Rotator cuff tears are a common source of pain and disability in the upper extremity, occurring up to 50-80% in an elderly population. Surgical treatment is normally considered for patients who fail in non-operative management, have an acute tear, or are unable to participate in their normal daily activities despite a course of physical therapy. However, failure of tendon healing after rotator cuff surgery is a common complication. Multiple studies have demonstrated that preoperative tear size, patient age, and muscle quality are critical factors to determine surgical outcomes, but there is a need for improved non-invasive evaluation methods to assess tendon and muscle quality to determine optimal treatment strategy. The goals of this research project are to develop quantitative MRI methods for evaluating tendon and muscle quality, to assess the sensitivity of developed MRI methods to degeneration in comparison to histological analysis, and to evaluate tendon and muscle quality changes due to surgery. First, quantitative ultrashort echo- time methods will be developed to evaluate the rotator cuff tendons, which consist of highly-ordered collagen fiber structures resulting in a short T2 relaxation time. The quantified parameters will be compared between controls and patients having full-thickness rotator cuff tears, and MR quantification will be compared with semi- quantitative histologic grades of tendon samples acquired during surgery for the patient group. Second, muscle quantitative imaging techniques will be developed for two different purposes, to evaluate fibrosis and fat fractions simultaneously and to discriminate fat tissue as either beige or white fat. Muscle fibrosis has not been quantified much due to its short T2* relaxation time, but UTE multi-echo images will allow the quantification of muscle fibrosis as well. A potential of the presence of beige fat in the rotator cuff muscles after tendon tears has been shown, which is important because beige fat can promote muscle regeneration and healing. We will also develop accurate fat quantification technique to define beige fat. Third, a longitudinal study will be performed with patients having full-thickness rotator cuff tears before and after surgery using developed tendon and muscle evaluation techniques, and tendon and muscle quality changes due to surgery will be evaluated. Statistical analysis to determine important imaging metrics that affect clinical functional outcomes will be performed, too. This proposed approach includes comprehensive biomarkers of structural, micro-structural, and biochemical changes, and aims for fully quantitative imaging for more precise assessments. The proposed research and training plans are designed for the candidate to gain further knowledge in musculoskeletal biology and statistics, and expertise in clinical research design and methodologies while extending her MRI technical skills. The K01 grant award will help the candidate to be an independent imaging scientist in the musculoskeletal field.

Public Health Relevance

Rotator cuff tears are a common source of pain and disability in the upper extremity. MRI is commonly used to evaluate rotator cuff pathology and to determine the treatment strategy, but the re-tear rate after surgical repairs is high, up to 57% in elderly patients. Here, we propose development of new, quantitative MRI techniques to evaluate degenerative changes in the rotator cuff tendons and muscles comprehensively, which may help to understand the process of rotator cuff muscle and tendon degeneration, to assess healing effects after surgery, and eventually to determine the optimal treatment strategy.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
1K01AR075895-01A1
Application #
9977459
Study Section
Special Emphasis Panel (ZAR1)
Program Officer
Washabaugh, Charles H
Project Start
2020-07-01
Project End
2025-06-30
Budget Start
2020-07-01
Budget End
2021-06-30
Support Year
1
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118