Technical Note: A direct ray‐tracing method to compute integral depth dose in pencil beam proton radiography with a multilayer ionization chamber

Farace, Paolo; Righetto, Roberto; Deffet, Sylvain; Meijers, Artürs; Stappen, F. Vander

doi:10.1118/1.4966703

Cited by 26 publications

(51 citation statements)

References 14 publications

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“…In proton radiography, two‐dimensional range error computation is very sensitive to spatial misalignments between CT simulation and proton radiography acquisitions . Previously, Mumot et al in a study using range probe for one‐dimensional range verification, anticipated that range errors are sensitive to misalignment.…”

Section: Discussionmentioning

confidence: 99%

“…Conventional positioning accuracy for patient treatment might not be sufficient in order to allow in vivo range verification afterwards, and spatial alignment procedures need to be developed. The potential of registration with proton radiography and its improving effect on range error mapping have been recently demonstrated by a simplified algorithm considering only two‐dimensional translations . In the present study, a novel six‐degree of freedom registration method was developed and the effects of confounding factors such as measurement noise, CT calibration errors, and of spot‐spacing were investigated.…”

Section: Discussionmentioning

confidence: 99%

“…A fast and accurate analytical method for computing the simulated IDD, named

I D D_{s i m u}

, consists of a convolution of a Gaussian kernel G having a standard deviation equal to the spot size with a reference IDD, called

I D D_{r e f}

, which corresponds to the one that would be measured without the phantom through the beam path:

\begin{matrix} I D D_{s i m u} false(x_{i}, y_{i}, z false) = & \sum_{j \in S_{i}} G false(x_{i} - x_{j}, y_{i} - y_{j} false) \\ I D D_{r e f} false(z + W E T (x_{i}, y_{i}) false) \end{matrix}

where the coordinates

(x_{i}, y_{i})

of each pencil beam are expressed in the gantry coordinate system, z refers to the depth axis of the IDD, WET refers to the water‐equivalent thickness of the phantom along the path of the beam and

S_{i}

denotes the set of the indices of the pixels lying in the cross‐section of the beamlet. In the case of parallel beamlets, WET can be computed by

W E T false(x, y false) = \sum_{z} S P R false(x, y, z false) s_{z}

where SPR is the stopping power relative to water, and

s_{z}

is the size of a voxel along the z‐direction.…”

Section: Methodsmentioning

confidence: 99%

“…A fast and accurate analytical method 11 for computing the simulated IDD, named IDD simu , consists of a convolution of a Gaussian kernel G having a standard deviation equal to the spot size with a reference IDD, called IDD ref , which corresponds to the one that would be measured without the phantom through the beam path:…”

Section: A2 Idd Simulationmentioning

confidence: 99%

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Registration of pencil beam proton radiography data with X‐ray CT

Deffet

Macq

Righetto³

et al. 2017

Medical Physics

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For registration of proton radiography data with X-ray CT, the use of a direct ray-tracing algorithm to compute sums of squared differences and corrections of range errors showed very good accuracy and robustness with respect to three confounding factors: measurement noise, calibration error, and spot spacing. It is therefore a suitable algorithm to use in the in vivo range verification framework, allowing to separate in postprocessing the proton range uncertainty due to setup errors from the other sources of uncertainty.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…A fast and accurate analytical method for computing the simulated IDD, named

I D D_{s i m u}

, consists of a convolution of a Gaussian kernel G having a standard deviation equal to the spot size with a reference IDD, called

I D D_{r e f}

, which corresponds to the one that would be measured without the phantom through the beam path:

\begin{matrix} I D D_{s i m u} false(x_{i}, y_{i}, z false) = & \sum_{j \in S_{i}} G false(x_{i} - x_{j}, y_{i} - y_{j} false) \\ I D D_{r e f} false(z + W E T (x_{i}, y_{i}) false) \end{matrix}

where the coordinates

(x_{i}, y_{i})

of each pencil beam are expressed in the gantry coordinate system, z refers to the depth axis of the IDD, WET refers to the water‐equivalent thickness of the phantom along the path of the beam and

S_{i}

denotes the set of the indices of the pixels lying in the cross‐section of the beamlet. In the case of parallel beamlets, WET can be computed by

W E T false(x, y false) = \sum_{z} S P R false(x, y, z false) s_{z}

where SPR is the stopping power relative to water, and

s_{z}

is the size of a voxel along the z‐direction.…”

Section: Methodsmentioning

confidence: 99%

Section: A2 Idd Simulationmentioning

confidence: 99%

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Registration of pencil beam proton radiography data with X‐ray CT

Deffet

Macq

Righetto³

et al. 2017

Medical Physics

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“…The dose maps were exported separately for each beamlet so that the IDDs could later be reconstructed. We used a discretization (a uniform grid of 1 mm) identical to that used by Farace et al to experimentally demonstrate the validity of such an approach to model MLIC acquisitions . The IDDs obtained from the simulations were then downsampled to correspond to the depth resolution of the Giraffe.…”

Section: Methodsmentioning

confidence: 99%

Sparse deconvolution of proton radiography data to estimate water equivalent thickness maps

2019

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Purpose In proton therapy, the conversion of the planning computed tomography (CT) into proton stopping powers is tainted by uncertainties which may jeopardize dose conformity. Proton radiography provides a direct information on the energy reduction of protons in the patient. However, it is currently limited by the degradation (“blurring”) of the one‐dimensional depth‐dose deposition profiles which constitute the pixels. Methods An iterative algorithm is implemented to extract high‐resolution water equivalent thickness (WET) maps from the measurements of depth‐dose profiles acquired with a multilayer ionization chamber. The method relies on the assumption that those curves are a function of the WET, which can benefit from a sparse representation. Results When used without relying on any prior knowledge derived from the planning CT, the method already outperforms the published one in terms of accuracy. We also propose a variant which integrates the planning CT in a robust fashion to further improve the deconvolution result and reach an accuracy of 1.5 mm on the estimated WET. The methods are applied to both synthetic data and actual proton radiography acquisitions on phantoms. Conclusions Besides the increase in accuracy achieved in the estimation of WET maps from proton radiography data, we demonstrate that the proposed deconvolution algorithm is also more robust with respect to confounding factors such as residual setup errors or changes in the anatomy. Therefore, proton radiography using a range probe provides both the required accuracy to assess and reduce range uncertainty in proton therapy and the simplicity of integrated‐mode proton radiography.

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Refinement of the Hounsfield look‐up table by retrospective application of patient‐specific direct proton stopping‐power prediction from dual‐energy CT

et al. 2020

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Background and Purpose: Proton treatment planning relies on an accurate determination of stopping-power ratio (SPR) from x-ray computed tomography (CT). A refinement of the heuristic CTbased SPR prediction using a state-of-the-art Hounsfield look-up table (HLUT) is proposed, which incorporates patient SPR information obtained from dual-energy CT (DECT) in a retrospective patient-cohort analysis. Material and Methods: SPR datasets of 25 brain-tumor patients, 25 prostate-cancer patients, and three nonsmall cell lung-cancer (NSCLC) patients were calculated from clinical DECT scans with the comprehensively validated DirectSPR approach. Based on the median frequency distribution of voxelwise correlations between CT number and SPR within the irradiated volume, a piecewise linear function was specified (DirectSPR-based adapted HLUT). Differences in dose distribution and proton range were assessed for the nonadapted and adapted HLUT in comparison to the DirectSPR method, which has been shown to be an accurate and reliable SPR estimation method. Results: The application of the DirectSPR-based adapted HLUT instead of the nonadapted HLUT reduced the systematic proton range differences from 1.2% (1.1 mm) to À0.1% (0.0 mm) for braintumor patients, 1.7% (4.1 mm) to 0.2% (0.5 mm) for prostate-cancer patients, and 2.0% (2.9 mm) to À0.1% (0.0 mm) for NSCLC patients. Due to the large intra-and inter-patient tissue variability, range differences to DirectSPR larger than 1% remained for the adapted HLUT. Conclusions: The incorporation of patient-specific correlations between CT number and SPR, derived from a retrospective application of DirectSPR to a broad patient cohort, improves the SPR accuracy of the current state-of-the-art HLUT approach. The DirectSPR-based adapted HLUT has been clinically implemented at the University Proton Therapy Dresden (Dresden, Germany) in 2017. This already facilitates the benefits of an improved DECT-based tissue differentiation within clinical routine without changing the general approach for range prediction (HLUT), and represents a further step toward full integration of the DECT-based DirectSPR method for treatment planning in proton therapy.

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Technical Note: A direct ray‐tracing method to compute integral depth dose in pencil beam proton radiography with a multilayer ionization chamber

Abstract: The ray-tracing algorithm can reliably replace the TPS method in MLIC PR for in vivo range verification and it can be a key component to develop software tools for spatial alignment and correction of CT calibration.

Cited by 26 publications

References 14 publications

Registration of pencil beam proton radiography data with X‐ray CT

Registration of pencil beam proton radiography data with X‐ray CT

Sparse deconvolution of proton radiography data to estimate water equivalent thickness maps

Refinement of the Hounsfield look‐up table by retrospective application of patient‐specific direct proton stopping‐power prediction from dual‐energy CT

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