Musculoskeletal disorders and diseases are a leading cause of physical disability with over 40 percent of disabling conditions being related to this. The areas of highest risk are in the knees (>9 million people in the US alone) and spine (>25 percent of the global population) due to soft tissue degeneration, which can lead to debilitating neck, back, and knee pain.
As soft tissue degeneration is one of the leading musculoskeletal diseases, there are many patients that require treatment. However, it is currently not possible to detect these early degenerative states. Therefore, there is no method of not only characterizing these early stages nor is there a way to monitor early disease treatment options.
Currently, MRI is the state-of-the-art technology for early detection of soft tissue degeneration due to its excellent non-invasive soft tissue contrast and routine clinical use. Furthermore, the ability of MRI to provide enhanced tissue characterization through quantitative relaxometrysets it apart as a diagnostic tool. However, conventional monoexponential T2 and T1 values have shown limited specificity to individual matrix components and their anomalous signal decay as well as early changes in soft tissue structure during degradation. Thus, the variation in monoexponentialdecay times may not reflect as large of a dynamic range as expected, leading to a decrease in potential matrix component sensitivity.
Researchers at the CU Boulder and Emory University have developed a stretched exponential model that can more accurately detect subtle compositional changes leading to early soft tissue degeneration detection. The effectiveness of a stretched exponential model is dependent on two different phenomena:
- The presence of anomalous relaxation due to microscopic heterogeneity
- The corresponding macroscopic change in decay time distributions
The addition of the alpha parameter permits the SE decay model to capture this heterogeneity. The result of the SE model detecting the increase in heterogeneity is an increase in model dynamic range. Within a given tissue region consisting of multiple pixels, this increased relaxation parameter range can be characterized by a stable distribution detailing changes on the macroscopic level suggest higher sensitivity to soft tissue compositional differences.
The increased sensitivity to compositional changes could be useful not only diagnostically but also in the comparison of related treatments. This stretched exponential analysis could be easily implemented in already existing MRI software. The image data acquisition would remain the same with only subtle changes to the post processing method.
This has potential applications for:
- Early detection of soft tissue degradation
- Early detection of musculoskeletal disease
- Disease prediction using machine learning
- Determining appropriate treatment regimen for patients
The inventor team is seeking companies looking to license this technology, as an add on to existing MRI equipment – or as a core technology for development of new products on the market.