Radiomic Analysis of Sonograms for the Detection of Tendon Damage

dc.contributor.authorHubert, Samuel Laurenceen
dc.contributor.committeechairWang, Vincent M.en
dc.contributor.committeememberPerez, Miguel A.en
dc.contributor.committeememberKozar, Albert Johnen
dc.contributor.departmentDepartment of Biomedical Engineering and Mechanicsen
dc.date.accessioned2025-06-04T08:05:36Zen
dc.date.available2025-06-04T08:05:36Zen
dc.date.issued2025-06-03en
dc.description.abstractThe exact precursors to tendinopathy can be multiple and nuanced. Further complicating effective diagnosis is the reliance on tendon pain as a measure of injury. Even severe tendinopathies can remain asymptomatic as overuse damage accumulates and diagnosis using quantitative indicators of damage is challenging. Hence, there is a critical need to establish biomarkers to assist in early detection of tendinopathy in at-risk populations such as athletes. One set of imaging biomarkers can be generated using radiomics. This technique extracts numerical values from medical images, such as those captured using ultrasound (US), for clinical decision-making. US images are the standard for confirming clinical tendinopathy diagnoses, as they are more accurate, less time-consuming, and less expensive than other modalities. Texture analysis, a subset of radiomics, uses algorithms to quantify the shade, alignment, and distribution of pixels within US images to assess the disease state of a tissue. Prior clinical studies of texture analysis have shown that it can reliably identify differences in tissue structure and composition. In symptomatic cases of tendinopathy, tendon US images appear darker and more disordered. One drawback to previous in vivo studies, however, is the large variation of higher order statistics between US images, likely due to changes in ultrasound transducer operation or patient positioning. To circumvent these limitations, imaging of an ex vivo tendon will be conducted simultaneously with loading in this study. A total of 14 juvenile, female porcine superficial digital flexor tendons were excised and loaded using an MTS Insight 10 load frame. US imaging was carried out using a Supersonic Imagine Aixplorer SLH20-6 transducer. After preloading at 2 N, each sample in the progressive strain group (n = 6) was subjected to progressively increasing strain levels (2, 4, 6, and 8%), utilizing a 25-minute stress relaxation at each strain magnitude with 20-minute recoveries. Once the stress stabilized, the equilibrium stiffness (Eeq) was calculated, and additional US images were taken. The onset of tendon damage was defined by a reduction in Eeq, as well as other mechanical parameters such as laxity and percent relaxation. The strain level at which this damage occurs informed extension parameters for the second group (n = 8), where US images were acquired before and after applying a single 10% strain ramp to induce damage. It was found that numerous parameters describing image texture correlated with reductions in tendon mechanical properties, with higher order parameters generally correlating more strongly. This novel data suggests that higher order parameters can serve as reliable imaging biomarkers of matrix damage and can be used in the future for clinical diagnostics.en
dc.description.abstractgeneralTendinopathy is a type of degenerative tendon injury that develops from overuse. One major challenge is that many tendinopathies do not immediately cause pain, even when damage is severe, which makes it difficult to diagnose the condition before it becomes debilitating. To improve early detection, there is an urgent need to develop clinical methods that can reveal early signs of damage in the tendon. Currently, symptomatic tendinopathy is frequently confirmed by ultrasound (US) imaging, which uses sonic waves to visualize the tendon through the skin. One promising approach to streamlining this process is a method called radiomics, which converts medical images—like those acquired using US—to numerical data for analysis. This technique can look at patterns in these images, such as light and dark areas, to identify signs of tissue damage. Previous clinical studies have shown that radiomics can detect changes in tendon structure, but results can vary due to factors such as patient positioning during scanning. To avoid this pitfall, this study examined tendons dissected from pigs in a controlled lab setting. Different levels of stretch were applied to the tendon to simulate the long-term overuse that causes tendinopathy. At the same time, US images of the tendon were being acquired to measure how the image patterns changed after the stretches had been applied. It was found that radiomic analysis of these patterns was able to detect damage. These findings suggest that quantitative US image analysis could help identify tendinopathy earlier and more reliably than conventional clinical assessments.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:44331en
dc.identifier.urihttps://hdl.handle.net/10919/135039en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttendinopathyen
dc.subjectultrasounden
dc.subjecttextureen
dc.subjectbiomechanicsen
dc.titleRadiomic Analysis of Sonograms for the Detection of Tendon Damageen
dc.typeThesisen
thesis.degree.disciplineBiomedical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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