Data-Driven Method for Improving Brain Tumor Imaging

LLNL Data Science Institute Seminar Series

Brain tumor incidence is expected to rise by 6% over the next 20 years. Nearly 79,000 patients will be diagnosed in the U.S. this year alone. In a DSI seminar on August 2, 2018, Dr. Maryam Vareth outlined the University of California at San Francisco’s (UCSF’s) efforts to improve brain tumor outcomes through data-driven medicine.

Standard magnetic resonance imaging (MRI) is a mainstay of brain tumor diagnosis and evaluation, but it poses challenges when clinicians attempt to distinguish treatment effects from recurrent tumors. More advanced imaging is needed to better define tumor regions so that radiation treatments can target areas with high probability of recurrence.

Vareth described a potential solution called magnetic resonance spectroscopic imaging (MRSI)—static metabolic imaging that zeroes in on tumor chemistry. With MRSI, clinicians can identify metabolic changes in the brain earlier than when a recurrent tumor would show up with standard MRI. Moreover, MRSI is noninvasive and can be performed on a regular MRI machine.

The MRSI process creates indices of signals from choline, creatine, N-acetyl-aspartate, lipid, and lactate. Data can then be analyzed in map of voxels (3D pixels). With an inherently low signal, however, MRSI scans take a long time—especially if clinicians need to scan the entire brain, not just one region. Faster MRSI scans will help encourage clinicians to adopt this type of imaging.

Vareth’s team is developing a fast-trajectory MRSI analysis method to reduce scan time significantly. “An MRI is a very expensive Fourier transform machine,” she explained, so acceleration can be achieved through modified k-space sampling (below the Nyquist rate) of raw data. This process involves compressed sensing and parallel imaging as well as weighting images according to their sensor proximity (i.e., sensitivity is higher closer to a sensor within the machine).

Vareth and her UCSF colleagues are working toward “super-resolution” of MRSI and exploring the potential of deep learning to further enhance image quality while reducing scan duration. The team has developed software, called SIVIC, for processing automated prescription and reconstruction of MRSI data. SIVIC is available on GitHub.

Speaker(s)

Maryam Vareth

Health & Life Sciences Lead

Maryam Vareth leads BIDS’ data science research in the Health & Life Sciences. She is also a Co-Director of the Innovate For Health initiative, a collaboration among UC Berkeley, UCSF, and the Janssen Pharmaceutical Companies of Johnson & Johnson. As an experienced researcher, engineer, and data scientist, she applies mathematics, statistics and physics to solve unmet needs in healthcare and to enhance patients’ experience during their medical journey. She is an advocate for “data-driven” medicine, and in particular for linking large-scale medical imaging data with medical diagnostics and therapeutics to extract clinically-relevant insights through the use of open source and open research practices.  

Dr. Vareth received her BS and MS training in Electrical Engineering and Computer Science (EECS) from UC Berkeley, where she was awarded the prestigious Regent’s and Chancellor’s Scholarship. She completed her PhD through the joint UC Berkeley-UCSF Bioengineering program as a National Science Foundation Fellow, where she was awarded the Margaret Hart Surbeck Endowed Fellowship for Interdisciplinary Research for her work on developing new techniques and algorithms for the acquisition, reconstruction and quantitative analysis of Magnetic Resonance Spectroscopy Imaging (MRSI), with the goal of improving its speed, sensitivity and specificity to improve the management of patients with brain tumors. She conducted her post-doctoral fellowship at UCSF, combining structural, physiological and metabolic imaging data from large clinical trials to quantitatively characterize heterogeneity within malignant brain tumors.