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Updated: Jun 16, 2026

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography
Published on: May 10, 2022
Quantifying the Viscoelastic Properties of Pancreatic Tissue: A Comparative Study of Human, Porcine and Engineered
Kunal Joshi1, Daniel M Espino2, Duncan E T Shepherd2
1Department of HPB Surgery, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, UK.
Abstract:
Pancreatoduodenectomy (PD) is among the most complex abdominal procedures, with a leak from the pancreatic anastomosis (postoperative pancreatic fistula, POPF) continuing to be a major source of morbidity and mortality. The mechanical characteristics of the pancreas, particularly tissue texture, are closely linked to POPF risk, yet remain poorly defined, limiting the accuracy of currently available training models. This study sought to quantify the viscoelastic behaviour of human pancreatic tissue and to compare it with porcine pancreas and a commercially available synthetic substitute. Fresh and frozen human pancreatic samples, porcine lobes, and synthetic tissues underwent uniaxial compression ramp testing and dynamic mechanical analysis. Human pancreatic tissue was markedly 'stiffer' (mean Young's modulus 148.3 ± 117.0 kPa) compared with porcine (25.1 ± 17.1 kPa) and synthetic (34.5 ± 17.6 kPa) samples. Hard pancreas exhibited over twice the material stiffness of soft pancreas in both fresh (218.8 vs. 93.0 kPa, p < 0.001) and frozen (148.9 vs. 55.6 kPa, p = 0.002) states, whilst no significant variation was observed between different porcine lobes. Storage and loss modulus assessment demonstrated frequency-dependent differences, with synthetic models unable to reproduce the nonlinear viscoelastic profile of human tissue. These findings provide the first robust biomechanical benchmarks for pancreatic tissue and reveal the limitations of current phantoms. Integrating these data into advanced synthetic and computational models may improve surgical training, reduce the learning curve, and enhance patient outcomes after PD.
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