Journal Article

DKFZ-2023-01777

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter.

Schilling, A. ; Aehle, M. ; Alme, J. ; Barnaföldi, G. G. ; Bodova, T. ; Borshchov, V. ; van den Brink, A. ; Eikeland, V. N. ; Feofilov, G. ; Garth, C. ; Gauger, N. R. ; Grøttvik, O. S. ; Helstrup, H. ; Igolkin, S. ; Keidel, R. ; Kobdaj, C. ; Kortus, T. ; Leonhardt, V. ; Mehendale, S. ; Mulawade, R. N. ; Odland, O. H. ; O'Neill, G. ; Papp, G. ; Peitzmann, T. ; Pettersen, H. E. S. ; Piersimoni, P. ; Protsenko, M. ; Rauch, M. ; Rehman, A. U. ; Richter, M. ; Roehrich, D. ; Santana, J. ; Seco, J.DKFZ* ; Songmoolnak, A. ; Sudár, Á. ; Tambave, G. ; Tymchuk, I. ; Ullaland, K. ; Varga-Kőfaragó, M. ; Volz, L. ; Wagner, B. ; Wendzel, S. ; Wiebel, A. ; Xiao, R. ; Yang, S. ; Zillien, S.

2023
IOP Publ. Bristol

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Please use a persistent id in citations: doi:10.1088/1361-6560/acf5c2

Abstract: Objective.Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach.We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results.The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1·107protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance.Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.

Keyword(s): machine learning ; particle therapy ; range verification ; uncertainty

Note: Phys. Med. Biol. 68 (2023) 194001

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Contributing Institute(s):

1. E041 Med. Physik in der Radioonkologie (E041)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2023

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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