Aljaafari Lamyaa, Speight Richard, Buckley David L, Al-Qaisieh Bashar, Andersson Sebastian, Bird David
Leeds Institute of Cardiovascular & Metabolic Medicine (LICAMM), University of Leeds, Woodhouse, Leeds, LS2 9JT, United Kingdom.
Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds, LS9 7TF, United Kingdom.
Biomed Phys Eng Express. 2025 Apr 11;11(3). doi: 10.1088/2057-1976/adc818.
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated for eleven patients using a CycleGAN algorithm. Clinical volumetric modulated arc treatment plans (VMAT) were calculated on CT and recalculated on sCT, and dose differences were assessed using dose volume histogram (DVH). For position verification, the sCT images were validated as reference images to 4D cone beam computed tomography (4D CBCT) by calculating the translational and rotational differences between sCT and CT registrations to 4D CBCT.The mean dose differences for the planning target volume (PTV) and organs at risk (OAR) between the CT and sCT plans were 0.0% and < 0.5%, respectively. For positioning verification, the systematic translational and rotational differences were < 0.5 mm and < 0.5°, respectively in all directions.This is the first study to validate a sCT model for liver cancer in terms of both dosimetry and patient positioning, marking a significant step in demonstrating the feasibility of an MRI-only workflow for treating liver cancer. The generated sCTs showed dosimetric differences within clinically acceptable levels and were successfully used as reference images for treatment position verification. This CycleGAN model is accessible through the research version of a commercial vendor, with potential for development as a clinical solution.
仅使用磁共振成像（MRI）的工作流程需要合成计算机断层扫描（sCT）图像来进行剂量计算。本研究评估了深度学习生成的sCT用于肝脏放疗时的剂量测定和患者定位准确性。使用循环生成对抗网络（CycleGAN）算法为11名患者生成了sCT图像。在CT上计算临床容积调强弧形治疗计划（VMAT），并在sCT上重新计算，使用剂量体积直方图（DVH）评估剂量差异。为了进行位置验证，通过计算sCT与CT到四维锥形束计算机断层扫描（4D CBCT）配准之间的平移和旋转差异，将sCT图像验证为4D CBCT的参考图像。CT和sCT计划之间计划靶区（PTV）和危及器官（OAR）的平均剂量差异分别为0.0%和<0.5%。对于定位验证，所有方向上的系统平移和旋转差异分别<0.5毫米和<0.5°。这是第一项在剂量测定和患者定位方面验证肝癌sCT模型的研究，标志着在证明仅使用MRI治疗肝癌工作流程的可行性方面迈出了重要一步。生成的sCT显示出在临床可接受水平内的剂量差异，并成功用作治疗位置验证的参考图像。这个CycleGAN模型可以通过商业供应商的研究版本获得，具有开发成临床解决方案的潜力。
