Institute for Experimental Molecular Imaging, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University Clinic, Forckenbeckstraße 55, 52074, Aachen, Germany.
Inviscan SAS, Strasbourg, France.
Mol Imaging Biol. 2019 Dec;21(6):1034-1043. doi: 10.1007/s11307-019-01339-0.
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.
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.
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.
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.
在生物医学研究中，成像方式有助于发现病理机制，从而开发和评估新的诊断和治疗方法。然而，虽然临床医学成像领域存在数据存储标准，但生物医学研究的数据管理标准尚未建立。本工作旨在开发一种用于多模态成像研究的免费安全文件格式，支持多达五个维度的常见体内成像方式，以此作为建立生物医学研究数据管理标准的一步。
图像使用无损压缩算法进行压缩。在压缩的图像切片上计算加密哈希。哈希和压缩并行计算，根据可用核心数加速计算。然后，带有数字签名时间戳的哈希图像被加密写入文件。结构中的字段、压缩切片、哈希和时间戳被序列化，以便从文件中读写。C++实现已在来自六个成像站点的多模态数据上进行了测试，并且记录良好，并集成到临床前图像分析软件中。
该格式已通过包括荧光分子断层扫描/X 射线计算机断层扫描（CT）、正电子发射断层扫描（PET）/CT、单光子发射计算机断层扫描/CT 和 PET/磁共振成像在内的多种成像方式进行了测试。为了评估性能，我们测量了压缩率、比率和压缩所花费的时间。此外，还测量了在网络驱动器上写入和读取的时间和速度。我们的研究结果表明，对于 μCT 数据，我们可以实现近 50%的存储空间减少。并行化将哈希计算速度提高了 4 倍。我们实现了 354MB 文件大小的 137MB/s 压缩率。
该文件格式的开发是朝着以标准化方式抽象和管理临床前和临床多模态成像研究中常见过程迈出的一步。这项工作还定义了多模态成像方式和分析软件之间更好的接口。
