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434 Ionizing radiation acoustic imaging (iRAI) for volumetric mapping the dose deep in the liver during radiation therapy

Part of: JCTS_2023_ABSTRACT_COLLECTION

Published online by Cambridge University Press:  24 April 2023

Wei Zhang ,
Ibrahim Oraiqat ,
Dale Litzenberg ,
Kai-Wei Chang ,
Scott Hadley ,
Noora Ba Sunbul ,
Martha M. Matuszak ,
Christopher Tichacek ,
Eduardo G. Moros  and
Paul L. Carson
...Show all authors
Wei Zhang
Affiliation:
Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
Ibrahim Oraiqat
Affiliation:
Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida
Dale Litzenberg
Affiliation:
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
Kai-Wei Chang
Affiliation:
Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
Scott Hadley
Affiliation:
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
Noora Ba Sunbul
Affiliation:
Department of Nuclear Engineering, University of Michigan, Ann Arbor, Michigan
Martha M. Matuszak
Affiliation:
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan Department of Nuclear Engineering, University of Michigan, Ann Arbor, Michigan
Christopher Tichacek
Affiliation:
Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
Eduardo G. Moros
Affiliation:
Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
Paul L. Carson
Affiliation:
Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan Department of Radiology, University of Michigan, Ann Arbor, Michigan
Kyle C. Cuneo
Affiliation:
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
Xueding Wang
Affiliation:
Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan Department of Radiology, University of Michigan, Ann Arbor, Michigan
Issam El Naqa
Affiliation:
Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
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Abstract

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OBJECTIVES/GOALS: The goal of this study was to develop a clinically applicable technique to increase the precision of in vivo dose monitoring during radiation therapy by mapping the dose deposition and resolving the temporal dose accumulation while the treatment is being delivered in real time. METHODS/STUDY POPULATION: Ironizing radiation acoustic imaging (iRAI) is a novel imaging concept with the potential to map the delivered radiation dose on anatomic structure in real time during external beam radiation therapy without interrupting the clinical workflow. The iRAI system consisted of a custom-designed two-dimensional (2D) matrix transducer array with integrated preamplifier array, driven by a clinic-ready ultrasound imaging platform. The feasibility of iRAI volumetric imaging in mapping dose delivery and real-time monitoring of temporal dose accumulation in a clinical treatment plan were investigated with a phantom, a rabbit model, and a cancer patient. RESULTS/ANTICIPATED RESULTS: The total dose deposition and temporal dose accumulation in 3D space of a clinical C-shape treatment plan in a targeted region were first imaged and optimized in a phantom. Then, semi-quantitative iRAI measurements were achieved in an in vivo rabbit model. Finally, for the first time, real-time visualization of radiation dose delivered deep in a patient with liver metastases was performed with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the radiation dose deposition during treatment. DISCUSSION/SIGNIFICANCE: Described here is the pioneering role of an iRAI system in mapping the 3D radiation dose deposition of a complex clinical radiotherapy treatment plan. iRAI offers a cost-effective and practical solution for real-time visualization of 3D radiation dose delivery, potentially leading to personalized radiotherapy with optimal efficacy and safety.

Type
Team Science
Information
Journal of Clinical and Translational Science , Volume 7 , Issue s1 , April 2023 , pp. 129
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2023. The Association for Clinical and Translational Science