PERFORM Centre, Concordia University, Montreal, H4B 1R6, Canada.
Department of Electrical and Computer Engineering, Concordia University, Montreal, H3G 1M8, Canada.
Med Phys. 2017 Jul;44(7):3875-3882. doi: 10.1002/mp.12268. Epub 2017 May 16.
The advancement of medical image processing techniques, such as image registration, can effectively help improve the accuracy and efficiency of brain tumor surgeries. However, it is often challenging to validate these techniques with real clinical data due to the rarity of such publicly available repositories.
Pre-operative magnetic resonance images (MRI), and intra-operative ultrasound (US) scans were acquired from 23 patients with low-grade gliomas who underwent surgeries at St. Olavs University Hospital between 2011 and 2016. Each patient was scanned by Gadolinium-enhanced T1w and T2-FLAIR MRI protocols to reveal the anatomy and pathology, and series of B-mode ultrasound images were obtained before, during, and after tumor resection to track the surgical progress and tissue deformation. Retrospectively, corresponding anatomical landmarks were identified across US images of different surgical stages, and between MRI and US, and can be used to validate image registration algorithms. Quality of landmark identification was assessed with intra- and inter-rater variability.
In addition to co-registered MRIs, each series of US scans are provided as a reconstructed 3D volume. All images are accessible in MINC2 and NIFTI formats, and the anatomical landmarks were annotated in MNI tag files. Both the imaging data and the corresponding landmarks are available online as the RESECT database at https://archive.norstore.no (search for "RESECT").
The proposed database provides real high-quality multi-modal clinical data to validate and compare image registration algorithms that can potentially benefit the accuracy and efficiency of brain tumor resection. Furthermore, the database can also be used to test other image processing methods and neuro-navigation software platforms.
医学图像处理技术(如图像配准)的进步可以有效提高脑肿瘤手术的准确性和效率。然而,由于此类公共可用存储库的罕见性,通常难以使用真实临床数据来验证这些技术。
从 2011 年至 2016 年在圣奥拉夫大学医院接受手术的 23 名低级别胶质瘤患者中采集了术前磁共振成像(MRI)和术中超声(US)扫描。每位患者均接受钆增强 T1w 和 T2-FLAIR MRI 方案扫描,以显示解剖结构和病理,并且在肿瘤切除前、中、后获得一系列 B 模式超声图像,以跟踪手术进展和组织变形。回顾性地,在不同手术阶段的 US 图像之间、MRI 和 US 之间识别出相应的解剖学标志,并可用于验证图像配准算法。使用内部和内部评估者之间的变异性评估标志识别的质量。
除了配准的 MRI 之外,还提供了每一系列的 US 扫描作为重建的 3D 体积。所有图像都以 MINC2 和 NIFTI 格式提供,并且解剖学标志在 MNI 标记文件中进行了注释。成像数据和相应的地标都可以在网上以 RESECT 数据库的形式获得,网址为 https://archive.norstore.no(搜索“RESECT”)。
该数据库提供了真实的高质量多模态临床数据,可用于验证和比较图像配准算法,从而有可能提高脑肿瘤切除的准确性和效率。此外,该数据库还可以用于测试其他图像处理方法和神经导航软件平台。