Krah Nils, Dauvergne Denis, Létang Jean Michel, Rit Simon, Testa Étienne
Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, LYON, France.
University of Lyon, Université Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne, France.
Phys Med Biol. 2021 Oct 12;66(20). doi: 10.1088/1361-6560/ac2999.
This note addresses an issue faced by every proton computed tomography (CT) reconstruction software: the modelling and the parametrisation of the multiple Coulomb scattering power for the estimation of the most likely path (MLP) of each proton. The conventional approach uses a polynomial model parameterised as a function of depth for a given initial beam energy. This makes it cumbersome to implement a software that works for proton CT data acquired with an arbitrary beam energy or with energy modulation during acquisition. We propose a simple way to parametrise the scattering power based on the measured proton CT list-mode data only and derive a compact expression for the MLP based on a conventional MLP model. Our MLP does not require any parameter. The method assumes the imaged object to be homogeneous, as most conventional MLPs, but requires no information about the material as opposed to most conventional MLP expressions which often assume water to infer energy loss. Instead, our MLP automatically adapts itself to the energy-loss which actually occurred in the object and which is one of the measurements required for proton CT reconstruction. We validate our MLP method numerically and find excellent agreement with conventional MLP methods.
本笔记讨论了每个质子计算机断层扫描(CT)重建软件所面临的一个问题:用于估计每个质子最可能路径(MLP)的多次库仑散射功率的建模和参数化。传统方法使用一个多项式模型,该模型根据给定的初始束流能量作为深度的函数进行参数化。这使得实现一个适用于以任意束流能量采集的质子CT数据或在采集过程中进行能量调制的质子CT数据的软件变得繁琐。我们提出了一种仅基于测量的质子CT列表模式数据对散射功率进行参数化的简单方法,并基于传统的MLP模型推导出了MLP的紧凑表达式。我们的MLP不需要任何参数。该方法与大多数传统的MLP一样,假设成像对象是均匀的,但与大多数传统的MLP表达式不同,后者通常假设为水来推断能量损失,而我们的方法不需要关于材料的任何信息。相反,我们的MLP会自动适应在对象中实际发生的能量损失,而这是质子CT重建所需的测量之一。我们对我们的MLP方法进行了数值验证,发现与传统的MLP方法有很好的一致性。