An optimization algorithm for dose reduction with fluence‐modulated proton CT

Abstract

Purpose: Fluence-modulated proton computed tomography (FMpCT) using pencil beam scanning aims at achieving task-specific image noise distributions by modulating the imaging proton fluence spot-by-spot based on an object-specific noise model. In this work, we present a method for fluence field optimization and investigate its performance in dose reduction for various phantoms and image variance targets. Methods: The proposed method uses Monte Carlo simulations of a proton CT (pCT) prototype scanner to estimate expected variance levels at uniform fluence. Using an iterative approach, we calculate a stack of target variance projections that are required to achieve the prescribed image variance, assuming a reconstruction using filtered backprojection. By fitting a pencil beam model to the ratio of uniform fluence variance and target variance, relative weights for each pencil beam can be calculated. The quality of the resulting fluence modulations is evaluated by scoring imaging doses and comparing them to those at uniform fluence, as well as evaluating conformity of the achieved variance with the prescription. For three different phantoms, we prescribed constant image variance as well as two regions-of-interest (ROI) imaging tasks with inhomogeneous image variance. The shape of the ROIs followed typical beam profiles for proton therapy. Results: Prescription of constant image variance resulted in a dose reduction of 8.9% for a homogeneous water phantom compared to a uniform fluence scan at equal peak variance level. For a more heterogeneous head phantom, dose reduction increased to 16.0% for the same task. Prescribing two different ROIs resulted in dose reductions between 25.7% and 40.5% outside of the ROI at equal peak variance levels inside the ROI. Imaging doses inside the ROI were increased by 9.2% to 19.2% compared to the uniform fluence scan, but can be neglected assuming that the ROI agrees with the therapeutic dose region. Agreement of resulting variance maps with the prescriptions was satisfactory. Conclusions: We developed a method for fluence field optimization based on a noise model for a real scanner used in pCT. We demonstrated that it can achieve prescribed image variance targets. A uniform fluence field was shown not to be dose optimal and dose reductions achievable with the proposed method for FMpCT were considerable, opening an interesting perspective for image guidance and adaptive therapy.

The role of Monte Carlo simulation in understanding the performance of proton computed tomography

“…Using a MC simulation with good agreement to measurements allowed to disentangle noise contributions and demonstrate that the heterogeneity of the object is important. In a subsequent study, Dickmann et al [114] used such simulation in a fluence modulated proton CT optimization strategy.…”

Fluence-modulated proton CT optimized with patient-specific dose and variance objectives for proton dose calculation

“…The beam shape was elliptical because standard deviations were different for the horizontal coordinate u and the vertical coordinate v due to operation in research mode. In accordance with recent experiments (Dickmann et al 2020a) at the Northwestern Medicine Chicago proton center using the phase-II pCT scanner the standard deviations were chosen as σ u = 3.7 mm and σ v = 2.9 mm and in agreement with Dickmann et al (2020) the divergence was δ u = 5.2 × 10 −4 mm −1 and δ v = 5.8 × 10 −4 mm −1 . The smaller spot size compared to Dickmann et al (2020) was necessary due to an upgrade of the treatment facility.…”

“…In accordance with recent experiments (Dickmann et al 2020a) at the Northwestern Medicine Chicago proton center using the phase-II pCT scanner the standard deviations were chosen as σ u = 3.7 mm and σ v = 2.9 mm and in agreement with Dickmann et al (2020) the divergence was δ u = 5.2 × 10 −4 mm −1 and δ v = 5.8 × 10 −4 mm −1 . The smaller spot size compared to Dickmann et al (2020) was necessary due to an upgrade of the treatment facility. Horizontally, N PB,u = 51 columns of pencil beams were interspaced by 5.6 mm and vertically N PB,v = 21 rows were interspaced by 4.5 mm resulting in a total of 1071 pencil beams covering 285.6 mm × 94.5 mm.…”

“…the vicinity of the treatment beam path, but reducing imaging fluence elsewhere. Dickmann et al (2020) proposed a three-step optimization algorithm for FMpCT which calculates imaging fluences achieving a target image variance map. It used an iterative variance forward-projection approach allowing to solve for the fluence modulation for each projection independently.…”

“…Optimized fluence modulations were employed experimentally by modulating pencil beams and using a prototype pCT scanner resulting in good agreements between simulated and experimental scans in terms of image variance and RSP accuracy (Dickmann et al 2020a). The studies of Dickmann et al (2020Dickmann et al ( , 2020a were limited to phantoms with tissue-equivalent materials and the optimization algorithm only took into account image variance and not imaging dose. Dose savings were achieved implicitly by prescribing higher image variance outside of the ROI, and it remained unclear if the chosen variance level achieved the optimal dose saving, and whether this level was the lowest achievable.…”