Thiel cadaver suitability for image-based abdominal ultrasound research

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Lawley, Alistair and Zin, May Thu and Turbet, Catriona and Hampson, Rory and Dobie, Gordon (2025) Thiel cadaver suitability for image-based abdominal ultrasound research. Annals of Anatomy - Anatomischer Anzeiger, 262. 152713. ISSN 0940-9602 (https://doi.org/10.1016/j.aanat.2025.152713)

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Abstract

Introduction Medical ultrasound is a widely utilised diagnostic tool, offering healthcare professionals a safe, real-time imaging solution effective across various disciplines and care settings. Despite extensive clinical use, ultrasound receives limited focus from AI and imaging research, due to the lack of extensive datasets. Unlike CT and MRI, where scan recording is part of the workflow, creating large databases ideal for study, ultrasound is a live modality where only limited recording is performed, and results are not generally collated into significant datasets. Therefore, capturing an ultrasound dataset for imaging research is time-consuming and expensive due to clinical and ethical hurdles involved in scanning patients, making it inaccessible to many researchers. This study investigates Thiel cadavers as a more accessible and cost-effective sample for early-stage ultrasound research compared to in-vivo patient scanning. Materials & Methods We present a dataset of 37 abdominal scans and provide an initial review of their suitability for ultrasound imaging studies. Thiel cadavers are preserved using a liquid embalming method that maintains many tissue properties essential for ultrasound scanning. Results Over 70% of the scans were deemed of sufficient quality, when compared against similar patient scans, for use in imaging research. Changes in the fluid balance within tissues and abdominal cavity were the main limitation in obtaining quality scans. Conclusions This study demonstrates that Thiel cadavers maintain sufficient tissue properties for diverse ultrasound imaging research, providing a cost-effective, low-risk option for speculative research in abdominal ultrasound AI where clinical trials are not yet feasible, and existing datasets are unavailable.

ORCID iDs

Lawley, Alistair ORCID logoORCID: https://orcid.org/0000-0002-0903-1116, Zin, May Thu, Turbet, Catriona, Hampson, Rory ORCID logoORCID: https://orcid.org/0000-0001-7903-7460 and Dobie, Gordon ORCID logoORCID: https://orcid.org/0000-0003-3972-5917;
CORE (COnnecting REpositories)