CT scan data underlying the PhD dissertation: Numerical and deep learning algorithms for automated quality assurance in proton therapy

The dataset contains CT scans used as input for the algorithm developed in chapters 2 and 3 of the dissertation. Using a CT scan and a treatment plan as inputs, dose and dose change computations in regions of interest can be performed. Specifically, the dataset contains:<br>a head and neck CT scan obtained from the CORT dataset [1],a prostate CT scan obtained from the Cancer Imaging Archive [2],and multiple self-made custom water box CT scans. In addition to a homogeneous water box CT scan (i.e., a cube with uniform 0 Hounsfield Units (HU) composition), there are scans where a slab with half the side-length of the cube and composition of either bone (1000 HU) or air (-1000 HU) is inserted in the water box at varying distances from the middle point.<br>All the scans consist of CT slices and are stored in the DICOM format. To correctly read, relate to each other and further process the different CT slices, appropriate DICOM reading software (e.g., the pydicom Python package) must be used. <br>References:[1] - Craft, D., Bangert, M., Long, T., Papp, D., &amp; Unkelbach, J. (2014). <em>Supporting material for: "Shared data for IMRT optimization research: the CORT dataset"</em> [Data set]. GigaScience Database. https://doi.org/10.5524/100110[2] - Yorke, A. A., McDonald, G. C., Solis, D., &amp; Guerrero, T. (2019). <strong>Pelvic Reference Data (Version 1) [Data set].</strong> The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.WOSKQ5OO本数据集包含用于本论文第2、3章所开发算法的输入CT扫描影像。该算法以CT扫描影像与治疗计划作为输入，可对感兴趣区域内的剂量及剂量变化进行计算。具体而言，本数据集包含： 1. 来自CORT数据集[1]的头颈部CT扫描影像； 2. 来自癌症影像档案库（Cancer Imaging Archive）[2]的前列腺CT扫描影像； 3. 多份自制定制水模CT扫描影像。 除均匀水模CT扫描影像（即由亨氏单位（Hounsfield Unit, HU）值为0的均匀物质构成的立方体）外，本数据集还包含在水模中插入一块边长为原立方体一半的插入板的扫描影像，该插入板的物质成分为骨（HU值1000）或空气（HU值-1000），且插入位置距离中心点的距离各不相同。 所有扫描影像均由CT断层切片组成，以DICOM格式存储。若要正确读取、关联各CT切片并进行后续处理，需使用适配的DICOM读取软件（例如Python的pydicom库）。 参考文献： [1] Craft, D., Bangert, M., Long, T., Papp, D., & Unkelbach, J. (2014). 《用于调强放疗优化研究的共享数据：CORT数据集》支持材料[数据集]. GigaScience数据库. https://doi.org/10.5524/100110 [2] Yorke, A. A., McDonald, G. C., Solis, D., & Guerrero, T. (2019). 盆腔参考数据（版本1）[数据集]. 癌症影像档案库. https://doi.org/10.7937/TCIA.2019.WOSKQ5OO

搜集汇总

背景与挑战

背景概述

该数据集包含用于质子治疗自动质量保证算法开发的CT扫描数据，具体包括头颈部、前列腺及自定义水模的DICOM格式扫描文件，主要用于支持博士论文中的数值和深度学习算法研究。数据来源包括公开医学影像数据库和自建模拟，适用于医学物理和肿瘤学领域的质量保证应用。