Computational and image processing methods for analysis and automation of anatomical alignment and joint spacing in reconstructive surgery.

PURPOSE: Reconstructive surgeries to treat a number of musculoskeletal conditions, from arthritis to severe trauma, involve implant placement and reconstructive planning components. Anatomically matched 3D-printed implants are becoming increasingly patient-specific; however, the preoperative planning and design process requires several hours of manual effort from highly trained engineers and clinicians. Our work mitigates this problem by proposing algorithms for the automatic re-alignment of unhealthy anatomies, leading to more efficient, affordable, and scalable treatment solutions. METHODS: Our solution combines global alignment techniques such as iterative closest points with novel joint space refinement algorithms. The latter is achieved by a low-dimensional characterization of the joint space, computed from the distribution of the distance between adjacent points in a joint. RESULTS: Experimental validation is presented on real clinical data from human subjects. Compared with ground truth healthy anatomies, our algorithms can reduce misalignment errors by 22% in translation and 19% in rotation for the full foot-and-ankle and 37% in translation and 39% in rotation for the hindfoot only, achieving a performance comparable to expert technicians. CONCLUSION: Our methods and histogram-based metric allow for automatic and unsupervised alignment of anatomies along with techniques for global alignment of complex arrangements such as the foot-and-ankle system, a major step toward a fully automated and data-driven re-positioning, designing, and diagnosing tool.

Diabetes is associated with a lower minimum moment of inertia among older women: An analysis of 3D reconstructions of clinical CT scans.

Hip fractures are a significant burden on the aging population, often resulting in reduced mobility, loss of independence, and elevated risk of mortality. While fracture risk is generally inversely related to bone mineral density (BMD), people with diabetes suffer a higher fracture rate despite having a higher BMD. To better understand the connection between diabetes and fracture risk, we developed a method to measure the minimum moment of inertia (mMOI; a geometric factor associated with fracture risk) from clinical CT scans of the pelvis. Since hip fractures are more prevalent in women, we focused on females in this study. We hypothesized that females with diabetes would have a lower mMOI along the femoral neck than those without diabetes, indicative of a higher fracture risk. Three-dimensional models of each hip were created from clinical CT scans of 40 older women (27 with diabetes: 10 fracture/17 non-fractured; 13 without diabetes: non-fractured controls). The mMOI of each hip (n = 80) was reported as the average from three trials. People with diabetes had an 18% lower mMOI as compared to those without diabetes after adjusting for age and BMI (p = 0.02). No differences in the mMOIs between the fractured and contralateral hips in the diabetic group were observed (p = 0.78). Similarly, no differences were observed between the fractured and non-fractured hips of people with diabetes (p = 0.29) when accounting for age and BMI. This suggests structural differences in the hips of individuals with diabetes (measured by the mMOI) may be associated with their elevated fracture risk.