Biomechanical characterization of the female rectum in vivo through MRI-based inverse finite element analysis.

Silva, Ana Telma; Martins, Madalena; Ferreira, Nuno Miguel; Parente, Marco; Brandão, Sofia; Silva, Elisabete · J Biomech · 2026

biomechanical · Level V

Where this comes from

Abstract

Rectal prolapse is a pelvic floor disorder characterized by pathological descent of the rectum resulting from impaired pelvic support. Understanding the rectum's in vivo biomechanical behavior is essential for improving diagnosis and treatment planning. This study aimed to estimate the in vivo mechanical properties of the rectum using inverse finite element analysis (FEA) driven by magnetic resonance imaging (MRI) data. MRI scans from six women (three with rectal prolapse and three asymptomatic controls) were analyzed to quantify supero-inferior (SI) and antero-posterior (AP) rectal displacements, as well as puborectalis muscle thickness. A previously validated three-dimensional pelvic model was adapted to simulate the Valsalva maneuver under an intra-abdominal pressure (IAP) of 4 kPa. Rectal tissue was modeled using a Yeoh hyperelastic formulation, with material parameters optimized through inverse FEA by minimizing the discrepancy between experimental and simulated displacements. The prolapse group exhibited a marked increase in SI displacement (26.22 mm) compared to controls (3.44 mm), together with reduced puborectalis muscle thickness. The optimized model indicated a 28% reduction in rectal stiffness in women with prolapse. Numerical simulations further showed increased SI displacement of the bladder neck in the prolapse group (9.73 mm) compared to asymptomatic controls (6.40 mm), indicating that biomechanical impairment extends beyond the rectum to the anterior pelvic compartment. This difference corresponds to approximately 34% of the SI bladder neck displacement observed in the prolapse group. Inverse FEA based on in vivo MRI enables non-invasive characterization of rectal tissue mechanics, with potential applications in patient-specific diagnosis, surgical planning, and outcome prediction.