New Large Animal Model for Aortic Aneurysms in the Viscerorenal Segment.

BACKGROUND: Aortic aneurysms in the viscerorenal-segment are nowadays treatable by endovascular means. Previously, new endograft techniques were only tested in healthy animals. We aimed to establish a new large animal model for testing complex endovascular stent techniques preclinically. METHODS: In sheep, four juxtarenal and two type IV thoracoabdominal aortic aneurysms were surgically created via a retroperitoneal approach. Two pieces out of a 10 × 15-cm bovine pericardial patch were sewn with the healthy aorta longitudinally. The viscerorenal segment was clamped, and the aorta was incised longitudinally. Then, the patches were longitudinally sewn together. In the meantime, antegrade flow through the native part of the aorta was already established by tangential clamping. Computed tomography angiography was performed after 4, 8, and 52 wk. RESULTS: Technical success was 100%. The median surgical procedure time was 3 h, the median blood loss was 210 mL, and the viscerorenal-segment clamping time was 2-4 min. The animals started drinking 1 h after arousal from anesthesia. One animal died after 1 wk because of delayed bleeding and another died after 1 y because of aneurysm rupture by a secondary bacterial infection. Four animals survived. The proximal landing zone diameter and the clock position of the vessel were stable over 52 wk. CONCLUSIONS: Surgical creation of an aortic aneurysm in the viscerorenal-segment in sheep was successful, without an ischemia/reperfusion injury. This animal model offers a new platform for evaluating innovative endovascular therapy options in vivo.

Data Curation for Preclinical and Clinical Multimodal Imaging Studies.

PURPOSE: In biomedical research, imaging modalities help discover pathological mechanisms to develop and evaluate novel diagnostic and theranostic approaches. However, while standards for data storage in the clinical medical imaging field exist, data curation standards for biomedical research are yet to be established. This work aimed at developing a free secure file format for multimodal imaging studies, supporting common in vivo imaging modalities up to five dimensions as a step towards establishing data curation standards for biomedical research. PROCEDURES: Images are compressed using lossless compression algorithm. Cryptographic hashes are computed on the compressed image slices. The hashes and compressions are computed in parallel, speeding up computations depending on the number of available cores. Then, the hashed images with digitally signed timestamps are cryptographically written to file. Fields in the structure, compressed slices, hashes, and timestamps are serialized for writing and reading from files. The C++ implementation is tested on multimodal data from six imaging sites, well-documented, and integrated into a preclinical image analysis software. RESULTS: The format has been tested with several imaging modalities including fluorescence molecular tomography/x-ray computed tomography (CT), positron emission tomography (PET)/CT, single-photon emission computed tomography/CT, and PET/magnetic resonance imaging. To assess performance, we measured the compression rate, ratio, and time spent in compression. Additionally, the time and rate of writing and reading on a network drive were measured. Our findings demonstrate that we achieve close to 50 % reduction in storage space for µCT data. The parallelization speeds up the hash computations by a factor of 4. We achieve a compression rate of 137 MB/s for file of size 354 MB. CONCLUSIONS: The development of this file format is a step to abstract and curate common processes involved in preclinical and clinical multimodal imaging studies in a standardized way. This work also defines better interface between multimodal imaging modalities and analysis software.