Experimental fluence‐modulated proton computed tomography by pencil beam scanning

Dedes, G.; Johnson, R. P.; Pankuch, Mark; Detrich, Nick; Pols, Willemijn M. A.; Rit, Simon; Schulte, R.; Parodi, Katia; Landry, Guillaume

doi:10.1002/mp.12989

Cited by 18 publications

(22 citation statements)

References 33 publications

(80 reference statements)

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“…In a simulation study, we estimated dose savings for FMpCT using three different phantoms, and compared our proposed solution with a straightforward intersection-based fluence modulation. 31 We also verified that the resulting variance map approaches the target variance. Both a constant variance target as well as two ROIs following typical treatment beam paths were investigated.…”

Section: Introductionmentioning

confidence: 58%

“…Variance targets are used in step b of the proposed algorithm, and therefore independent of the imaged object. In agreement to previous investigations 31 , the prescription contrast of 4 was chosen such that it is higher than the variance dynamic range of a unit fluence scan, 34 reasonably achievable without expecting regions with vanishing counts or distortions of RSP accuracy.…”

Section: F Simulation Studymentioning

confidence: 81%

“…Recently, fluence-modulated proton computed tomography (FMpCT) has also been proposed 30 and its initial experimental feasibility using pencil beam scanning was investigated. 31 The best achievable dose efficiency through fluence modulation or other techniques is a key requirement for x-ray CT 32 and most likely will be for pCT as it moves closer to the clinics. Moreover, ROI imaging is of high interest for particle therapy treatment planning and dose verification, where only a fraction of the image volume (the treatment beam path) is of relevance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 58%

Section: F Simulation Studymentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Finally, other detector limitations such as pile-up and non-uniform detector performance could alter the noise image with respect to what is reconstructed assuming that every proton that exits the object can be detected with the same accuracy. Further work will aim at carefully investigating the impact of detector uncertainties by making use of the validated simulation platform of Giacometti et al (2017) as well as experimentally acquired pencil beam scanning data from Dedes et al (2018).…”

Section: Discussionmentioning

confidence: 99%

“…While they could show the feasibility of fluence modulated pCT (FMpCT) in a simulation study, they relied on a 'forward planning' approach where simple geometric considerations guided a binary fluence modulation on a pencil beam by pencil beam basis. The same approach was employed for the recent experimental realization of FMpCT using the proton tracking phase II pCT prototype of the Loma Linda University and University of California Santa Cruz (Dedes et al 2018). Further developments in FMpCT thus require the modeling of the relation between proton fluence and pCT image noise to allow using an optimization strategy where pencil beam fluence could be continuously adjusted to achieve image noise prescriptions.…”

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confidence: 99%

Two-dimensional noise reconstruction in proton computed tomography using distance-driven filtered back-projection of simulated projections

et al. 2018

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Relative proton stopping power (RSP), the ratio of the proton stopping power of a given material to that of water at a given energy, is necessary for most clinical dose calculation methods used in proton therapy. RSP is currently estimated by x-ray computed tomography (CT) scans in clinical practice (Taasti et al 2018). The conversion from photon attenuation coefficients to RSP contributes considerably to range uncertainties (Paganetti 2012, Yang et al 2012). The potential of reducing these uncertainties by direct RSP measurements at the treatment position has motivated the recent revival of proton computed tomography (pCT), which was first proposed by Cormack in the early 1960s (Cormack 1963). Modern pre-clinical pCT scanners measure the positions and residual energies of the protons behind (and in some designs also in front of) the patient in a series of projections, from which an RSP image can be reconstructed (

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Comparative accuracy and resolution assessment of two prototype proton computed tomography scanners

et al. 2022

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BackgroundImproving the accuracy of relative stopping power (RSP) in proton therapy may allow reducing range margins. Proton computed tomography (pCT) has been shown to provide state‐of‐the‐art RSP accuracy estimation, and various scanner prototypes have recently been built. The different approaches used in scanner design are expected to impact spatial resolution and RSP accuracy. PurposeThe goal of this study was to perform the first direct comparison, in terms of spatial resolution and RSP accuracy, of two pCT prototype scanners installed at the same facility and by using the same image reconstruction algorithm. MethodsA phantom containing cylindrical inserts of known RSP was scanned at the phase‐II pCT prototype of the U.S. pCT collaboration and at the commercially oriented ProtonVDA scanner. Following distance‐driven binning filtered backprojection reconstruction, the radial edge spread function of high‐density inserts was used to estimate the spatial resolution. RSP accuracy was evaluated by the mean absolute percent error (MAPE) over the inserts. No direct imaging dose estimation was possible, which prevented a comparison of the two scanners in terms of RSP noise. ResultsIn terms of RSP accuracy, both scanners achieved the same MAPE of 0.72% when excluding the porous sinus insert from the evaluation. The ProtonVDA scanner reached a better overall MAPE when all inserts and the body of the phantom were accounted for (0.81%), compared to the phase‐II scanner (1.14%). The spatial resolution with the phase‐II scanner was found to be 0.61 lp/mm, while for the ProtonVDA scanner somewhat lower at 0.46 lp/mm. ConclusionsThe comparison between two prototype pCT scanners operated in the same clinical facility showed that they both fulfill the requirement of an RSP accuracy of about 1%. Their spatial resolution performance reflects the different design choices of either a scanner with full tracking capabilities (phase‐II) or of a more compact tracker system, which only provides the positions of protons but not their directions (ProtonVDA).

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Experimental fluence‐modulated proton computed tomography by pencil beam scanning

Cited by 18 publications

References 33 publications

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

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

Two-dimensional noise reconstruction in proton computed tomography using distance-driven filtered back-projection of simulated projections

Comparative accuracy and resolution assessment of two prototype proton computed tomography scanners

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