RESECT: a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries

Purpose: 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.
Acquisition and validation methods: Pre-operative magnetic resonance images (MRI), and intra-operative ultrasound (US) scans were acquired from 24 patients with low-grade glioma 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.
Data format and access: In addition to co-registered MRIs, each series of US scans are provided as a reconstructed 3D volume. All images are accessible in MINC 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.
Potential impact: 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.

数据集用途：医学图像处理技术（例如图像配准（image registration））的发展，可有效提升脑肿瘤手术的精度与效率。但由于此类公开可用的数据集仓库较为稀缺，利用真实临床数据对上述技术进行验证往往颇具挑战。
数据采集与验证方法：本数据集采集自2011年至2016年间，在圣奥拉夫大学医院（St. Olavs University Hospital）接受手术的24例低级别胶质瘤患者，包含术前磁共振成像（magnetic resonance images, MRI）与术中超声（ultrasound, US）扫描数据。所有患者均接受钆增强T1加权（Gadolinium-enhanced T1w）及T2液体衰减反转恢复（T2-FLAIR）MRI扫描，以显示其解剖结构与病理特征；同时在肿瘤切除术前、术中及术后采集一系列B型超声图像，用于追踪手术进程与组织形变。回顾性地在不同手术阶段的超声图像之间，以及MRI与US图像之间标注了对应解剖标志点，可用于验证图像配准算法。研究同时通过组内与组间评分者变异度，对解剖标志点的标注质量进行了评估。
数据格式与获取方式：除配准后的MRI数据外，每一组超声扫描数据均以重建三维容积形式提供。所有图像均支持MINC与NIFTI格式读取，解剖标志点则以MNI标记文件（MNI tag files）格式完成标注。影像数据及配套解剖标志点均以RESECT数据库的形式在线公开。
潜在应用价值：本数据集提供真实高质量的多模态临床数据，可用于验证与对比图像配准算法，有望提升脑肿瘤切除术的精度与效率。此外，该数据集还可用于测试其他图像处理方法及神经导航软件平台。