Ger Rachel B, Yang Jinzhong, Ding Yao, Jacobsen Megan C, Cardenas Carlos E, Fuller Clifton D, Howell Rebecca M, Li Heng, Stafford R Jason, Zhou Shouhao, Court Laurence E
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
Med Phys. 2018 Jul 14. doi: 10.1002/mp.13090.
Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images.
Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model.
The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR.
The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.
磁共振成像(MRI)可在不使用电离辐射的情况下对患者解剖结构进行无创评估。鉴于此以及其高软组织对比度,MRI的使用有所增加,并且在纵向研究中具有应用潜力,此类研究旨在比较患者在不同时间点的解剖结构或肿瘤变化。可变形图像配准对此类研究可能有用。在此,我们描述了两个数据集,可用于评估MR图像系统的配准准确性,因为不能假定其与在CT图像上测量的配准准确性相同。
创建了两组图像以测试配准准确性。(a)通过将十个0.35毫米的金标记物放入猪肉中制作了一个猪模型。将猪模型固定在带有可移动分隔物的塑料容器中。在猪模型以四种不同方式压缩的情况下采集了T1加权、T2加权和CT图像。由于所选的体素大小,标记物在MR图像上不可见,因此它们不会干扰测量的配准准确性,而标记物在CT图像上可见,并用于识别不同位置之间已知的变形。(b)使用28例头颈部鳞状细胞癌患者在放疗前、放疗中和放疗后的MR扫描创建了合成图像。利用这些患者扫描数据创建了患者间和患者内变异模型。使用患者间模型创建了四张合成预处理图像,并使用患者内模型为每张预处理图像创建了四张合成后处理图像。
提供了猪模型在四个位置的T1加权、T2加权和CT扫描图像。提供了四张T1加权合成预处理图像,每张图像分别配有四张合成后处理图像,以及四张T2加权合成预处理图像,每张图像也分别配有四张合成后处理图像。此外,还提供了用于生成合成后处理图像的应用变形矢量场。这些数据可在癌症成像存档(TCIA)的MRI-DIR数据集中获取。
所提议的数据库提供了两组图像(一组是采集的,一组是计算机生成的),用于评估可变形图像配准的准确性。每种技术都提供了T1加权和T2加权图像,因为两种类型图像中不同的图像对比度可能会影响配准准确性。