Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts.
Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts; Department of BioMedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, DKFZ, Heidelberg, Germany; Department of Experimental and Clinical Medicine, Magna Græcia University of Catanzaro, Catanzaro, Italy.
Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):792-800. doi: 10.1016/j.ijrobp.2018.06.024. Epub 2018 Jun 30.
To investigate advanced multimodal methods for pseudo-computed tomography (CT) generation from standard magnetic resonance imaging sequences and to validate the results by intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans. We present 2 novel methods that employ key techniques to enhance pseudo-CTs and investigate the effect on image quality and applicability for IMRT and VMAT planning.
The data set contains CT and magnetic resonance image scans from 15 patients who underwent cranial radiation therapy. For each patient, pseudo-CTs of the head were generated with a patch-based and a voxel-based algorithm. The accuracy of the pseudo-CTs in comparison to clinical CTs was evaluated by mean absolute error, bias, and the Dice coefficient (of bone). IMRT and VMAT plans were created for each patient. Dose distributions were calculated with both the pseudo-CT and the clinical CT scans and compared by gamma tests, dose-volume histograms, and isocenter doses.
The generated pseudo-CTs exhibited average mean absolute errors of 118.7 ± 10.4 HU for the voxel-based algorithm and 73.0 ± 6.4 HU for the patch-based algorithm. The dose calculations were in good agreement and showed gamma test (2 mm, 2%) pass rates for both beam setups (IMRT and VMAT) of over 99% for 14 patients and over 98% for 1 patient.
We showed that the key techniques of our 2 novel algorithms advance the quality of pseudo-CT significantly and generate very competitive pseudo-CTs compared with previously published methods. This quality was confirmed by low dose error in comparison to the ground-truth CT. With the achieved level of accuracy, our patch-based algorithm especially is a candidate for clinical routine use in IMRT and VMAT planning.
研究从标准磁共振成像序列生成伪 CT 的高级多模态方法，并通过调强放疗（IMRT）和容积旋转调强放疗（VMAT）计划验证结果。我们提出了 2 种新颖的方法，它们采用关键技术来增强伪 CT，并研究其对图像质量和 IMRT 和 VMAT 计划适用性的影响。
数据集包含 15 名接受颅放射治疗的患者的 CT 和磁共振图像扫描。对于每个患者，使用基于补丁和基于体素的算法生成头部伪 CT。通过平均绝对误差、偏差和骨的 Dice 系数（bone）评估伪 CT 与临床 CT 的准确性。为每个患者创建了 IMRT 和 VMAT 计划。使用伪 CT 和临床 CT 扫描计算剂量分布，并通过伽马测试、剂量体积直方图和等中心剂量进行比较。
生成的伪 CT 的基于体素算法的平均绝对误差为 118.7±10.4 HU，基于补丁的算法为 73.0±6.4 HU。剂量计算结果非常吻合，对于两种设置（IMRT 和 VMAT），伽马测试（2mm，2%）通过率均超过 99%，14 名患者和 1 名患者的通过率均超过 98%。
我们表明，我们的 2 种新颖算法的关键技术显著提高了伪 CT 的质量，并生成了与以前发表的方法相比非常有竞争力的伪 CT。与真实 CT 相比，低剂量误差证实了这种质量。达到了如此高的精度水平，我们的基于补丁的算法特别适合在 IMRT 和 VMAT 计划中用于临床常规使用。
