Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRI

迈向精准放疗：基于超高质量 4D-MRI 的呼吸运动生理模型

• 批准号: 10653082
• 负责人: Jing Cai
• 金额: $ 41.67万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-18 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10653082
• 关键词: 3-Dimensional; 4D Imaging; 4D MRI; Abdomen; Algorithms; Biological; Biomechanics; Breathing; Cancer Patient; Chest; Communities; Data; Development; Dose; Four-dimensional; Future; Goals; Human; Hybrids; Image; Imaging Techniques; Imaging technology; Liver; Lung; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Medical Imaging; Medicine; Methods; Modeling; Modernization; Morphologic artifacts; Morphology; Motion; Normal tissue morphology; Paint; Patients; Phase; Physiological; Predisposition; Process; Prospective Studies; Radiation Dose Unit; Radiation therapy; Research; Resolution; Sampling; Scheme; Techniques; Technology; Therapeutic; Time; Trees; Walking; cancer radiation therapy; clinical implementation; digital; image guided; image registration; improved; improved outcome; multimodality; novel; personalized medicine; physiologic model; public health relevance; radiation mitigation; radiation-induced injury; radiomics; respiratory; respiratory imaging; spatiotemporal; therapy development; tool; tumor

ABSTRACT Despite numerous advances in radiotherapy in the past decade, which have effectively enhanced local or locoregional tumor control for many patients, there remains substantial room for improvement. A compelling need in today's era of precision radiotherapy is to further widen the therapeutic window and improve radiation dose conformity to the defined target volume, through technological improvements such as advanced image guidance and motion management. Four-dimensional (4D) imaging and deformable image registration (DIR) are two of the most important tools behind many recent radiotherapy advances, but both are facing significant challenges as the requirement for precision increases. Major limitations of current 4D imaging technology include low temporal/spatial resolution, long image acquisition time, suboptimal tumor contrast, and susceptibility to artifacts caused by irregular breathing. Meanwhile, current DIR techniques focus on morphological similarity but not on the physiological plausibility of the deformation, leading to unrealistic results in various applications. These limitations have significantly hampered the advancement of precision radiotherapy. Our long-term goal is to enhance precision radiotherapy through the development and clinical implementation of advanced image guidance and motion management techniques. The overall objective of this application is to develop, cross-- fertilize, and evaluate two techniques: (a) ultra-quality 4D-MRI and (b) physiologically-based motion modeling, for precision radiotherapy applications. Aim 1 is to develop and optimize a 4D-MRI technique for imaging respiratory motion in the thorax and abdomen at ultra-high spatiotemporal resolution. Aim 2 is to develop a physiologically-based motion modeling method for respiratory motion estimation. Aim 3 is to evaluate ultra- quality 4D-MRI and physiological motion modeling in a patient study. Aim 4 is to construct physiologically realistic 4D digital phantoms for future development of precision radiotherapy applications. Successful completion of these aims will yield powerful image guidance and motion management tools for precision radiotherapy. Such improvements will take precision radiotherapy to a whole new level, by significantly improving radiation dose conformity and opening doors for biological-based treatment adaptation for more effective personalized treatment. The proposed research will have a high impact to the fields of both radiotherapy and medical imaging. It will trigger a wave of extensive studies on a number of new and existing applications such as 4D radiotherapy, radiomics, human digital phantom, function-based dose painting, adaptive planning, etc. Most importantly, it will ultimately improve outcomes for cancer patients by improving our ability to precisely deliver radiation treatment to the target and mitigate radiation-induced injury to normal tissues.

摘要 尽管放射治疗在过去十年中取得了许多进展，有效地增强了当地或 局部区域肿瘤控制对于许多患者来说，仍有很大的改善空间。令人信服的 当今精确放射治疗时代的需要是进一步拓宽治疗窗口，改善辐射 通过先进图像等技术改进，剂量符合所定义的目标体积 指导和动作管理。四维(4D)成像和可变形图像配准(DIR) 是最近许多放射治疗进展背后的两个最重要的工具，但两者都面临着重大的 随着对精度的要求不断提高，这也带来了挑战。当前4D成像技术的主要局限性包括 时间/空间分辨率低，图像采集时间长，肿瘤对比度不佳，对 由不规律的呼吸引起的人工制品。同时，目前的DIR技术主要集中在形态相似性上，但 没有对生理上的似是而非的变形，导致在各种应用中产生不切实际的结果。 这些局限性严重阻碍了精确放射治疗的发展。我们的长期目标 是通过先进影像的开发和临床实施来加强精确放射治疗 引导和运动管理技术。此应用程序的总体目标是开发、交叉- 对两种技术：(A)超高质量的4D-MRI和(B)基于生理的运动建模， 用于精确放射治疗应用。目标1是开发和优化4D-MRI成像技术 在超高时空分辨率下胸部和腹部的呼吸运动。目标2是开发一种 用于呼吸运动估计的基于生理的运动建模方法。目标3是评估超 患者研究中的高质量4D-MRI和生理性运动建模。目标4是构建生理上的逼真 4D数字体模是未来精确放射治疗应用的发展方向。成功完成 这些目标将为精确放射治疗提供强大的图像引导和运动管理工具。是这样的 通过显著提高辐射剂量，改进将把精确放射治疗带到一个全新的水平。 符合并为基于生物的治疗适应打开大门，以实现更有效的个性化 治疗。这项研究将对放射治疗和医学成像领域产生重大影响。 它将引发一波对一些新的和现有的应用程序的广泛研究，如4D放射治疗， 放射组学、人体数字体模、基于功能的剂量绘制、自适应规划等。最重要的是，它将 通过提高我们精确提供放射治疗的能力，最终改善癌症患者的预后 并减轻辐射对正常组织的损伤。

项目成果

MRI super-resolution via realistic downsampling with adversarial learning.

• DOI: 10.1088/1361-6560/ac232e
• 发表时间: 2021-10-05
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Huang B;Xiao H;Liu W;Zhang Y;Wu H;Wang W;Yang Y;Yang Y;Miller GW;Li T;Cai J
• 通讯作者: Cai J

Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion.

• DOI: 10.1002/pro6.1167
• 发表时间: 2022-09
• 期刊: Precision radiation oncology

Motion robust 4D-MRI sorting based on anatomic feature matching: A digital phantom simulation study.

• DOI: 10.1016/j.radmp.2020.01.003
• 发表时间: 2020-03
• 期刊: Radiation medicine and protection
• 作者: Yang, Zi;Ren, Lei;Yin, Fang-Fang;Liang, Xiao;Cai, Jing
• 通讯作者: Cai, Jing

A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy.

• DOI: 10.1002/mp.15542
• 发表时间: 2022-05
• 期刊: MEDICAL PHYSICS
• 影响因子: 3.8
• 作者: Xiao, Haonan;Ni, Ruiyan;Zhi, Shaohua;Li, Wen;Liu, Chenyang;Ren, Ge;Teng, Xinzhi;Liu, Weiwei;Wang, Weihu;Zhang, Yibao;Wu, Hao;Lee, Ho-Fun Victor;Cheung, Lai-Yin Andy;Chang, Hing-Chiu Charles;Li, Tian;Cai, Jing
• 通讯作者: Cai, Jing