In-beam positron emission tomography (PET) monitoring can provide early treatment assessment in proton therapy. However, open ring configurations and low positron emitter production yields degrade image quality, hampering the assessment accuracy. To achieve the highest precision for range monitoring, it is compulsory to mitigate image noise and compensate for data truncation. The goal of this article is to study the performance of state-of-the-art algorithms for in-beam PET image reconstruction, by evaluating the impact of the system response model and assessing the accuracy of range measurements. The approaches investigated here were maximum-a-posteriori algorithms combined with total-variation and medianroot priors. Maximum-likelihood-expectation-maximization was used as reference. To compute the system matrix, different models were compared: a precise Monte Carlo model, and single-ray tracing with and without Gaussian blurring in image space. The proposed methods were tested on simulations of spread-out-bragg-peaks delivered on phantoms. The in-beam PET innovative imaging scanner geometry was used as a case study. After image post-processing, the explored methods delivered similar results. This article demonstrates the feasibility and reliability of using a simple and fast reconstruction method to perform range evaluations, given the correct image post-processing.

Evaluation of In-Beam PET Treatment Verification in Proton Therapy With Different Reconstruction Methods

Veronica Ferrero
Co-first
;
Francesco Pennazio
Co-first
;
Piergiorgio Cerello;Elisa Fiorina;Sara Garbolino;Vincenzo Monaco;Magdalena Rafecas
2020-01-01

Abstract

In-beam positron emission tomography (PET) monitoring can provide early treatment assessment in proton therapy. However, open ring configurations and low positron emitter production yields degrade image quality, hampering the assessment accuracy. To achieve the highest precision for range monitoring, it is compulsory to mitigate image noise and compensate for data truncation. The goal of this article is to study the performance of state-of-the-art algorithms for in-beam PET image reconstruction, by evaluating the impact of the system response model and assessing the accuracy of range measurements. The approaches investigated here were maximum-a-posteriori algorithms combined with total-variation and medianroot priors. Maximum-likelihood-expectation-maximization was used as reference. To compute the system matrix, different models were compared: a precise Monte Carlo model, and single-ray tracing with and without Gaussian blurring in image space. The proposed methods were tested on simulations of spread-out-bragg-peaks delivered on phantoms. The in-beam PET innovative imaging scanner geometry was used as a case study. After image post-processing, the explored methods delivered similar results. This article demonstrates the feasibility and reliability of using a simple and fast reconstruction method to perform range evaluations, given the correct image post-processing.
2020
4
2
202
211
https://ieeexplore.ieee.org/document/8845670
Clinical imaging; image reconstruction; iterative algorithms; positron emission tomography (PET); quantitative imaging techniques; radiation therapy; range monitoring
Veronica Ferrero; Francesco Pennazio; Piergiorgio Cerello; Elisa Fiorina; Sara Garbolino; Vincenzo Monaco; Richard Wheadon; Magdalena Rafecas
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1892277
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