Accurate 4D Liver Tumor Localization for Radiotherapy using Contrast-Agent-Free X-ray Imaging and Liver Biomechanical Modeling

使用无造影剂 X 射线成像和肝脏生物力学建模进行精确的 4D 肝脏肿瘤定位以进行放射治疗

• 批准号: 10684825
• 负责人: You Zhang
• 金额: $ 40.33万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684825
• 关键词: 3-Dimensional; 3D Print; Affect; Anatomy; Biomedical Engineering; Blood Vessels; Breathing; Cirrhosis; Clinic; Clinical; Clinical Research; Cone; Contrast Media; Data; Dose; Effectiveness; Elasticity; Elements; Generations; Goals; Image; Imaging Techniques; Implant; Institution; Invaded; Lead; Liver; Liver Cirrhosis; Liver neoplasms; Location; Malignant neoplasm of liver; Manuals; Medical; Metastatic Neoplasm to the Liver; Methods; Modeling; Motion; Nodal; Normal tissue morphology; Operative Surgical Procedures; Organ; Patients; Positioning Attribute; Process; Prospective Studies; Protocols documentation; Radiation; Radiation Dose Unit; Radiation therapy; Reference Standards; Research Project Grants; Respiration; Respiratory Diaphragm; Risk; Roentgen Rays; Safety; Site; Study models; System; Targeted Radiotherapy; Techniques; Technology; Therapeutic; Tissues; Toxic effect; Training; Uncertainty; Work; Workload; X-Ray Medical Imaging; biomechanical model; cancer therapy; clinical practice; clinical translation; cone-beam computed tomography; four-dimensional computed tomography; image guided; improved; innovation; liver function; migration; prevent; prospective; research clinical testing; response; tumor; vector

ABSTRACT Radiotherapy has become an increasingly effective technique to treat patients affected by primary and metastatic liver cancers, especially after the advent of stereotactic body radiotherapy (SBRT). SBRT delivers a large and focused radiation dose to liver tumors and achieves superior local control and survival. However, for current liver SBRT, a volume usually much larger than the actual tumor (with treatment margins up to 15 mm beyond the tumor boundary) is treated, to account for tumor localization uncertainties under the cone-beam computed tomography (CBCT) image guidance. Such a large treatment volume incurs more radiation to normal liver tissues and organs-at-risks and raises the concern of normal tissue toxicity, especially for patients with liver cirrhosis and a limited healthy liver tissue reserve. It also prevents further dose escalation to maximize the effectiveness of SBRT. Currently, there lacks a reliable technique to accurately localize liver tumors by CBCT, mostly due to the respiration-induced liver motion and the low contrast of liver tumors against the normal liver tissues. In response to PAR-19-158, we propose to develop a biomechanical modeling-guided 4DCBCT technique (Bio- 4DCBCT), which generates 4DCBCT images to capture the liver tumor motion for accurate 4D localization. The Bio-4DCBCT incorporates liver biomechanical modeling into the 4DCBCT generation process, which can substantially improve the localization accuracy of CBCT at low-contrast regions. The goal of this study is to develop, optimize and validate the Bio-4DCBCT technique to achieve ~ 2 mm tumor localization accuracy. In this study we will pursue three specific aims: SA1. Optimize Bio-4DCBCT through a retrospective patient study. SA2. Evaluate Bio-4DCBCT by developing a motion-enabled, deformable, physical liver phantom. SA3. Evaluate Bio-4DCBCT through a prospective clinical study by comparing Bio-4DCBCT with the current clinically-applied techniques. The innovation of the project is the Bio-4DCBCT technique and its application for accurate 4D liver tumor localization, which leads to substantial treatment volume/margin reduction to achieve safer, more viable and more effective liver SBRT. The Bio-4DCBCT technique is developed on the conventional CBCT system under standard image acquisition protocols, which will be readily applicable in radiotherapy clinics worldwide without hardware upgrade or image acquisition protocol change. The successful clinical translation of this technology is expected to create a favorable shift in current tumor localization paradigms, not only for liver but also for other tumor sites where low tissue contrast adversely affects the radiotherapy treatment accuracy.

摘要 放射治疗已成为治疗受原发和转移影响的患者的一种日益有效的技术 肝癌，特别是在立体定向全身放射治疗(SBRT)出现后。SBRT提供了一个大型和 聚焦放射剂量对肝脏肿瘤的影响，并实现卓越的局部控制和生存。然而，对于目前的肝脏 SBRT，体积通常比实际肿瘤大得多(治疗边缘超过15 mm 肿瘤边界)进行治疗，以解决锥束计算机下肿瘤定位的不确定性 层析成像(CBCT)图像制导。如此大的治疗量会对正常肝组织产生更多的辐射 和处于危险中的器官，并引起了对正常组织毒性的关注，特别是对肝硬变患者 以及有限的健康肝组织储备。它还可以防止进一步的剂量增加，以最大限度地提高疗效 对SBRT的影响。目前，CBCT缺乏一种可靠的技术来准确定位肝脏肿瘤，这主要是由于 呼吸引起的肝脏运动和肝肿瘤与正常肝组织的低对比度。在……里面 作为对PAR-19-158的响应，我们建议开发一种生物力学模型引导的4DCBCT技术(Bio- 4DCBCT)，生成4DCBCT图像以捕捉肝脏肿瘤的运动，以进行准确的4D定位。这个 BIO-4DCBCT将肝脏生物力学建模融入到4DCBCT的生成过程中，可以 大大提高了CBCT在低对比度区域的定位精度。这项研究的目标是 开发、优化和验证Bio-4DCBCT技术，以达到~2 mm的肿瘤定位精度。在……里面 这项研究我们将追求三个具体目标：SA1。通过回顾患者研究优化Bio-4DCBCT。 SA2.通过开发可运动、可变形的物理肝脏模型来评估Bio-4DCBCT。SA3.评估 Bio-4DCBCT与目前临床应用比较的前瞻性临床研究 技巧。该项目的创新之处在于Bio-4DCBCT技术及其在精确4D肝脏中的应用 肿瘤定位，这导致大幅减少治疗量/边际，以实现更安全、更可行 和更有效的肝脏SBRT。Bio-4DCBCT技术是在常规CBCT系统上发展起来的 在标准图像采集协议下，这将很容易适用于世界各地的放射治疗诊所 无需硬件升级或图像采集协议更改。这本书的成功临床翻译 技术有望为目前的肿瘤定位模式带来有利的转变，不仅是针对肝脏，也是针对肝脏 也适用于组织对比度低的其他肿瘤部位，这些部位的组织对比度对放射治疗的准确性有不利影响。

项目成果

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).

• DOI: 10.1088/1361-6560/acb30d
• 发表时间: 2023-02-06
• 期刊: Physics in medicine and biology
• 影响因子: 3.5

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling.

• DOI: 10.1088/1361-6560/ac6b7b
• 发表时间: 2022-05-24
• 期刊: PHYSICS IN MEDICINE AND BIOLOGY
• 影响因子: 3.5
• 作者: Shao, Hua-Chieh;Wang, Jing;Bai, Ti;Chun, Jaehee;Park, Justin C.;Jiang, Steve;Zhang, You
• 通讯作者: Zhang, You

Real-time liver tumor localization via combined surface imaging and a single x-ray projection.

• DOI: 10.1088/1361-6560/acb889
• 发表时间: 2023-03-09
• 期刊: Physics in medicine and biology
• 影响因子: 3.5

ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge.

• DOI: 10.7759/cureus.40895
• 发表时间: 2023-06
• 期刊: Cureus
• 作者: Li Y;Li Z;Zhang K;Dan R;Jiang S;Zhang Y
• 通讯作者: Zhang Y

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

• DOI: 10.1088/1361-6560/ad46dc
• 发表时间: 2024-06-07
• 期刊: PHYSICS IN MEDICINE AND BIOLOGY
• 影响因子: 3.5
• 作者: Shao，Hua-Chieh;Mengke，Tielige;Zhang，You
• 通讯作者: Zhang，You