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.
The successful outcome of the proposed project will result in the development and translation into the clinical setting of new methods to enable greatly improved imaging of lower back pain in human subjects. While this project focuses on lower back pain, these new imaging techniques could ultimately benefit the clinical management of other musculoskeletal pain.
