Real-time MRI-guided adaptive radiotherapy of unresectable pancreatic cancer

项目摘要 胰腺癌是所有癌症中死亡率最高的，5年生存率仅为9%。手术 仍然是唯一的治愈性治疗选择，尽管只有不到20%的患者适合切除。 大约30-40%的患者存在局部晚期不可切除的肿瘤， 通过标准治疗的长期存活率。消融辐射剂量的使用（生物等效 剂量100 Gy）产生的结果与预后较差的患者的手术切除相当。 功能.然而，由于呼吸运动，必须控制器官运动，以最大限度地减少毒性。 胃肠道在这个项目中，我们将开发新的实时体积MRI技术，可以引导 放射治疗，使使用消融剂量的风险最小。我们的技术，称为MR信号匹配 （MRSIGMA），预先学习3D运动状态，并在离线学习阶段分配唯一的运动签名 并在在线匹配阶段执行快速签名获取和匹配。我们已经证明 实时跟踪肝脏肿瘤，成像延迟（采集加重建）约为250 ms， 马先生。我们将与Elekta合作，在Unity MR-Linac系统上实施MRSIGMA， MRSIGMA的输出与多叶准直器（MLC）系统，使辐射束跟踪3D 移动肿瘤的位置和形状。具体目标如下： 1.为快速运动数据库开发欠采样动态MRI数据的深度学习重建 在离线学习期间生成和在线匹配期间适配 a.开发卷积神经网络，用于快速重建运动解析数据（< 10秒） B.检测解剖学变化，例如运动基线漂移，并相应地调整运动数据库 C.对动态MRI体模和10名志愿者进行初始确认 2.评价MRSIGMA在50例局部肿瘤患者的实时体积运动成像中的潜力 晚期不可切除的胰腺癌 a.准确度假设：实时MRSIGMA不劣于非实时XDGRASP参考 B.再现性假设：两种MRSIGMA扫描的实时成像性能等同 3.在动态体模上开发并验证合作提出的MRSIGMA引导MLC跟踪 关于Elekta a.利用MRSIGMA的输出信息开发MLC控制软件 B.评价跟踪延迟、几何误差、再现性和剂量测定准确度

PROJECT SUMMARY Pancreatic cancer has the highest mortality rate of all cancers, with a 5-year survival rate of only 9%. Surgery still represents the only curative treatment option, though less than 20% of patients are candidates for resection. Approximately 30-40% of patients present with locally advanced unresectable tumors with no significant chance of long-term survival through standard treatments. The use of ablative radiation doses (biologically equivalent doses of 100Gy) produces results that are comparable to surgical resection in patients with inferior prognostic features. However, organ motion, due to respiratory motion, must be managed to minimize toxicity in the gastrointestinal tract. In this project, we will develop novel real-time volumetric MRI technology that can guide radiotherapy to enable the use of ablative doses with minimal risk. Our technique, called MR SIGnature Matching (MRSIGMA), pre-learns 3D motion states and assigns unique motion signatures during an offline learning phase and performs fast signature acquisition and matching during an online matching phase. We have demonstrated real-time tracking of liver tumors with an imaging latency (acquisition plus reconstruction) of about 250 ms using MRSIGMA. We will collaborate with Elekta to implement MRSIGMA on the Unity MR-Linac system and to link the output of MRSIGMA with the multileaf collimator (MLC) system to enable the radiation beam to track the 3D position and shape of the moving tumor in real-time. Specific Aims are as follows: 1. Develop deep learning reconstruction of undersampled dynamic MRI data for rapid motion database generation during offline learning and adaptation during online matching a. Develop a convolutional neural network for rapid reconstruction of motion-resolved data (< 10 seconds) b. Detect anatomical changes, such as motion baseline drifts, and adapt the motion database accordingly c. Perform initial validation on a dynamic MRI phantom and ten volunteers 2. Validate the potential of MRSIGMA for real-time volumetric tumor motion imaging on fifty patients with locally advanced unresectable pancreatic cancer a. Accuracy hypothesis: real-time MRSIGMA is noninferior to a non-real-time XDGRASP reference b. Reproducibility hypothesis: two MRSIGMA scans present equivalent real-time imaging performance 3. Develop and validate on dynamic phantoms the proposed MRSIGMA-guided MLC tracking in collaboration with Elekta a. Develop software to control the MLC with the output of MRSIGMA b. Evaluate tracking latency, geometric error, reproducibility and dosimetric accuracy