A theoretical framework to predict the most likely ion path in particle imaging

Integrated-mode proton radiography with 2D lateral projections

Integrated-mode proton radiography leading to water equivalent thickness (WET) maps is an avenue of interest for motion management, patient positioning, andin vivorange verification. Radiographs can be obtained using a pencil beam scanning setup with a large 3D monolithic scintillator coupled with optical cameras. Established reconstruction methods either (1) involve a camera at the distal end of the scintillator, or (2) use a lateral view camera as a range telescope. Both approaches lead to limited image quality. The purpose of this work is to propose a third, novel reconstruction framework that exploits the 2D information provided by two lateral view cameras, to improve image quality achievable using lateral views. The three methods are first compared in a simulated Geant4 Monte Carlo framework using an extended cardiac torso (XCAT) phantom and a slanted edge. The proposed method with 2D lateral views is also compared with the range telescope approach using experimental data acquired with a plastic volumetric scintillator. Scanned phantoms include a Las Vegas (contrast), 9 tissue-substitute inserts (WET accuracy), and a paediatric head phantom. Resolution increases from 0.24 (distal) to 0.33 lp mm-1(proposed method) on the simulated slanted edge phantom, and the mean absolute error on WET maps of the XCAT phantom is reduced from 3.4 to 2.7 mm with the same methods. Experimental data from the proposed 2D lateral views indicate a 36% increase in contrast relative to the range telescope method. High WET accuracy is obtained, with a mean absolute error of 0.4 mm over 9 inserts. Results are presented for various pencil beam spacing ranging from 2 to 6 mm. This work illustrates that high quality proton radiographs can be obtained with clinical beam settings and the proposed reconstruction framework with 2D lateral views, with potential applications in adaptive proton therapy.

Energy painting: helium-beam radiography with thin detectors and multiple beam energies

Objective.Compact ion imaging systems based on thin detectors are a promising prospect for the clinical environment since they are easily integrated into the clinical workflow. Their measurement principle is based on energy deposition instead of the conventionally measured residual energy or range. Therefore, thin detectors are limited in the water-equivalent thickness range they can image with high precision. This article presents ourenergy paintingmethod, which has been developed to render high precision imaging with thin detectors feasible even for objects with larger, clinically relevant water-equivalent thickness (WET) ranges.Approach.A detection system exclusively based on pixelated silicon Timepix detectors was used at the Heidelberg ion-beam therapy center to track single helium ions and measure their energy deposition behind the imaged object. Calibration curves were established for five initial beam energies to relate the measured energy deposition to WET. They were evaluated regarding their accuracy, precision and temporal stability. Furthermore, a 60 mm × 12 mm region of a wedge phantom was imaged quantitatively exploiting the calibrated energies and five different mono-energetic images. These mono-energetic images were combined in a pixel-by-pixel manner by averaging the WET-data weighted according to their single-ion WET precision (SIWP) and the number of contributing ions.Main result.A quantitative helium-beam radiograph of the wedge phantom with an average SIWP of 1.82(5) % over the entire WET interval from 150 mm to 220 mm was obtained. Compared to the previously used methodology, the SIWP improved by a factor of 2.49 ± 0.16. The relative stopping power value of the wedge derived from the energy-painted image matches the result from range pullback measurements with a relative deviation of only 0.4 %.Significance.The proposed method overcomes the insufficient precision for wide WET ranges when employing detection systems with thin detectors. Applying this method is an important prerequisite for imaging of patients. Hence, it advances detection systems based on energy deposition measurements towards clinical implementation.

Exploration of differentiability in a proton computed tomography simulation framework

Objective.Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications.Approach.This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques.Main results.The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a 'fuzzy voxels' approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem.Significance.Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary.

A likelihood-based particle imaging filter using prior information

BACKGROUND

Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium.

PURPOSE

This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise.

METHODS

The Prior Filter uses Fermi-Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied.

RESULTS

The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method.

CONCLUSIONS

The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.

Relative stopping power resolution in time-of-flight proton CT

Objective. Proton computed tomography (CT) is similar to x-ray CT but relies on protons rather than photons to form an image. In its most common operation mode, the measured quantity is the amount of energy that a proton has lost while traversing the imaged object from which a relative stopping power map can be obtained via tomographic reconstruction. To this end, a calorimeter which measures the energy deposited by protons downstream of the scanned object has been studied or implemented as energy detector in several proton CT prototypes. An alternative method is to measure the proton’s residual velocity and thus its kinetic energy via the time of flight (TOF) between at least two sensor planes. In this work, we study the RSP resolution, seen as image noise, which can be expected from TOF proton CT systems.

Approach. We rely on physics models on the one hand and statistical models of the relevant uncertainties on the other to derive closed form expressions for the noise in projection images. The TOF measurement error scales with the distance between the TOF sensor planes and is reported as velocity error in ps/m. We use variance reconstruction to obtain noise maps of a water cylinder phantom given the scanner characteristics and additionally reconstruct noise maps for a calorimeter-based proton CT system as reference. We use Monte Carlo simulations to verify our model and to estimate the noise due to multiple Coulomb scattering inside the object. We also provide a comparison of TOF helium and proton CT.

Main results. We find that TOF proton CT with 30 ps m−1 velocity error reaches similar image noise as a calorimeter-based proton CT system with 1% energy error (1 sigma error). A TOF proton CT system with a 50 ps m−1 velocity error produces slightly less noise than a 2% calorimeter system. Noise in a reconstructed TOF proton CT image is spatially inhomogeneous with a marked increase towards the object periphery. Our modelled noise was consistent with Monte Carlo simulated images. TOF helium CT offers lower RSP noise at equal fluence, but is less advantageous at equal imaging dose.

Significance. This systematic study of image noise in TOF proton CT can serve as a guide for future developments of this alternative solution for estimating the residual energy of protons and helium ions after the scanned object.

Experimental helium-beam radiography with a high-energy beam: Water-equivalent thickness calibration and first image-quality results

PURPOSE

A clinical implementation of ion-beam radiography (iRad) is envisaged to provide a method for on-couch verification of ion-beam treatment plans. The aim of this work is to introduce and evaluate a method for quantitative water-equivalent thickness (WET) measurements for a specific helium-ion imaging system for WETs that are relevant for imaging thicker body parts in the future.

METHODS

Helium-beam radiographs (αRads) are measured at the Heidelberg Ion-beam Therapy Center (HIT) with an initial beam energy of 239.5 MeV/ u. An imaging system based on three pairs of thin silicon pixel detectors is used for ion path reconstruction and measuring the energy deposition (dE) of each particle behind the object to be imaged. The dE behind homogeneous plastic blocks is related to their well-known WETs between 280.6mm and 312.6 mm with a calibration curve that is created by fitting the measured data points. The quality of the quantitative WET measurements is determined by the uncertainty of the measured WET of a single ion (single-ion WET precision) and the deviation of a measured WET value to the well-known WET (WET accuracy). Subsequently, the fitted calibration curve is applied to an energy deposition radiograph of a phantom with a complex geometry. The spatial resolution (modulation transfer function at 10% (MTF_10% )) and WET accuracy (mean absolute percentage difference (MAPD)) of the WET map, are determined.

RESULTS

In the optimal imaging WET-range from ∼  280 mm to 300 mm, the fitted calibration curve reached a mean single-ion WET precision of 1.55 ± 0.00%. Applying the calibration to an ion radiograph (iRad) of a more complex WET distribution, the spatial resolution was determined to be MTF_10% = 0.49 ± 0.03 lp/mm and the WET accuracy was assessed as MAPD to 0.21%.

CONCLUSIONS

Using a beam energy of 239.5MeV/ u and the proposed calibration procedure, quantitative αRads of WETs between ∼  280mm to 300 mm can be measured and show high potential for clinical use. The proposed approach with the resulting image qualities encourages further investigation towards the clinical application of helium-beam radiography. This article is protected by copyright. All rights reserved.

Experimental comparison of photon versus particle computed tomography to predict tissue relative stopping powers

PURPOSE

Measurements comparing relative stopping power (RSP) accuracy of state-of-the-art systems representing single-energy and dual-energy computed tomography (SECT/DECT) with proton CT (pCT) and helium CT (HeCT) in biological tissue samples.

METHODS

We used 16 porcine and bovine samples of various tissue types and water, covering an RSP range from 0.90 ± 0.06 to 1.78 ± 0.05. Samples were packed and sealed into 3D-printed cylinders ( d = 2  cm, h = 5  cm) and inserted into an in-house designed cylindrical polymethyl methacrylate (PMMA) phantom ( d = 10  cm, h = 10  cm). We scanned the phantom in a commercial SECT and DECT (120 kV; 100  and 140 kV/Sn (tin-filtered)); and acquired pCT and HeCT ( E ∼ 200  MeV/u, 2 ∘ steps, ∼ 6.2 × 10 6 (p)/ ∼ 2.3 × 10 6 (He) particles/projection) with a particle imaging prototype. RSP maps were calculated from SECT/DECT using stoichiometric methods and from pCT/HeCT using the DROP-TVS algorithm. We estimated the average RSP of each tissue per modality in cylindrical volumes of interest and compared it to ground truth RSP taken from peak-detection measurements.

RESULTS

Throughout all samples, we observe the following root-mean-squared RSP prediction errors ± combined uncertainty from reference measurement and imaging: SECT 3.10 ± 2.88%, DECT 0.75 ± 2.80%, pCT 1.19 ± 2.81%, and HeCT 0.78 ± 2.81%. The largest mean errors ± combined uncertainty per modality are SECT 8.22 ± 2.79% in cortical bone, DECT 1.74 ± 2.00% in back fat, pCT 1.80 ± 4.27% in bone marrow, and HeCT 1.37 ± 4.25% in bone marrow. Ring artifacts were observed in both pCT and HeCT reconstructions, imposing a systematic shift to predicted RSPs.

CONCLUSION

Comparing state-of-the-art SECT/DECT technology and a pCT/HeCT prototype, DECT provided the most accurate RSP prediction, closely followed by particle imaging. The novel modalities pCT and HeCT have the potential to further improve on RSP accuracies with work focusing on the origin and correction of ring artifacts. Future work will study accuracy of proton treatment plans using RSP maps from investigated imaging modalities.

The accuracy of helium ion CT based particle therapy range prediction: an experimental study comparing different particle and x-ray CT modalities

This work provides a quantitative assessment of helium ion CT (HeCT) for particle therapy treatment planning. For the first time, HeCT based range prediction accuracy in a heterogeneous tissue phantom is presented and compared to single-energy x-ray CT (SECT), dual-energy x-ray CT (DECT) and proton CT (pCT). HeCT and pCT scans were acquired using the US pCT collaboration prototype particle CT scanner at the Heidelberg Ion-Beam Therapy Center. SECT and DECT scans were done with a Siemens Somatom Definition Flash and converted to RSP. A Catphan CTP404 module was used to study the RSP accuracy of HeCT. A custom phantom of 20 cm diameter containing several tissue equivalent plastic cubes was used to assess the spatial resolution of HeCT and compare it to DECT. A clinically realistic heterogeneous tissue phantom was constructed using cranial slices from a pig head placed inside a cylindrical phantom (ø150 mm). A proton beam (84.67 mm range) depth-dose measurement was acquired using a stack of GafchromicTM EBT-XD films in a central dosimetry insert in the phantom. CT scans of the phantom were acquired with each modality, and proton depth-dose estimates were simulated based on the reconstructions. The RSP accuracy of HeCT for the plastic phantom was found to be 0.3 ± 0.1%. The spatial resolution for HeCT of the cube phantom was 5.9 ± 0.4 lp cm-1for central, and 7.6 ± 0.8 lp cm-1for peripheral cubes, comparable to DECT spatial resolution (7.7 ± 0.3 lp cm-1and 7.4 ± 0.2 lp cm-1, respectively). For the pig head, HeCT, SECT, DECT and pCT predicted range accuracy was 0.25%, -1.40%, -0.45% and 0.39%, respectively. In this study, HeCT acquired with a prototype system showed potential for particle therapy treatment planning, offering RSP accuracy, spatial resolution, and range prediction accuracy comparable to that achieved with a commercial DECT scanner. Still, technical improvements of HeCT are needed to enable clinical implementation.

Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks

BACKGROUND

Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality.

MATERIAL AND METHODS

The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods.

RESULTS

The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads.

CONCLUSION

The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality.

kV-kV and kV-MV DECT based estimation of proton stopping power ratio - a simulation study

PURPOSE

This study aims to estimate the proton stopping power ratio (SPR) by using 80-120 kV and 120 kV-6 MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations.

METHODS

Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, lnI_m. The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied.

RESULTS

With the beam hardening correction applied, the results of 120 kV-6 MV DECT are comparable to those of 80-120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV-6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80-120 kV DECT.

CONCLUSIONS

The 120 kV-6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV-6 MV DECT can predict the SPR more accurately than the 80-120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required.

Calculating 1/β2p2 for most likely path estimates for protons and helium ions using an analytical model

In ion computed tomography, limited spatial resolution can be related to the non-straight path of ions resulting from multiple Coulomb scattering in the object to be imaged. By including sophisticated path estimates such as most likely path (MLP) or optimized cubic spline into the image reconstruction algorithm, the achieved spatial resolution can be substantially improved compared to assuming a simple straight line path only. The typically used implementation of the MLP is a matrix-based approach employing Bayesian statistics and modelling multiple Coulomb scattering as Gaussian distribution. For the elements of the scattering matrices, the term 1/β(w)²p(w)², depending on the momentum and velocity of an ion within a phantom depth w, has to be known and integrated along the depth w. Usually, this term is extracted from a Monte Carlo simulation and approximated by a polynomial fit to solve the integral. In the present study, an existing analytical model for ion ranges and stopping powers was used to calculate 1/β(w)²p(w)² and the scattering matrices for the MLP and was tested for protons and helium ions. The model was investigated for 10 cm to 40 cm water targets and initial energies ranging from 150 MeV to 300 MeV for protons and 150 MeV/u to 300 MeV/u for helium ions. In all cases, the calculated value obtained for 1/β(w)²p(w)² was compared to a GATE simulation. The difference between root-mean-square errors of MLP estimates using calculated and simulated 1/β(w)²p(w)² values were found to be smaller than 3 μm for all investigated water targets and energies.

Statistical limitations in ion imaging

In this study, we investigated the capacity of various ion beams available for radiotherapy to produce high quality relative stopping power map acquired from energy-loss measurements. The image quality metrics chosen to compare the different ions were signal-to-noise ratio (SNR) as a function of dose and spatial resolution. Geant4 Monte Carlo simulations were performed for: hydrogen, helium, lithium, boron and carbon ion beams crossing a 20 cm diameter water phantom to determine SNR and spatial resolution. It has been found that protons possess a significantly larger SNR when compared with other ions at a fixed range (up to 36% higher than helium) due to the proton nuclear stability and low dose per primary. However, it also yields the lowest spatial resolution against all other ions, with a resolution lowered by a factor 4 compared to that of carbon imaging, for a beam with the same initial range. When comparing for a fixed spatial resolution of 10 lp cm⁻¹, carbon ions produce the highest image quality metrics with proton ions producing the lowest. In conclusion, it has been found that no ion can maximize all image quality metrics simultaneously and that a choice must be made between spatial resolution, SNR, and dose.

The impact of path estimates in iterative ion CT reconstructions for clinical-like cases

Ion computed tomography (CT) promises to mitigate range uncertainties inherent in the conversion of x-ray Hounsfield units into ion relative stopping power (RSP) for ion beam therapy treatment planning. To improve accuracy and spatial resolution of ion CT by accounting for statistical multiple Coulomb scattering deflection of the ion trajectories from a straight line path (SLP), the most likely path (MLP) and the cubic spline path (CSP) have been proposed. In this work, we use FLUKA Monte Carlo simulations to investigate the impact of these path estimates in iterative tomographic reconstruction algorithms for proton, helium and carbon ions. To this end the ordered subset simultaneous algebraic reconstruction technique was used and coupled with a total variation superiorization (TVS). We evaluate the image quality and dose calculation accuracy in proton therapy treatment planning of cranial patient anatomies. CSP and MLP generally yielded nearly equal image quality with an average RSP relative error improvement over the SLP of 0.6%, 0.3% and 0.3% for proton, helium and carbon ion CT, respectively. Bone and low density materials have been identified as regions of largest enhancement in RSP accuracy. Nevertheless, only minor differences in dose calculation results were observed between the different models and relative range errors of better than 0.5% were obtained in all cases. Largest improvements were found for proton CT in complex scenarios with strong heterogeneities along the beam path. The additional TVS provided substantially reduced image noise, resulting in improved image quality in particular for soft tissue regions. Employing the CSP and MLP for iterative ion CT reconstructions enabled improved image quality over the SLP even in realistic and heterogeneous patient anatomy. However, only limited benefit in dose calculation accuracy was obtained even though an ideal detector system was simulated.

Proton path reconstruction for pCT using neural networks

The Most Likely Path formalism (MLP) is widely established as the most statistically precise method for proton path reconstruction in proton computed tomography (pCT). However, while this method accounts for small-angle Multiple Coulomb Scattering (MCS) and energy loss, inelastic nuclear interactions play an influential role in a significant number of proton paths. By applying cuts based on energy and direction, tracks influenced by nuclear interactions are largely discarded from the MLP analysis. In this work we propose a new method to estimate the proton paths based on a Deep Neural Network (DNN). Through this approach, estimates of proton paths equivalent to MLP predictions have been achieved in the case where only MCS occurs, together with an increased accuracy when nuclear interactions are present. Moreover, our tests indicate that the DNN algorithm can be considerably faster than the MLP algorithm.

Helium radiography with a digital tracking calorimeter-a Monte Carlo study for secondary track rejection

Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient. Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks. In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions. By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent Path Length error envelope was 1.0 mm for helium and 1.1 mm for protons. This accuracy is expected to be sufficient for helium imaging for pre-treatment verification purposes.

Statistical limitations in proton imaging

Proton imaging is a promising technology for proton radiotherapy as it can be used for: (1) direct sampling of the tissue stopping power, (2) input information for multi-modality RSP reconstruction, (3) gold-standard calibration against concurrent techniques, (4) tracking motion and (5) pre-treatment positioning. However, no end-to-end characterization of the image quality (signal-to-noise ratio and spatial resolution, blurring uncertainty) against the dose has been done. This work aims to establish a model relating these characteristics and to describe their relationship with proton energy and object size. The imaging noise originates from two processes: the Coulomb scattering with the nucleus, producing a path deviation, and the energy loss straggling with electrons. The noise is found to increases with thickness crossed and, independently, decreases with decreasing energy. The scattering noise is dominant around high-gradient edge whereas the straggling noise is maximal in homogeneous regions. Image quality metrics are found to behave oppositely against energy: lower energy minimizes both the noise and the spatial resolution, with the optimal energy choice depending on the application and location in the imaged object. In conclusion, the model presented will help define an optimal usage of proton imaging to reach the promised application of this technology and establish a fair comparison with other imaging techniques.

Accuracy of low-dose proton CT image registration for pretreatment alignment verification in reference to planning proton CT

Purpose

Proton CT (pCT) has the ability to reduce inherent uncertainties in proton treatment by directly measuring the relative proton stopping power with respect to water, thereby avoiding the uncertain conversion of X‐ray CT Hounsfield unit to relative stopping power and the deleterious effect of X‐ ray CT artifacts. The purpose of this work was to further evaluate the potential of pCT for pretreatment positioning using experimental pCT data of a head phantom.

Methods

The performance of a 3D image registration algorithm was tested with pCT reconstructions of a pediatric head phantom. A planning pCT simulation scan of the phantom was obtained with 200 MeV protons and reconstructed with a 3D filtered back projection (FBP) algorithm followed by iterative reconstruction and a representative pretreatment pCT scan was reconstructed with FBP only to save reconstruction time. The pretreatment pCT scan was rigidly transformed by prescribing random errors with six degrees of freedom or deformed by the deformation field derived from a head and neck cancer patient to the pretreatment pCT reconstruction, respectively. After applying the rigid or deformable image registration algorithm to retrieve the original pCT image before transformation, the accuracy of the registration was assessed. To simulate very low‐dose imaging for patient setup, the proton CT images were reconstructed with 100%, 50%, 25%, and 12.5% of the total number of histories of the original planning pCT simulation scan, respectively.

Results

The residual errors in image registration were lower than 1 mm and 1° of magnitude regardless of the anatomic directions and imaging dose. The mean residual errors ranges found for rigid image registration were from −0.29 ± 0.09 to 0.51 ± 0.50 mm for translations and from −0.05 ± 0.13 to 0.08 ± 0.08 degrees for rotations. The percentages of sub‐millimetric errors found, for deformable image registration, were between 63.5% and 100%.

Conclusion

This experimental head phantom study demonstrated the potential of low‐dose pCT imaging for 3D image registration. Further work is needed to confirm the value pCT for pretreatment image‐guided proton therapy.

The impact of secondary fragments on the image quality of helium ion imaging

Single-event ion imaging enables the direct reconstruction of the relative stopping power (RSP) information required for ion-beam therapy. Helium ions were recently hypothesized to be the optimal species for such technique. The purpose of this work is to investigate the effect of secondary fragments on the image quality of helium CT (HeCT) and to assess the performance of a prototype proton CT (pCT) scanner when operated with helium beams in Monte Carlo simulations and experiment. Experiments were conducted installing the U.S. pCT consortium prototype scanner at the Heidelberg Ion-Beam Therapy Center (HIT). Simulations were performed with the scanner using the TOPAS toolkit. HeCT images were reconstructed for a cylindrical water phantom, the CTP404 (sensitometry), and the CTP528 (line-pair) [Formula: see text] ® modules. To identify and remove individual events caused by fragmentation, the multistage energy detector of the scanner was adapted to function as a [Formula: see text] telescope. The use of the developed filter eliminated the otherwise arising ring artifacts in the HeCT reconstructed images. For the HeCT reconstructed images of a water phantom, the maximum RSP error was improved by almost a factor 8 with respect to unfiltered images in the simulation and a factor 10 in the experiment. Similarly, for the CTP404 module, the mean RSP accuracy improved by a factor 6 in both the simulation and the experiment when the filter was applied (mean relative error 0.40% in simulation, 0.45% in experiment). In the evaluation of the spatial resolution through the CTP528 module, the main effect of the filter was noise reduction. For both simulated and experimental images the spatial resolution was  ∼4 lp cm-1. In conclusion, the novel filter developed for secondary fragments proved to be effective in improving the visual quality and RSP accuracy of the reconstructed images. With the filter, the pCT scanner is capable of accurate HeCT imaging.

Helium CT: Monte Carlo simulation results for an ideal source and detector with comparison to proton CT

PURPOSE

To evaluate the accuracy of relative stopping power and spatial resolution of images reconstructed with simulated helium CT (HeCT) in comparison to proton CT (pCT).

METHODS

A Monte Carlo (MC) study with the TOPAS tool was performed to compare the accuracy of relative stopping power (RSP) reconstruction and spatial resolution of low-fluence HeCT to pCT, both using 200 MeV/u particles. An ideal setup consisting of a flat beam source and a totally absorbing energy-range detector was implemented to estimate the theoretically best achievable RSP accuracy for the calibration and reconstruction methods currently used for pCT. The phantoms imaged included a cylindrical water phantom with inserts of different materials, sizes, and positions, a Catphan phantom with a module containing high-contrast line pairs (CTP528) and a module with cylindrical inserts of different RSP (CTP404), as well as a voxelized 10-year-old female phantom. Dose to the cylindrical water phantom was also calculated. The RSP accuracy was studied for all phantoms except the CTP528 module. The latter was used for the estimation of the spatial resolution, evaluated as the modulation transfer function (MTF) at 10%.

RESULTS

An overall error under 0.5% was achieved for HeCT for the water phantoms with the different inserts, in all cases better than that for pCT, in some cases by a factor 3. The inserts in the CTP404 module were reconstructed with an average RSP accuracy of 0.3% for HeCT and 0.2% for pCT. Anatomic structures (brain, bones, air cavities, etc.) in the digitized head phantom were well recognizable and no artifacts were visible with both HeCT and pCT. The three main tissue materials (soft tissue, brain, and cranium) were well identifiable in the reconstructed RSP-volume distribution with both imaging modalities. Using 360 projection angles, the spatial resolution was 4 lp/cm for HeCT and 3 lp/cm for pCT. Generally, spatial resolution increased with the number of projection angles and was always higher for HeCT than for pCT for the same number of projections. When HeCT and pCT scan were performed to deliver the same dose in the phantom, the resolution for HeCT was higher than pCT.

CONCLUSION

MC simulations were used to compare HeCT and pCT image reconstruction. HeCT images had similar or better RSP accuracy and higher spatial resolution compared to pCT. Further investigation of the potential of helium ion imaging is warranted.