Stopping Power Ratio Estimation Research Articles

Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers.

Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCTimages. To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinicalapplications. The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testingpurposes. The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results ofCBCT. We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.

Commissioning and Validation of CT Number to SPR Calibration in Carbon Ion Therapy Facility

PurposeWe performed computed tomography (CT)—stopping power ratio (SPR) calibration in a carbon-ion therapy facility and evaluated SPR estimation accuracy. Materials and MethodsA polybinary tissue model method was used for the calibration of CT numbers and SPR. As a verification by dose calculation, we created a virtual phantom to which the CT-SPR calibration table was applied. Then, SPR was calculated from the change in the range of the treatment planning beam when changing to 19 different CT numbers, and the accuracy of the treatment planning system (TPS) calculation of SPR values from the CT-SPR calibration table was validated. As a verification by measurement, 5 materials (water, milk, olive oil, ethanol, 40% K2HPO4) were placed in a container, and the SPR was obtained by measurement from the change in the range of the beam that passed through the materials. ResultsThe results of the dose calculations of the TPS showed that the results agreed within 1% for the lower CT numbers up to 1000 HU, but there was a difference of 3.0% in the higher CT number volume. The difference between the SPR calculated by TPS and the SPR caused by the difference in the energy of the incident particles agreed within 0.51%. The accuracy of SPR estimation was measured, and the error was within 2% for all materials tested. ConclusionThese results indicate that the SPR estimation errors are within the range of errors that can be expected in particle therapy. From commissioning and verification results, the CT-SPR calibration table obtained during this commissioning process is clinically applicable.

Investigation on the proton range uncertainty with spectral CT-based virtual monoenergetic images.

The stopping power ratio (SPR) prediction error will contribute to the range uncertainty of proton therapy. Spectral CT is promising in reducing the uncertainty in SPR estimation. The purpose of this research is to determine the optimal energy pairs of SPR prediction for each tissue and to evaluate the dose distribution and range difference between the spectral CT with the optimal energy pairs method and the single energy CT (SECT) method. A new method was proposed based on image segmentation to calculate the proton dose with spectral CT images for the head and body phantom. CT number of each organ region were converted to SPR with the optimal energy pairs of each organ. The CT images were segmented into different organ parts with thresholding method. Virtual monoenergetic (VM) images from 70keV to 140keV were investigated to determine the optimal energy pairs for each organ based on Gammex 1467 phantom. The beam data of Shanghai Advanced Proton Therapy facility (SAPT) was employed in matRad (an open-source software for radiation treatment planning) for the dose calculation. The optimal energy pairs were obtained for each tissue. The dose distribution of two tumor sites (brain and lung) were calculated with the aforementioned optimal energy pairs. The maximum dose deviation between spectral CT and SECT at the target region was 2.57% and 0.84% for the lung tumor and brain tumor respectively. The range difference between spectral and SECT was significant with 1.8411mm for the lung tumor. γ passing rate was 85.95% and 95.49% for the lung tumor and brain tumor with the criterion 2%/2mm. This work presents a way to determine the optimal energy pairs for each organ and to calculate the dose distribution based on the more accurate SPR prediction.

Stopping-power ratio of body tissues with updated effective energies and elemental I values for treatment planning of proton therapyand ion beam therapy with helium, carbon, oxygen, and neon ions.

The stopping-power ratio (SPR) of body tissues relative to water depends on the particle energy and mean excitation energy (I value) of the tissues. Effective energies to minimize the range error in proton therapyand ion beam therapy with helium, carbon, oxygen, and neon ions and elemental I values have been updated in recent studies. We investigated the effects of these updates on SPR estimation for computed tomography-based treatment planning. The updates led to an increase of up to 0.5% in the SPRs of soft tissues, whereas they led to a decrease of up to 1.9% in the SPRs of bone tissues compared with the current clinical settings. For 44proton beams planned for 15 randomly sampled patients, the mean water-equivalent target depth change was -0.2mm with a standard deviation of 0.2mm. The maximum change was -0.6mm, which we consider to be insignificant in clinical practice.

Evaluation of treatment planning system accuracy in estimating the stopping‐power ratio of immobilization devices for proton therapy

To assess treatment planning system (TPS) accuracy in estimating the stopping-power ratio (SPR) of immobilization devices commonly used in proton therapy and to evaluate the dosimetric effect of SPR estimation error for a set of clinical treatment plans. Computed tomography scans of selected clinical immobilization devices were acquired. Then, the water-equivalent thickness (WET) and SPR values of these devices based on the scans were estimated in a commercial TPS. The reference SPR of each device was measured using a multilayer ion chamber (MLIC), and the differences between measured and TPS-estimated SPRs were calculated. These findings were utilized to calculate corrected dose distributions of 15 clinical proton plans for three treatment sites: extremity, abdomen, and head-and-neck. The original and corrected dose distributions were compared using a set of target and organs-at-risk (OARs) dose-volume histogram (DVH) parameters. On average, the TPS-estimated SPR was 19.5% lower (range, -35.1% to 0.2%) than the MLIC-measured SPR. Due to the relatively low density of most immobilization devices used, the WET error was typically <1mm, but up to 2.2mm in certain devices. Overriding the SPR of the immobilization devices to the measured values did not result in significant changes in the DVH metrics of targets and most OARs. However, some critical OARs showed noticeable changes of up to 6.7% in maximum dose. The TPS tends to underestimate the SPR of selected proton immobilization devices by an average of about 20%, but this does not induce major WET errors because of the low density of the devices. The dosimetric effect of this SPR error was negligible for most treatment sites, although the maximum dose of a few OARs exhibited noticeable variations.

Towards subpercentage uncertainty proton stopping-power mapping via dual-energy CT: Direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method.

To assess the potential of a joint dual-energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%. JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for 10 high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR-BVM stopping-power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high-density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (head phantom) and 1.1% and -1.1% (body phantom). The overall root-mean-square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and twofold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k=1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6-0.9%. Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k=1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving<1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise, and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.

Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study

Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limitations in resolving the influence from the beam-hardening artifact, i.e. CT number variation of the same object scanned under different imaging conditions, such as different patient size and location in the field-of-view (FOV). We present a convolutional neural network (CNN)-based framework to estimate proton SPR that accounts for patient geometry variation and addresses CT number variation. The proposed framework was tested on both prostate and head-and-neck (HN) patient datasets. Simulated CT images were used in order to have a well-defined ground-truth SPR for evaluation. Two training scenarios were evaluated: training with patient CT images (ideal scenario) and training with computational phantoms (realistic scenario). For the training in ideal scenario, computational phantoms were created based on 120 kVp patient CT images using a custom-defined density and material translation curve. Then, 80 kVp and 150 kVp Sn DECT image pairs were obtained using ray-tracing simulation, and their corresponding SPR was calculated from the known density and elemental compositions. For the training in realistic scenario, computational phantoms were created based on the geometry of calibration phantoms. For both scenarios, evaluation was performed on the phantoms created from patient CT images. Compared to a conventional parametric model, U-net trained with computational phantoms (realistic scenario) reduced the SPR estimation uncertainty (95th percentile) of the prostate patient from 1.10% to 0.71%, and HN patient from 2.11% to 1.20%. With the U-net trained with patient images (ideal scenario) uncertainty values were 0.32% and 0.42% for prostate and HN patients, respectively. These results suggest that CNN has great potential to improve the accuracy of SPR estimation in proton therapy by incorporating individual patient geometry information.

Material assignment for proton range prediction in Monte Carlo patient simulations using stopping-power datasets

Motivation and objective. For each institute, the selection and calibration of the most suitable approach to assign material properties for Monte Carlo (MC) patient simulation in proton therapy is a major challenge. Current conventional approaches based on computed tomography (CT) depend on CT acquisition and reconstruction settings. This study proposes a material assignment approach, referred to as MATA (MATerial Assignment), which is independent of CT scanner properties and, therefore, universally applicable by any institute. Materials and methods. The MATA approach assigns material properties to the physical quantity stopping-power ratio (SPR) using a set of 40 material compositions specified for human tissues and linearly determined mass density. The application of clinically available CT-number-to-SPR conversion avoids the need for any further calibration. The MATA approach was validated with homogeneous and heterogeneous SPR datasets by assessing the SPR accuracy after material assignment obtained either based on dose scoring or determination of water-equivalent thickness. Finally, MATA was applied on patient datasets to evaluate dose differences induced by different approaches for material assignment and SPR prediction. Results. The deviation between the SPR after material assignment and the input SPR was close to zero in homogeneous datasets and below 0.002 (0.2% relative to water) in heterogeneous datasets, which was within the systematic uncertainty in SPR estimation. The comparison of different material assignment approaches revealed relevant differences in dose distribution and SPR. The comparison between two SPR prediction approaches, a standard look-up table and direct SPR determination from dual-energy CT, resulted in patient-specific mean proton range shifts between 1.3 mm and 4.8 mm. Conclusion. MATA eliminates the need for institution-specific adaptations of the material assignment. It allows for using any SPR dataset and thus facilitates the implementation of more accurate SPR prediction approaches. Hence, MATA provides a universal solution for patient modeling in MC-based proton treatment planning.

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

Proton treatment planning relies on an accurate determination of stopping-power ratio (SPR) from x-ray computed tomography (CT). A refinement of the heuristic CT-based 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. 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. The application of the DirectSPR-based adapted HLUT instead of the nonadapted HLUT reduced the systematic proton range differences from 1.2% (1.1mm) to -0.1% (0.0mm) for brain-tumor patients, 1.7% (4.1mm) to 0.2% (0.5mm) for prostate-cancer patients, and 2.0% (2.9mm) to -0.1% (0.0mm) 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. 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.

Optimal energy selection for proton stopping-power-ratio estimation using dual-energy CT-based monoenergetic imaging

The dual-energy computed tomography (DECT)-based approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation, but cannot be easily implemented with most commercial proton treatment planning systems (TPS). In this study, we revisited the idea of coupling the stoichiometric calibration method with virtual monoenergetic CT datasets (MonoCT) generated by modern DECT scanners, because of its readiness for implementation with the existing TPS. Our objective was to determine the optimal energy of the MonoCT dataset for stoichiometric calibration and estimate the overall uncertainty in SPR estimation at the optimal energy. We performed stoichiometric calibration for MonoCT datasets across the energy range available on a Siemens Force DECT scanner and a Philips IQon DECT scanner in a 10 keV step. We estimated the uncertainties of different sources (imaging, modeling, and inherent uncertainties) for different tissue types (lung, soft, and bone tissues) associated with each energy; these were then combined into a single composite uncertainty for three tumor sites (head-and-neck (HN), lung, and prostate). The optimal energy was eventually selected based on the composite range uncertainty, which turned out to be 160 keV for both DECT scanners. At 160 keV, the total uncertainties (2σ) in SPR estimation were determined to be 3.2%–4.5%, 0.9%, and 1.4%–1.6% for lung, soft, and bony tissues, respectively. These results were comparable to the corresponding values estimated for the DECT approach evaluated in our previous study: 3.8%, 1.2% and 2.0%, for lung, soft, and bony tissues, respectively. The composite range uncertainties (2σ) were estimated as 1.5%, 1.7%, and 1.5% for prostate, lung, and HN, respectively. Our results demonstrated the potential of MonoCT images for reducing proton SPR uncertainty. Further clinical studies are needed to compare this approach with the DECT approach directly on real patient cases.

Systematic analysis of the impact of imaging noise on dual-energy CT-based proton stopping power ratio estimation.

Dual-energy CT (DECT) has been shown to have a great potential in reducing the uncertainty in proton stopping power ratio (SPR) estimation, when compared to current standard method - the stoichiometric method based on single-energy CT (SECT). However, a few recent studies indicated that imaging noise may have a substantial impact on the performance of the DECT-based approach, especially at a high noise level. The goal of this study is to quantify the uncertainty in SPR and range estimation caused by noise in the DECT-based approach under various conditions. Two widely referred parametric DECT methods were studied: the Hünemohr-Saito (HS) method and the Bourque method. Both methods were calibrated using Gammex tissue substitute inserts scanned on the Siemens Force DECT scanner. An energy pair of 80 and 150kVp with a tin filter was chosen to maximize the spectral separation. After calibrating the model with the Gammex phantom, CT numbers were synthesized using the density and elemental composition from ICRU 44 human tissues to be used as a reference, in order to evaluate the impact of noise alone while putting aside other sources of uncertainty. Gaussian noise was introduced to the reference CT numbers and its impact was measured with the difference between estimated SPR and its noiseless reference SPR. The uncertainty caused by noise was divided into two independent categories: shift of the mean SPR and variation of SPR. Their overall impact on range uncertainty was evaluated on homogeneous and heterogeneous tissue samples of various water equivalent path lengths (WEPL). Due to the algorithms being nonlinear and/or having hard thresholds in the CT number to SPR mapping, noise in the CT numbers induced a shift in the mean SPR from its noiseless reference SPR. The degree of the mean shift was dependent on the algorithm and tissue type, but its impact on the SPR uncertainty was mostly small compared to the variation. All mean shifts observed in this study were within 0.5% at a noise level of 2%. The ratio of the influence of variation to mean shift was mostly greater than 1, indicating that variation more likely determined the uncertainty caused by noise. Overall, the range uncertainty (95th percentile) caused by noise was within 1.2% and 1.0% for soft and bone tissues, respectively, at 2% noise with 50 voxels. This value can be considered an upper limit as more voxels and lower noise level rapidly decreased the uncertainty. We have systematically evaluated the impact of noise to the DECT-based SPR estimation and identified under various conditions that the variation caused by noise is the dominant uncertainty-contributing component. We conclude that, based on the noise level and tumor depth, it is important to estimate and include the uncertainty due to noise in estimating the overall range uncertainty before implementing a small margin in the range of 1%.

Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data.

To experimentally commission a dual-energy CT (DECT) joint statistical image reconstruction (JSIR) method, which is built on a linear basis vector model (BVM) of material characterization, for proton stopping power ratio (SPR) estimation. The JSIR-BVM method builds on the relationship between the energy-dependent photon attenuation coefficients and the proton stopping power via a pair of BVM component weights. The two BVM component images are simultaneously reconstructed from the acquired DECT sinograms and then used to predict the electron density and mean excitation energy (I-value), which are required by the Bethe equationfor SPR computation. A post-reconstruction image-based DECT method, which utilizes the two separate CT images reconstructed via the scanner's software, was implemented for comparison. The DECT measurement data were acquired on a Philips Brilliance scanner at 90 and 140kVp for two phantoms of different sizes. Each phantom contains 12 different soft and bony tissue surrogates with known compositions. The SPR estimation results were compared to the reference values computed from the known compositions. The difference of the computed water equivalent path lengths (WEPL) across the phantoms between the two methods was also compared. The overall root-mean-square (RMS) of SPR estimation error of the JSIR-BVM method are 0.33% and 0.37% for the head- and body-sized phantoms, respectively, and all SPR estimates of the test samples are within 0.7% of the reference ground truth. The image-based method achieves overall RMS errors of 2.35% and 2.50% for the head- and body-sized phantoms, respectively. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image-based method. The average differences between the JSIR-BVM method and the image-based method are 0.54% and 1.02% for the head- and body-sized phantoms, respectively. By taking advantage of an accurate polychromatic CT data model and a model-based DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves good accuracy and precision on proton SPR estimation for various tissue surrogates and object sizes. In contrast, the experimentally achievable accuracy of the image-based method may be limited by the uncertainties in the image formation process. The result suggests that the JSIR-BVM method has the potential for more accurate SPR prediction compared to post-reconstruction image-based methods in clinical settings.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction.

Photon counting detectors (PCDs) are being introduced in advanced x-ray computed tomography (CT) scanners. From a single PCD-CT acquisition, multiple images can be reconstructed, each based on only a part of the original x-ray spectrum. In this study, we investigated whether PCD-CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements. A previously developed DECT-based SPR estimation method was adapted to PCD-CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD-CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD-CT images, full spectrum (one-bin) or two-bin images, respectively. In a theoretical framework, we evaluated the effect of patient-specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD-CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues. For the theoretical calculations, the root-mean-square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT- and SECT-based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four-bin images. In all tested cases, the new PCD method produced similar or better results than the SECT- and DECT-based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD-CT scans will be a qualified candidate for SPR estimations.

OC-0086: Validation of proton stopping power ratio estimation based on dual energy CT using organic tissues

OC-0086: Validation of proton stopping power ratio estimation based on dual energy CT using organic tissues

Material elemental decomposition in dual and multi-energy CT via a sparsity-dictionary approach for proton stopping power ratio calculation.

Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual-energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi-energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary-based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation. A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non-negative combination of only a few tissues in the dictionary. We formulated this as a non-convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies. Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method. The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy.

Validation of proton stopping power ratio estimation based on dual energy CT using fresh tissue samples

Dual energy CT (DECT) has been shown, in theoretical and phantom studies, to improve the stopping power ratio (SPR) determination used for proton treatment planning compared to the use of single energy CT (SECT). However, it has not been shown that this also extends to organic tissues. The purpose of this study was therefore to investigate the accuracy of SPR estimation for fresh pork and beef tissue samples used as surrogates of human tissues. The reference SPRs for fourteen tissue samples, which included fat, muscle and femur bone, were measured using proton pencil beams. The tissue samples were subsequently CT scanned using four different scanners with different dual energy acquisition modes, giving in total six DECT-based SPR estimations for each sample. The SPR was estimated using a proprietary algorithm (syngo.via DE Rho/Z Maps, Siemens Healthcare, Forchheim, Germany) for extracting the electron density and the effective atomic number. SECT images were also acquired and SECT-based SPR estimations were performed using a clinical Hounsfield look-up table. The mean and standard deviation of the SPR over large volume-of-interests were calculated. For the six different DECT acquisition methods, the root-mean-square errors (RMSEs) for the SPR estimates over all tissue samples were between 0.9% and 1.5%. For the SECT-based SPR estimation the RMSE was 2.8%. For one DECT acquisition method, a positive bias was seen in the SPR estimates, having a mean error of 1.3%. The largest errors were found in the very dense cortical bone from a beef femur. This study confirms the advantages of DECT-based SPR estimation although good results were also obtained using SECT for most tissues.

Optimum size of a calibration phantom for x-ray CT to convert the Hounsfield units to stopping power ratios in charged particle therapy treatment planning.

In charged-particle therapy treatment planning, the volumetric distribution of stopping power ratios (SPRs) of body tissues relative to water is used for patient dose calculation. The distribution is conventionally obtained from computed tomography (CT) images of a patient using predetermined conversion functions from the CT numbers to the SPRs. One of the biggest uncertainty sources of patient SPR estimation is insufficient correction of beam hardening arising from the mismatch between the size of the patient cross section and the calibration phantom for producing the conversion functions. The uncertainty would be minimized by selecting a suitable size for the cylindrical water calibration phantom, referred to as an ‘effective size’ of the patient cross section, Leffective. We investigated the Leffective for pelvis, abdomen, thorax, and head and neck regions by simulating an ideal CT system using volumetric models of the reference male and female phantoms. The Leffective values were 23.3, 20.3, 22.7 and 18.8 cm for the pelvis, abdomen, thorax, and head and neck regions, respectively, and the Leffective for whole body was 21.0 cm. Using the conversion function for a 21.0-cm-diameter cylindrical water phantom, we could reduce the root mean square deviation of the SPRs and their mean deviation to ≤0.011 and ≤0.001, respectively, in the whole body. Accordingly, for simplicity, the effective size of 21.0 cm can be used for the whole body, irrespective of body-part regions for treatment planning in clinical practice.

Optimization of dual‐energy CT acquisitions for proton therapy using projection‐based decomposition

Dual-energy computed tomography (DECT) has been presented as a valid alternative to single-energy CT to reduce the uncertainty of the conversion of patient CT numbers to proton stopping power ratio (SPR) of tissues relative to water. The aim of this work was to optimize DECT acquisition protocols from simulations of X-ray images for the treatment planning of proton therapy using a projection-based dual-energy decomposition algorithm. We have investigated the effect of various voltages and tin filtration combinations on the SPR map accuracy and precision, and the influence of the dose allocation between the low-energy (LE) and the high-energy (HE) acquisitions. For all spectra combinations, virtual CT projections of the Gammex phantom were simulated with a realistic energy-integrating detector response model. Two situations were simulated: an ideal case without noise (infinite dose) and a realistic situation with Poisson noise corresponding to a 20 mGy total central dose. To determine the optimal dose balance, the proportion of LE-dose with respect to the total dose was varied from 10% to 90% while keeping the central dose constant, for four dual-energy spectra. SPR images were derived using a two-step projection-based decomposition approach. The ranges of 70 MeV, 90 MeV, and 100 MeV proton beams onto the adult female (AF) reference computational phantom of the ICRP were analytically determined from the reconstructed SPR maps. The energy separation between the incident spectra had a strong impact on the SPR precision. Maximizing the incident energy gap reduced image noise. However, the energy gap was not a good metric to evaluate the accuracy of the SPR. In terms of SPR accuracy, a large variability of the optimal spectra was observed when studying each phantom material separately. The SPR accuracy was almost flat in the 30-70% LE-dose range, while the precision showed a minimum slightly shifted in favor of lower LE-dose. Photon noise in the SPR images (20 mGy dose) had lower impact on the proton range accuracy as comparable results were obtained for the noiseless situation (infinite dose). Root-mean-square range errors averaged over all irradiation angles associated to dual-energy imaging were comprised between 0.50 mm and 0.72 mm for the noiseless situation and between 0.51 mm and 0.77 mm for the realistic scenario. The impact of the dual-energy spectra and the dose allocation between energy levels on the SPR accuracy and precision determined through a projection-based dual-energy algorithm were evaluated to guide the choice of spectra for dual-energy CT for proton therapy. The dose balance between energy levels was not found to be sensitive for the SPR estimation. The optimal pair of dual-energy spectra was material dependent but on a heterogeneous anthropomorphic phantom, there was no significant difference in range accuracy and the choice of spectra could be driven by the precision, i.e., the energy gap.

Comprehensive analysis of proton range uncertainties related to stopping-power-ratio estimation using dual-energy CT imaging

The dual-energy CT-based (DECT) approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation as compared to the conventional stoichiometric calibration approach. The objective of this study was to analyze the factors contributing to uncertainty in SPR estimation using the DECT-based approach and to derive a comprehensive estimate of the range uncertainty associated with SPR estimation in treatment planning. Two state-of-the-art DECT-based methods were selected and implemented on a Siemens SOMATOM Force DECT scanner. The uncertainties were first divided into five independent categories. The uncertainty associated with each category was estimated for lung, soft and bone tissues separately. A single composite uncertainty estimate was eventually determined for three tumor sites (lung, prostate and head-and-neck) by weighting the relative proportion of each tissue group for that specific site. The uncertainties associated with the two selected DECT methods were found to be similar, therefore the following results applied to both methods. The overall uncertainty (1σ) in SPR estimation with the DECT-based approach was estimated to be 3.8%, 1.2% and 2.0% for lung, soft and bone tissues, respectively. The dominant factor contributing to uncertainty in the DECT approach was the imaging uncertainties, followed by the DECT modeling uncertainties. Our study showed that the DECT approach can reduce the overall range uncertainty to approximately 2.2% (2σ) in clinical scenarios, in contrast to the previously reported 1%.

Comparison of projection- and image-based methods for proton stopping power estimation using dual energy CT

Abstract Background and Purpose Several strategies for estimating stopping power ratio (SPR) from dual-energy CT (DECT) have been proposed to improve accuracy of proton dose calculations. DECT methods can mainly be categorized into projection-based methods, where material decomposition is performed prior to image reconstruction, and image-based methods, where decomposition takes place after image reconstruction. With the advent of photon-counting and dual-layer technology, projection-based methods could be considered for SPR estimation. In this simulation-based study we compared the SPR accuracy of one projection- and three image-based DECT methods. Materials and Methods X-ray CT projections of the female ICRP phantom were simulated using two different X-ray spectra with a realistic detector response and noise levels. ICRP slices at four different locations were selected. Reference SPR-maps were computed at 200MeV. The SPR comparison was based on percentage deviation inside ROIs and relative range errors calculated with Radon transform of difference maps. Results SPR root-mean-square errors (RMSE) over the selected ROIs were 0.54% for the projection-based method and 0.68%, 0.61% and 0.70% for the three image-based methods. The RMSE for the relative range errors were slightly smaller for the projection-based approach, but close to zero for all decomposition domains as positive and negative errors averaged out over the slice. Conclusions SPR estimations with the projection-based method produced slightly better results (though not statistically significant) than the three image-based methods used in this simulation-based study, therefore, with the advent of technological developments, projection-based methods could be considered for SPR estimation if projection data is available.