Prediction of image noise contributions in proton computed tomography and comparison to measurements

J Dickmann,V Bashkirov,K Parodi,G Dedes,S Rit,R P Johnson,R W Schulte,P Wesp,G Landry,M Rädler,M Pankuch

doi:10.1088/1361-6560/ab2474

Abstract

We present a method to accurately predict image noise in proton computed tomography (pCT) using data generated from a Monte Carlo simulation and a patient or object model that may be generated from a prior x-ray CT image. This enables noise prediction for arbitrary beam fluence settings and, therefore, the application of fluence-modulated pCT (FMpCT), which can achieve prescribed noise targets and may significantly reduce the integral patient dose.We extended an existing Monte Carlo simulation of a prototype pCT scanner to include effects of quenching in the energy detector scintillators and constructed a beam model from experimental tracking data. Simulated noise predictions were compared to experimental data both in the projection domain and in the reconstructed image. Noise prediction agreement between simulated and experimental data in terms of the root-mean-square (RMS) error was better than 7% for a homogeneous water phantom and a sensitometry phantom with tubular inserts. For an anthropomorphic head phantom, modeling the anatomy of a five-year-old child, the RMS error was better than 9% in three evaluated slices. We were able to reproduce subtle noise features near heterogeneities. To demonstrate the feasibility of Monte Carlo simulated noise maps for fluence modulation, we calculated a fluence profile that yields a homogeneous noise level in the image. Unlike for bow-tie filters in x-ray CT this does not require constant fluence at the detector and the shape of the fluence profile is fundamentally different.Using an improved Monte Carlo simulation, we demonstrated the feasibility of using simulated data for accurate image noise prediction for pCT. We believe that the agreement with experimental data is sufficient to enable the future optimization of FMpCT fluence plans to achieve prescribed noise targets in a fluence-modulated acquisition.

Highlights

The use of proton computed tomography, initially proposed by Cormack (1963), promises superior accuracy of relative stopping power (RSP) images required for proton therapy dose calculations employed in treatment planning or for various adaptive proton therapy strategies
We present a method to accurately predict image noise in proton computed tomography using data generated from a Monte Carlo simulation and a patient or object model that may be generated from a prior x-ray CT image
Using an improved Monte Carlo simulation, we demonstrated the feasibility of using simulated data for accurate image noise prediction for proton computed tomography (pCT)

Summary

Introduction

The use of proton computed tomography (pCT), initially proposed by Cormack (1963), promises superior accuracy of relative stopping power (RSP) images required for proton therapy dose calculations employed in treatment planning or for various adaptive proton therapy strategies. Reported doses for exper imental operation of certain designs and for central pixel noise levels comparable to those of x-ray CT are at only 1 mGy (Sadrozinski et al 2016). This is comparable or lower to an in-room cone beam CT (Alaei and Spezi 2015) and can pave the way for daily in-room imaging prior to each treatment session to prevent inaccurate dose delivery during treatment due to positioning errors or anatomical changes. Fluence modulation has recently gained a strong research interest, due to technical improvements allowing implementation of such systems in x-ray CT (Bartolac and Jaffray 2013, Szczykutowicz and Mistretta 2013a, 2013b, Szczykutowicz et al 2015, Stayman et al 2016, Gang et al 2017, Mao et al 2018, Huck et al 2019, Shunhavanich et al 2019)

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