Using Modularized Pin Ridge Filter in Proton FLASH Planning for Liver Stereotactic Ablative Body Radiotherapy.

We previously developed a FLASH planning framework for streamlined pin-ridge-filter (pin-RF) design, demonstrating its feasibility for single-energy proton FLASH planning. In this study, we refined the pin-RF design for easy assembly using reusable modules, focusing on its application in liver SABR. This framework generates an intermediate IMPT plan and translates it into step widths and thicknesses of pin-RFs for a single-energy FLASH plan. Parameters like energy spacing, monitor unit limit, and spot quantity were adjusted during IMPT planning, resulting in pin-RFs assembled using predefined modules with widths from 1 to 6 mm, each with a WET of 5 mm. This approach was validated on three liver SABR cases. FLASH doses, quantified using the FLASH effectiveness model at 1 to 5 Gy thresholds, were compared to conventional IMPT (IMPT-CONV) doses to assess clinical benefits. The highest demand for 6 mm width modules, moderate for 2-4 mm, and minimal for 1- and 5-mm modules were shown across all cases. At lower dose thresholds, the two-beam case showed significant dose reductions (>23%), while the other two three-beam cases showed moderate reductions (up to 14.7%), indicating the need for higher fractional beam doses for an enhanced FLASH effect. Positive clinical benefits were seen only in the two-beam case at the 5 Gy threshold. At the 1 Gy threshold, the FLASH plan of the two-beam case outperformed its IMPT-CONV plan, reducing dose indicators by up to 28.3%. However, the three-beam cases showed negative clinical benefits at the 1 Gy threshold, with some dose indicators increasing by up to 16% due to lower fractional beam doses and closer beam arrangements. This study evaluated the feasibility of modularizing streamlined pin-RFs in single-energy proton FLASH planning for liver SABR, offering guidance on optimal module composition and strategies to enhance FLASH planning.

Streamlined pin-ridge-filter design for single-energy proton FLASH planning.

BACKGROUND: FLASH radiotherapy (FLASH-RT) with ultra-high dose rate has yielded promising results in reducing normal tissue toxicity while maintaining tumor control. Planning with single-energy proton beams modulated by ridge filters (RFs) has been demonstrated feasible for FLASH-RT. PURPOSE: This study explored the feasibility of a streamlined pin-shaped RF (pin-RF) design, characterized by coarse resolution and sparsely distributed ridge pins, for single-energy proton FLASH planning. METHODS: An inverse planning framework integrated within a treatment planning system was established to design streamlined pin RFs for single-energy FLASH planning. The framework involves generating a multi-energy proton beam plan using intensity-modulated proton therapy (IMPT) planning based on downstream energy modulation strategy (IMPT-DS), followed by a nested pencil-beam-direction-based (PBD-based) spot reduction process to iteratively reduce the total number of PBDs and energy layers along each PBD for the IMPT-DS plan. The IMPT-DS plan is then translated into the pin-RFs and the single-energy beam configurations for IMPT planning with pin-RFs (IMPT-RF). This framework was validated on three lung cases, quantifying the FLASH dose of the IMPT-RF plan using the FLASH effectiveness model. The FLASH dose was then compared to the reference dose of a conventional IMPT plan to measure the clinical benefit of the FLASH planning technique. RESULTS: The IMPT-RF plans closely matched the corresponding IMPT-DS plans in high dose conformity (conformity index of <1.2), with minimal changes in V7Gy and V7.4 Gy for the lung (<3%) and small increases in maximum doses (Dmax) for other normal structures (<3.4 Gy). Comparing the FLASH doses to the doses of corresponding IMPT-RF plans, drastic reductions of up to nearly 33% were observed in Dmax for the normal structures situated in the high-to-moderate-dose regions, while negligible changes were found in Dmax for normal structures in low-dose regions. Positive clinical benefits were seen in comparing the FLASH doses to the reference doses, with notable reductions of 21.4%-33.0% in Dmax for healthy tissues in the high-dose regions. However, in the moderate-to-low-dose regions, only marginal positive or even negative clinical benefit for normal tissues were observed, such as increased lung V7Gy and V7.4 Gy (up to 17.6%). CONCLUSIONS: A streamlined pin-RF design was developed and its effectiveness for single-energy proton FLASH planning was validated, revealing positive clinical benefits for the normal tissues in the high dose regions. The coarsened design of the pin-RF demonstrates potential advantages, including cost efficiency and ease of adjustability, making it a promising option for efficient production.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy.

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETddistributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETddistribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETdmap generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETdmaps generated by the proposed method are 0.096 ± 0.019 keVµm-1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keVµm-1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETdmaps from dose maps and has the potential to improve proton therapy planning by providing accurate LETdinformation.

Feasibility study of hybrid inverse planning with transmission beams and single-energy spread-out Bragg peaks for proton FLASH radiotherapy.

BACKGROUND: Ultra-high dose rate (FLASH) proton planning with only transmission beams (TBs) has limitations in normal tissue sparing. The single-energy spread-out Bragg peaks (SESOBPs) of the FLASH dose rate have been demonstrated feasible for proton FLASH planning. PURPOSE: To investigate the feasibility of combining TBs and SESOBPs for proton FLASH treatment. METHODS: A hybrid inverse optimization method was developed to combine the TBs and SESOBPs (TB-SESOBP) for FLASH planning. The SESOBPs were generated field-by-field from spreading out the BPs by pre-designed general bar ridge filters (RFs) and placed at the central target by range shifters (RSs) to obtain a uniform dose within the target. The SESOBPs and TBs were fully placed field-by-field allowing automatic spot selection and weighting in the optimization process. A spot reduction strategy was conducted in the optimization process to push up the minimum MU/spot assuring the plan deliverability at beam current of 165 nA. The TB-SESOBP plans were validated in comparison with the TB only (TB-only) plans and the plans with the combination of TBs and BPs (TB-BP plans) regarding 3D dose and dose rate (dose-averaged dose rate) distributions for five lung cases. The FLASH dose rate coverage (V40Gy/s ) was evaluated in the structure volume receiving > 10% of the prescription dose. RESULTS: Compared to the TB-only plans, the mean spinal cord D1.2cc drastically reduced by 41% (P < 0.05), the mean lung V7Gy and V7.4 Gy moderately reduced by up to 17% (P < 0.05), and the target dose homogeneity slightly increased in the TB-SESOBP plans. Comparable dose homogeneity was achieved in both TB-SESOBP and TB-BP plans. Besides, prominent improvements were achieved in lung sparing for the cases of relatively large targets by the TB-SESOBP plans compared to the TB-BP plans. The targets and the skin were fully covered with the FLASH dose rate in all three plans. For the OARs, V40Gy/s  = 100% was achieved by the TB-only plans while V40Gy/s  > 85% was obtained by the other two plans. CONCLUSION: We have demonstrated that the hybrid TB-SESOBP planning was feasible to achieve FLASH dose rate for proton therapy. With pre-designed general bar RFs, the hybrid TB-SESOBP planning could be implemented for proton adaptive FLASH radiotherapy. As an alternative FLASH planning approach to TB-only planning, the hybrid TB-SESOBP planning has great potential in dosimetrically improving OAR sparing while maintaining high target dose homogeneity.

A prediction model for dosimetric-based lung adaptive radiotherapy.

PURPOSE: Anatomical changes occurred during the treatment course of radiation therapy for lung cancer patients may introduce clinically unacceptable dosimetric deviations from the planned dose. Adaptive radiotherapy (ART) can compensate these dosimetric deviations in subsequent treatments via plan adaption. Determining whether and when to trigger plan adaption during the treatment course is essential to the effectiveness and efficiency of ART. In this study, we aimed to develop a prediction model as an auxiliary decision-making tool for lung ART to identify the patients with intrathoracic anatomical changes that would potentially benefit from the plan adaptions during the treatment course. METHODS: Seventy-one pairs of weekly cone-beam computer tomography (CBCT) and planning CT (pCT) from 17 advanced non-small cell lung cancer patients were enrolled in this study. To assess the dosimetric impacts brought by anatomical changes observed on each CBCT, dose distribution of the original treatment plan on the CBCT anatomy was calculated on a virtual CT generated by deforming the corresponding pCT to the CBCT and compared to that of the original plan. A replan was deemed needed for the CBCT anatomy once the recalculated dose distribution violated our dosimetric-based trigger criteria. A three-dimensional region of significant anatomical changes (region of interest, ROI) between each CBCT and the corresponding pCT was identified, and 16 morphological features of the ROI were extracted. Additionally, eight features from the overlapped volume histograms (OVHs) of patient anatomy were extracted for each patient to characterize the patient-specific anatomy. Based on the 24 extracted features and the evaluated replanning needs of the pCT-CBCT pairs, a nonlinear supporting vector machine was used to build a prediction model to identify the anatomical changes on CBCTs that would trigger plan adaptions. The most relevant features were selected using the sequential backward selection (SBS) algorithm and a shuffling-and-splitting validation scheme was used for model evaluation. RESULTS: Fifty-five CBCT-pCT pairs were identified of having an ROI, among which 21 CBCT anatomies required plan adaptions. For these 21 positive cases, statistically significant improvements in the sparing of lung, esophagus and spinal cord were achieved by plan adaptions. A high model performance of 0.929 AUC (area under curve) and 0.851 accuracy was achieved with six selected features, including five ROI shape features and one OVH feature. Without involving the OVH features in the feature selection process, the mean AUC and accuracy of the model significantly decreased to 0.826 and 0.779, respectively. Further investigation showed that poor prediction performance with AUC of 0.76 was achieved by the univariate model in solving this binary classification task. CONCLUSION: We built a prediction model based on the features of patient anatomy and the anatomical changes captured by on-treatment CBCT imaging to trigger plan adaption for lung cancer patients. This model effectively associated the anatomical changes with the dosimetric impacts for lung ART. This model can be a promising tool to assist the clinicians in making decisions for plan adaptions during the treatment courses.

The structural similarity index for IMRT quality assurance: radiomics-based error classification.

PURPOSE: The implementation of radiomics and machine learning (ML) techniques on analyzing two-dimensional gamma maps has been demonstrated superior to the conventional gamma analysis for error identification in intensity modulated radiotherapy (IMRT) quality assurance (QA). Recently, the Structural SIMilarity (SSIM) sub-index maps were shown to be able to reveal the error types of the dose distributions. In this study, we aimed to apply radiomics analysis on SSIM sub-index maps and develop ML models to classify delivery errors in patient-specific dynamic IMRT QA. METHODS: Twenty-one sliding-window IMRT plans of 180 beams for three treatment sites were involved in this study. Four types of machine-related errors of various magnitudes were simulated for each beam at each control point, including the monitor unit (MU) variations, same-directional and opposite-directional shifts of the multileaf collimators (MLCs) and random mispositioning of the MLCs. In the QA process, a total of 1620 portal dose (PD) images were acquired for the beams with and without errors. The predicted PD images of the original beams were set as references. To quantify the agreement between a measured PD image and the corresponding predicted PD image, four difference maps including three SSIM sub-index maps, and one dose difference-derived map were calculated. Then, radiomic features were extracted from the four difference maps of each measured PD image. We tested four typical classifiers including linear discriminant classifier (LDC), two supporting vector machine (SVM) classifiers, and random forest (RF) for this multiclass classification task. A nested cross-validation scheme was used for model evaluations, where the SVM recursive feature elimination method was applied for feature selection. Finally, the performance of the ML model on identifying the error-free and the erroneous cases was compared to that of the conventional gamma analysis. RESULTS: The statistics of the selected features showed that all of the difference maps and the feature categories made balanced contributions to solve this classification task. Best performance was achieved by the Linear-SVM model with average overall classification accuracy of 0.86. Specifically, the average classification accuracies of the shift, opening, and the random errors were around 0.9. Moreover, ~80% of error-free and MU errors were correctly classified. Using gamma analysis, the 3 mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2 mm/2% criterion increased to 0.79, still 8% worse than that of the ML model. CONCLUSIONS: We proposed an ML-based method for machine-related error identification in patient-specific dynamic IMRT QA, where radiomic analysis on SSIM sub-index maps were used for feature extraction. With extensive validation to select the best features and classifiers, high accuracies in error classification were achieved. Compared with the conventional gamma threshold method, this approach has great potential in error identification for the patient-specific IMRT QA process.

Electron modulated arc therapy (EMAT) using photon MLC for postmastectomy chest wall treatment I: Monte Carlo-based dosimetric characterizations.

PURPOSE: To study the dosimetric properties of electron arc beams delivered by photon-beam multi-leaf collimators (pMLC) in electron modulated arc therapy (EMAT) for postmastectomy chest wall treatments. METHODS: Using the Monte Carlo method, we simulated a 2100EX Varian linear accelerator and verified the beam models in a water tank. Dosimetric characterizations were performed on cylindrical water phantoms of elliptical bases with various field sizes, arc ranges and source-to-surface distances (SSDs) for 6, 9 and 12 MeV beam energy. RESULTS: The arc beam has a higher bremsstrahlung dose than the static beam at the isocenter due to crossfire, but choosing a field size greater than 5 cm effectively reduces the bremsstrahlung dose. The depths of the 90% maximum dose located at 1.7, 2.8 and 4.1 cm for 6, 9 and 12 MeV, respectively, are similar to those of the static beams and independent of the field size and arc range. CONCLUSION: Based on the study, we recommend using the 5 cm field width for electron arc beams considering both bremsstrahlung dose at the isocenter and the arc profile penumbra. To ensure sufficient PTV edge coverage, we recommend a field length extension of at least 4 cm from PTV's edge for all beam energies and an arc extension of around 7°, 5°, and 5° for beam energies 6, 9, and 12 MeV, respectively. These dosimetric characterizations are the basis of pMLC-delivered EMAT treatment planning for postmastectomy chest wall patients.

Flattening filter free in intensity-modulated radiotherapy (IMRT) - Theoretical modeling with delivery efficiency analysis.

PURPOSE: The flattening filter (FF) has traditionally been used to flatten beams or create uniform fields in conformal and intensity-modulated radiation therapy (IMRT) but reduces the dose rate. Many studies have confirmed improvement in delivery efficiency by removing the FF, also known as flattening filter free (FFF), especially for small field stereotactic body radiation therapy (SBRT); but it is unclear if large treatment fields still favor the FFF beam. We propose a novel, unified approach to quantify delivery efficiency of the FFF and flattened beams. METHODS: We modeled the FF effect by inverse conical filters and systematically studied delivery efficiency (beam-on time, BOT) by varying the filter thickness, including the FF and FFF mode. We formulated the BOT of different beams for any arbitrary fluence map in linear programming to solve the optimal inverse conical filter that minimizes the BOT. One-dimensional optimal filters of minimum BOT were also derived in closed form for conical fluence to gain insight for arbitrary clinical fluence maps. We evaluated the BOT of the FFF beam and flattened beam for conformal treatment fields of various dimensions ranging from 5 cm × 5 cm to 25 cm × 25 cm. We also analyzed the BOT for 698 clinical IMRT prostate fluence maps of field size 10 cm × 10 cm, 17 head-and-neck fluence maps of field size 15 cm × 15 cm, and additional realistic test data from 90° rotation and up to 40 cm × 40 cm enlargement of these clinical fluence maps, which were all initially generated with flattened beams. RESULTS: The FFF beam minimized the BOT for A field size less than 20 cm in single leaf pair cases and for conformal fields of dimension less than 20 cm × 20 cm. The FFF beam also minimized the BOT for all tested prostate and head-and-neck cases. The median BOT ratios of the FFF beam to the flattened beam were 0.56 and 0.61 for prostate and head-and-neck cases, respectively. The FFF beam minimized the BOT for field size up to 30 cm × 30 cm and had similar BOTs to those of the flattened beam for field size greater than 30 cm × 30 cm in those clinically realistic test data. CONCLUSION: The filter modeling and BOT calculation enable us to quantify delivery efficiency of the FFF beam and flattened beam in a unified approach. The FFF beam minimized the BOT both theoretically and in simulations for all clinically relevant field sizes and fluence maps in IMRT. The results for conformal fields imply that the FFF beam requires less BOT than the flattened beam for volumetric modulated arc therapy (VMAT) treatments. The delivery efficiency consideration favors the FFF beam in intensity-modulated treatments and may eventually lead to removal of the FF in all future linear accelerator head designs.