An Ionizing Radiation Acoustics Imaging (iRAI) Approach for guided Flash Radiotherapy

用于引导闪光放射治疗的电离辐射声学成像 (iRAI) 方法

• 批准号: 10707124
• 负责人: THOMAS R. BORTFELD
• 金额: $ 65.27万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707124
• 关键词: 3-Dimensional; 3D ultrasound; Acoustics; Address; Algorithms; Anatomy; Animal Model; Biomedical Engineering; Cancer Patient; Clinical; Computer Simulation; Data; Data Analytics; Data Science; Dedications; Deposition; Detection; Development; Dose; Dose Rate; Electron Beam; Electrons; Emerging Technologies; Ensure; FarGo; Film; Geometry; Height; Image; Imaging Techniques; Institution; Ionizing radiation; Ions; Lesion; Machine Learning; Malignant Neoplasms; Maps; Measures; Methods; Modality; Monitor; Monte Carlo Method; Morphology; Mus; Noise; Normal tissue morphology; Outcomes Research; Patient Care; Patients; Penetration; Performance; Physics; Physiologic pulse; Play; Positioning Attribute; Pre-Clinical Model; Protons; Quality of life; Radiation; Radiation Biophysics; Radiation Oncology; Radiation Physics; Radiation therapy; Radiology Specialty; Rattus; Real-Time Systems; Resolution; Risk; Role; Safety; Signal Transduction; Structure; System; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Translations; Treatment Efficacy; Treatment-related toxicity; Ultrasonography; Width; X-Ray Computed Tomography; absorption; acoustic imaging; anatomic imaging; attenuation; cancer radiation therapy; clinical implementation; clinical translation; cost effective; deep learning; deep learning algorithm; design; detector; dosimetry; ex vivo imaging; experimental study; falls; image guided; imaging approach; imaging modality; imaging system; improved; in silico; in vivo; in vivo Model; in vivo imaging; interoperability; irradiation; machine learning algorithm; multidisciplinary; pre-clinical; preclinical study; proton beam; quality assurance; real time monitoring; reconstruction; risk minimization; soft tissue; spatiotemporal; temporal measurement; tool; tumor; ultrasound

SUMMARY An emerging radiotherapy (RT) modality that utilizes ultra-high dose rate, known as FLASH-RT, has demonstrated unprecedented ability for improving RT therapeutic ratio in preclinical studies and early clinical cases. Because of lack of appropriate image-guidance technologies, these studies have been limited to superficial irradiations and simplistic cases where monitoring of delivered dose is permissible using existing methods. This severely handicaps the prospects of FLASH-RT and largely limits its promising impact for deep seated tumors, which constitute most of RT cancer cases. It is widely recognized that currently used dosimetry technologies fall short of providing the necessary guidance to deliver FLASH-RT in a practical clinical setting without exposing the patient to tremendous risks that go far beyond the traditional RT delivery. Undoubtedly, there is an unmet need to develop in vivo image-guidance techniques to safeguard FLASH-RT accurate delivery. We hold that these challenges can be resolved by refining the emerging technology of ionizing radiation-induced acoustic imaging (iRAI), which can be intrinsically paired with FLASH-RT delivery systems. iRAI is based on the known thermoacoustic phenomenon in radiation physics, where acoustic waves are generated from thermoelastic expansion of a substance following absorption of penetrating pulsated high energy radiation. Building upon our multi-institutional multidisciplinary team with expertise in ultrasound (US) imaging, RT physics, data analytics, and our promising preliminary results, we hypothesize that: (1) a dual-modality imaging system comprised of iRAI and US (iRAI-US) can simultaneously image both tissue morphology and 3D dose deposition during FLASH-RT delivery with high spatio-temporal resolutions; and (2) machine learning based reconstruction and anomaly detection can effectively improve imaging quality and mitigate errors, respectively, for clinical translation. Therefore, in this project we aim to exploit the technological potentials of iRAI-US and machine learning for developing an image-guidance platform for effective and safe FLASH-RT delivery. We will demonstrate its efficacy with electron and proton beams using computer simulations (in silico), tissue mimicking phantoms, and relevant preclinical in vivo models. Specifically, we will (1) develop and test a dual-mode imaging system for 3D radiation-acoustics dosimetry and US imaging for FLASH-RT; (2) evaluate the in vivo performance of iRAI-US dual imaging during electron and proton FLASH-RT deliveries; and (3) adapt and improve iRAI volumetric representation, temporal resolution and error detection for FLASH-RT using deep machine learning algorithms (DeepRAI) towards effective clinical implementation. Impact: Our proposed image-guided FLASH-RT, once validated, will offer a practical, robust, cost-effective, and unique system for safeguarding FLASH-RT delivery. These advancements will address the current challenges impeding the clinical translation of FLASH-RT and enable achieving its promise of limiting radiotherapy toxicity to normal tissues and thereby improving cancer patient care and quality of life.

总结 一种利用超高剂量率的新兴放射治疗（RT）模式，称为FLASH-RT， 在临床前研究和早期临床研究中证明了前所未有的提高RT治疗率的能力 例由于缺乏合适的图像引导技术，这些研究仅限于表面的 辐射和简单的情况下，其中允许使用现有方法监测输送剂量。这 严重阻碍了FLASH-RT的前景，并在很大程度上限制了其对深部肿瘤的有希望的影响， 这构成了大多数RT癌症病例。人们普遍认为，目前使用的剂量测定技术 缺乏在实际临床环境中提供FLASH-RT的必要指导， 患者面临的巨大风险远远超出了传统的RT输送。毫无疑问，有一个未满足的 需要开发体内图像引导技术，以保障FLASH-RT的准确输送。我们认为， 这些挑战可以通过改进电离辐射诱导声学成像的新兴技术来解决 （iRAI），其可以固有地与FLASH-RT递送系统配对。iRAI基于已知的 辐射物理学中的热声现象，其中声波由热弹性产生 物质在吸收穿透性脉冲高能辐射后的膨胀。建立在我们的 多机构多学科团队，拥有超声（US）成像、RT物理学、数据分析 和我们有希望的初步结果，我们假设：（1）一个双模态成像系统，包括 iRAI和US（iRAI-US）可以同时对组织形态和3D剂量沉积进行成像， 具有高时空分辨率的FLASH-RT递送;以及（2）基于机器学习的重建和 异常检测可以有效地提高成像质量和减少误差，分别为临床， 翻译.因此，在本项目中，我们的目标是利用iRAI-US和机器的技术潜力， 学习开发一个图像引导平台，以实现有效和安全的FLASH-RT输送。我们将 使用计算机模拟（计算机模拟）、组织模拟 Phantomy和相关的临床前体内模型。具体来说，我们将（1）开发和测试一种双模式成像 系统的三维辐射声学剂量学和超声成像的FLASH-RT;（2）评价在体 在电子和质子FLASH-RT输送期间iRAI-US双重成像的性能;以及（3）适应和 使用深度增强技术改进iRAI体积表示、时间分辨率和错误检测 机器学习算法（DeepRAI）用于有效的临床实施。 影响：我们提出的图像引导的FLASH-RT，一旦验证，将提供一个实用的，强大的，具有成本效益， 和独特的系统来保护FLASH-RT传输。这些进步将解决当前 阻碍FLASH-RT临床转化的挑战，并实现其限制 治疗对正常组织的毒性，从而改善癌症患者的护理和生活质量。