@article{QIMS23202,
author = {Laszlo Kiraly and Balint Kiraly and Krisztian Szigeti and Csaba Zsolt Tamas and Sandor Daranyi},
title = {Virtual museum of congenital heart defects: digitization and establishment of a database for cardiac specimens},
journal = {Quantitative Imaging in Medicine and Surgery},
volume = {9},
number = {1},
year = {2019},
keywords = {},
abstract = {Education and training of morphology for medical students, and professionals specializing in pediatric cardiology and surgery has traditionally been based on hands-on encounter with congenitally malformed cardiac specimens. Large international archives are no longer widely available due to stricter data protection rules, a reduced number of autopsies, attrition rate of existing specimens, and most importantly due to a higher survival rate of patients. Our Cardiac Archive houses about 400 cardiac specimens with congenital heart disease. The collection spans almost 60 years and thus goes back to pre-surgical era. Unfortunately, attrition rate due to desiccation has led to an increased natural decay in recent years. The present multi-institutional project focuses on saving the collection by digitization. Specimens are scanned by high-resolution micro-CT/MRI. Virtual 3D-models are segmented and a comprehensive database is built. We now report an initial feasibility study with six test specimens that provided promising results, however, adequate presentation of the intracardiac anatomy, including septa and cardiac valves requires further refinements. Computer assisted design methods are necessary to overcome consequences of pathological examination, shrinkage and/or distortion of the specimens. For a next step, we anticipate an expandable web-based virtual museum with interactive reference and training tools. Web access for professional third parties will be provided by registration/subscription. In a future phase, segmental wall motion data could be added to virtual models. 3D-printed models may replace actual specimens and serve as hands-on surgical training to elucidate complex morphologies, promote surgical emulation, and extract more accurate procedural knowledge based on such a collection.},
issn = {2223-4306}, url = {https://qims.amegroups.org/article/view/23202}
}