# Technology Research Site for Advanced, Faster Quantitative Imaging for BACPAC

> **NIH NIH UH3** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $166,865

## Abstract

PROJECT SUMMARY/ABSTRACT
Disorders of the spine have a tremendous impact on society; both physically through the morbidity of afflicted
individuals, and financially, through lost productivity and increased health care costs. Despite the significance
of this problem, the etiology of symptoms is diverse and unclear in many patients, and there are few reliable
methods by which to prospectively determine the appropriate course of patient care and to objectively evaluate
the effectiveness of various interventions. Challenges contributing to this major healthcare dilemma include
numerous sources of back pain, difficulty in visualization of responsible tissues using any single imaging
technique and difficulty in the localization of pain and contributing molecular processes. Magnetic Resonance
imaging (MR) has been used to characterize disc, muscle, nerves and Positron Emission Tomography (PET)
has been used to study bone turnover, and facet disease in subjects with lower back pain.
 The research and tool development proposed in this UH2/UH3 takes the critical next step in the clinical
translation of faster, quantitative magnetic resonance imaging (MR) of patients with lower back pain. New
optimized techniques and patient studies are required to investigate its clinical potential for quantitatively
characterizing the tissues implicated in lower back pain, and objective evaluation of pain. Our proposed
multidisciplinary Technology Research Site (Tech Site) of the NIH Back Pain Consortium (BACPAC) will
develop Phase IV TTMs (Research and Development for Technology Optimization) to leverage two key
technical advancements – development of machine learning based faster MR acquisition methods, and
machine learning for image segmentation and extraction of objective disease related features from images. We
will develop, validate, and deploy end-to-end deep learning-based technologies (TTMs) for accelerated image
reconstruction, tissue segmentation, detection of spinal degeneration, to facilitate automated, robust
assessment of structure-function relationships between spine characteristics, neurocognitive pain response,
and patient reported outcomes. To accomplish this important project, we have assembled a highly-experienced
multidisciplinary research team combining extensive expertise MR bioengineering, advanced MRI data
analysis, radiology, neuroscience, neurosurgery, orthopedic surgery, multi-dimensional analytics and have
existing research agreements with industry. The research facilities and environment include the clinical and
research infrastructure required for successful completion of the proposed translational project. The team has
disseminated tools before to academia, worked closely with industry and are motivated to totally work with
BACPAC as the plans of the consortium evolve.

## Key facts

- **NIH application ID:** 10304082
- **Project number:** 3UH3AR076724-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Sharmila Majumdar
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $166,865
- **Award type:** 3
- **Project period:** 2019-09-26 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10304082

## Citation

> US National Institutes of Health, RePORTER application 10304082, Technology Research Site for Advanced, Faster Quantitative Imaging for BACPAC (3UH3AR076724-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10304082. Licensed CC0.

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