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.

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.