Robust Pediatric MRI (with Dr. Shreyas Vasanawala, Lucile-Packard Children Hospital, Stanford University)
MRI is excellent for diagnosis and monitoring of pediatric disease, offering superb contrast and high resolution, without risk to a population particularly susceptible to cancer from ionizing radiation of computed tomography. However, MRI’s impact in children is limited by (1) lack of robustness from the technical demands and low signal to noise ratio (SNR) of imaging small moving structures, (2) long exams that limit access, cause motion artifacts, and often require anesthesia with attendant risk, and (3) research and development mostly focused on adults. Thus, children often lack the benefits of cross-sectional imaging altogether or are exposed to ionizing radiation.
This project aims to develop dedicated pediatric MRI coil arrays, fast image acquisitions using combination of compressed sensing with parallel imaging, motions correction algorithms and clinical validation.
Compressed Sensing and parallel imaging in Hyperpolarized 13C MRSI (With Peter Shin, Simon Hu, Peder Larson and Dan Vigneron, UCSF)
Hyper High polarization of nuclear spins in liquid state through dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at very high signal-to-noise, allowing for rapid assessment of tissue metabolism. The abundant SNR afforded by this hyperpolarization technique makes high-resolution 13C 3D-MRSI feasible. However, imaging is constrained by strict timing which limits the possible spatial and temporal resolution. The aim of the project is develop compressed sensing and parallel imaging techniques for acceleration of hyperpolarized 13C metabolic imaging that will enable translation of this technology to human studies.
Fast Reconstruction of Compressed Sensing MRI on Parallel Platforms (With Kurt Keutzer and Mark Murphy and GE healthcare)
Compressed Sensing exploits the natural compressibility in MRI images to reduce the sampling requirements and accelerate MRI acquisitions. How ever the penetration of CS to clinical MRI has been slow due to the massive amount of computation that is needed and clinically impractical reconstruction times of tens of minutes to hours. We will develop and validate fast compressed sensing reconstruction algorithms and and fast implementations, making CS-MRI clinically and commercially viable. At the end of this project, we aim to have an efficient, parallelized and modular reconstruction that would be able to reconstruct a typically sized 3D multi-channel CS-MRI exam in less than a minute.
Bespoke Coils: Flexible and conforming MRI coil arrays using printed electronics technology (With Ana-Claudia Arias and Joseph Corea)
While arrays of MRI coils can improve SNR and speed up acquisition time over single coils, most arrays today have a rigid structure, resulting in a poor fit and counteracting potential SNR gains. MRI resonant coil design has not kept pace with the new field of printed electronics which can pattern components directly on flexible substrates. In this project will extend these new techniques to print clothlike-MRI coils which will drape conformly over different body shapes, thus improving image quality for a wide patient base.
Rapid Time-Resolved 3D Phase-Contrast Pediatric Cardiovascular MRI
Magnetic resonance imaging provides essential information used in the care of children with heart defects: heart function (chamber sizes and pumping efficiencies), blood flow (in various vessels and across heart valves), and anatomy. However, as MRI exams take longer than an hour and require children to remain motionless, anesthesia is often required, which is risky in these fragile patients. This project will develop and validate a rapid comprehensive MRI technique for evaluation of congenital heart defects.
In particular we aim to develop velocity encoding strategies with high velocity-to-noise ratio and large velocity dynamic range, subsampling strategies for 3D time resolved phase-contrast, Reconstructions from subsampled data that exploit spatio-temporal sparsity of 4D flow, fast and clinically feasible implementation and clinical validation.