brain scans to guide the reduction of partial voluming effects from lower resolution, lower signal-to-noise ratio (SNR) brain scans. Though such “constrained reconstruction” schemes have been long-advocated in the MRI imaging community, their computational demands, limited performance and heavy operator input had previously limited their evaluation and practical characterization thus rendering them impractical for use within the confines of a clinical environment. Recently, we have shown that the combined use of segmentation free Bayesian approaches together with manifold mapping techniques can be used to provide a fast framework for Anatomically Guided Reconstruction where the information form high-resolution brain scans is used, routinely, to improve spatial resolution and SNR of concurrently acquired brain PET scans leading to improved sensitivity for the detection of low-contrast lesions in the brain. Our initial experience with extensions of this approach for sodium MRI of the brain has produced images of spatial resolution and SNR that far exceed what had been previously achieved using high and ultra-high magnetic field strengths. Establishing the limits of this approach and characterizing its performance on clinically relevant cases is the thrust of this proposal.

PROJECT NARRATIVE: This project will develop new methodology for improving the resolution of sodium images from the human brain. There have been approaches for generating sodium images of the brain using imaging times that are suitable for routine clinical use, however these images suffer from significant blur as a result of the demanding nature of this brain imaging application (much weaker signal than conventional MRI vanishing very quickly after it has been generated). Our approach seeks to reduce the deleterious effects of this blur through the use of anatomical information from concurrently acquired high-resolution conventional MRI scans of the brain.