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.

概括 使用超高剂量率的新兴放射疗法（RT）模式，称为Flash-RT 在临床前研究和早期临床上，证明了提高RT治疗比率的前所未有的能力 案例。由于缺乏适当的图像引导技术，这些研究仅限于表面 使用现有方法允许允许辐照和简单的情况。这 严重障碍闪存的前景，并在很大程度上限制了其对深座肿瘤的有希望的影响， 构成大多数RT癌症病例。人们广泛认识到，目前使用的剂量测定技术跌落 没有提供必要的指导以在实用的临床环境中提供闪存RT而不暴露 患者面临巨大的风险，远远超出了传统的RT交付。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护闪存-RT准确的传递。我们认为这些 可以通过完善电离辐射引起的声学成像的新兴技术来解决挑战 （IRAI），可以与Flash-RT输送系统内在配对。伊莱是基于已知的 辐射物理学中的热声现象，其中热弹性产生声波 吸收穿透性脉冲高能辐射后，物质的扩展。建立在我们的基础上 具有超声（US）成像，RT物理学，数据分析，具有专业知识的多机构多学科团队， 我们有希望的初步结果，我们假设：（1）由双模式成像系统组成 伊莱和我们（irai-us）可以同时想象组织形态和3D剂量沉积 具有高时空的分辨率的闪存-RT交付； （2）基于机器学习的重建和 异常检测可以有效地改善成像质量并减轻临床错误的错误 翻译。因此，在这个项目中，我们旨在利用Irai-US和Machine的技术潜力 学习开发图像指导平台，以进行有效且安全的闪存交付。我们将 使用计算机模拟（以硅硅）的形式证明其在电子和质子梁上的功效，模仿组织 幻影和相关的体内模型。具体来说，我们将（1）开发和测试双模式成像 3D辐射声学的系统剂量计和闪存的US成像； （2）评估体内 在电子和质子闪存-RT传递期间的IRAI-US双重成像的性能； （3）适应和 改善IRAI体积表示，时间分辨率和使用Deep的Flash-RT的错误检测 机器学习算法（DEEPRAI）朝着有效的临床实施。 影响：我们提出的图像引导的Flash-RT曾经验证，将提供实用，健壮，成本效益， 以及保护闪存交付的独特系统。这些进步将解决当前 挑战阻碍了Flash-RT的临床翻译并实现其限制的承诺 放疗对正常组织的毒性，从而改善癌症患者护理和生活质量。