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基于字典的质子射程验证的原声学剂量图成像

Dictionary-based protoacoustic dose map imaging for proton range verification.

作者信息

Freijo Clara, Herraiz Joaquin L, Sanchez-Parcerisa Daniel, Udias José Manuel

机构信息

Nuclear Physics Group, EMFTEL and IPARCOS, Faculty of Physical Sciences, University Complutense of Madrid, CEI Moncloa, 28040 Madrid, Spain.

Health Research Institute of the Hospital Clinico San Carlos (IdISSC), 28040 Madrid, Spain.

出版信息

Photoacoustics. 2021 Jan 16;21:100240. doi: 10.1016/j.pacs.2021.100240. eCollection 2021 Mar.

DOI:10.1016/j.pacs.2021.100240
PMID:33520652
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7820918/
Abstract

Proton radiotherapy has the potential to provide state-of-the-art dose conformality in the tumor area, reducing possible adverse effects on surrounding organs at risk. However, uncertainties in the exact location of the proton Bragg peak inside the patient prevent this technique from achieving full clinical potential. In this context, verification of the range of protons in patients is key to reduce uncertainty margins. Protoacoustic range verification employs acoustic pressure waves generated by protons due to the radio-induced thermoacoustic effect to reconstruct the dose deposited in a patient during proton therapy. In this paper, we propose to use the knowledge of the shape of the proton dose distribution to create a dictionary with the expected ultrasonic signals at predetermined detector locations. Using this dictionary, the reconstruction of deposited dose is performed by matching pre-calculated dictionary acoustic signals with data acquired online during treatment. The dictionary method was evaluated on a single-field proton plan for a prostate cancer patient. Dose calculation was performed with the open-source treatment planning system , while acoustic wave propagation was carried out with . We studied the ability of the proposed dictionary method to detect range variations caused by anatomical changes in tissue density, and alterations of lateral and longitudinal beam position. Our results show that the dictionary-based protoacoustic method was able to identify the changes in range originated by all the alterations introduced, with an average accuracy of 1.4 mm. This procedure could be used for verification, comparing the measured signals with the precalculated dictionary.

摘要

质子放疗有潜力在肿瘤区域提供最先进的剂量适形性,减少对周围危险器官可能产生的不良反应。然而,患者体内质子布拉格峰的确切位置存在不确定性,这使得该技术无法充分发挥其临床潜力。在这种情况下,验证患者体内质子的射程是减少不确定性余量的关键。原声波射程验证利用质子因辐射诱导热声效应产生的声压波来重建质子治疗期间沉积在患者体内的剂量。在本文中,我们建议利用质子剂量分布形状的知识,在预定的探测器位置创建一个包含预期超声信号的字典。利用这个字典,通过将预先计算的字典声信号与治疗期间在线采集的数据进行匹配来进行沉积剂量的重建。对一名前列腺癌患者的单野质子计划评估了字典法。使用开源治疗计划系统进行剂量计算,同时使用[具体软件名称未给出]进行声波传播计算。我们研究了所提出的字典法检测由组织密度的解剖学变化以及横向和纵向束位置改变引起的射程变化的能力。我们的结果表明,基于字典的原声波方法能够识别由所有引入的改变所引起的射程变化,平均精度为1.4毫米。该程序可用于验证,将测量信号与预先计算的字典进行比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/21a7e10696b2/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/1bcbb114cd46/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/7812bb4fadb6/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/5239784e501b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/88bc60a79a0e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/3c5d45ce251b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/21a7e10696b2/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/1bcbb114cd46/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/7812bb4fadb6/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/5239784e501b/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/88bc60a79a0e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/3c5d45ce251b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ac7/7820918/21a7e10696b2/gr6.jpg

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本文引用的文献

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2
New frontiers in proton therapy: applications in cancers.质子治疗的新前沿:癌症应用。
Cancer Commun (Lond). 2019 Oct 22;39(1):61. doi: 10.1186/s40880-019-0407-3.
3
Reconstructing 3D proton dose distribution using ionoacoustics.利用离子声重建 3D 质子剂量分布。
基于混合监督的深度迁移学习的三维原始声图像重建。
Phys Med Biol. 2024 Apr 3;69(8). doi: 10.1088/1361-6560/ad3327.
4
Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS.使用堆叠自编码器对磁共振波谱(MRS)数据进行去噪,以提高 MRS 的信噪比和速度。
Med Phys. 2023 Dec;50(12):7955-7966. doi: 10.1002/mp.16831. Epub 2023 Nov 10.
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Deep learning-based protoacoustic signal denoising for proton range verification.基于深度学习的质子射程验证原始声信号去噪。
Biomed Phys Eng Express. 2023 May 12;9(4). doi: 10.1088/2057-1976/acd257.
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Reconstruction of thermoacoustic emission sources induced by proton irradiation using numerical time reversal.基于数值时反法的质子辐照诱发热声发射源重建
Phys Med Biol. 2023 Jan 5;68(2). doi: 10.1088/1361-6560/acabfc.
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Front Oncol. 2022 Nov 3;12:925542. doi: 10.3389/fonc.2022.925542. eCollection 2022.
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3Ddose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.基于深度学习的质子声成像技术在前列腺质子治疗中的 3D 剂量验证。
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