Beltran, Chris

Abstract The Mayo Clinic Florida microdosimetric kinetic model (MCF MKM) is a recently developed clonogenic survival model. Since the MCF MKM relies on novel strategies to a priori determine the cell-specific model parameters, the only experiment-specific input values are the α and ß terms of the linear–quadratic model (LQM) of clonogenic survival for the reference photon exposure. Because the two LQM terms are anti-correlated, the fitting process of the reference photon survival curve was found to significantly influence the MCF MKM calculations. This article reports this effect for two clinically relevant cell lines (human brain glioblastoma A-172, human healthy foreskin fibroblasts AG01522) and ions (1H and 12C ions).

Abstract Objective . To investigate the impact of scan path optimization on the dose accuracy and beam delivery time (BDT) of proton pencil beam scanning in the dose-driven continuous scanning (DDCS). Approach . A diverse set of six clinical plans, representing various spot patterns and treatment sites, was used to evaluate the effectiveness of scan time optimization and scan length optimization. The DDCS dose discrepancy and BDT with optimized scan paths was compared to the default serpentine scan path. Main results . Both scan time optimization and scan path optimization were able to reduce the DDCS dose discrepancy compared to the default serpentine scan path. All plans, except for the layer repainting lung plan, achieved a 2%/2 mm gamma pass rate of over 99% and less than 1% PTV DVH root mean square error (RMSE) through scan path optimization. In the case of the layer repainting lung plan, when compared to the default serpentine scan path, the 2%/2 mm gamma pass rate showed improvements from 91.3% to 93.1% and 95.8%, while the PTV DVH RMSE decreased from 2.1% to 1.7% and 1.1% for scan time optimization and scan length optimization, respectively. Although scan time optimization resulted in shorter total scan times for all plans compared to the default scan path and scan length optimization tended to have longer total scan times. However, due to the short total scan times and their minimal contribution to the total BDT, the impact of scan path optimization on the total BDT was practically negligible. Significance . Both scan time optimization and scan length optimization proved to be effective in minimizing DDCS dose discrepancy. No definitive winner can be determined between these two optimization approaches. Both scan time and scan length optimization had minimal effect on the total BDT.

AbstractBackgroundKilo‐voltage cone‐beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely‐used Feldkamp‐Davis‐Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary‐based methods, and prior information‐based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART.PurposeOne of the primary challenges in IR‐based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high‐resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels.MethodsThe HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp‐Davis‐Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process.ResultsWe evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms.ConclusionThe proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high‐definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.

Pencil beam scanning delivered with continuous scanning has several advantages over conventional discrete spot scanning. Such advantages include improved beam delivery efficiency and reduced beam delivery time. However, a move dose is delivered between consecutive spots with continuous scanning, and current treatment planning systems do not take this into account. Therefore, continuous scanning and discrete spot plans have an inherent dose discrepancy. Using the operating parameters of the state-of-the-art particle therapy system, we conducted a proof-of-concept study in which we systematically generated 28 plans for cubic targets with different combinations of plan parameters and simulated the dose discrepancies between continuous scanning and a planned one. A nomograph to guide the selection of plan parameters was developed to reduce the dose discrepancy. The effectiveness of the nomograph was evaluated with two clinical cases (one prostate and one liver). Plans with parameters guided by the nomograph decreased dose discrepancy than those used standard plan parameters. Specifically, the 2%/2 mm gamma passing rate increased from 96.3% to 100% for the prostate case and from 97.8% to 99.7% for the liver case. The CTV DVH root mean square error decreased from 2.2% to 0.2% for the prostate case and from 1.8% to 0.9% for the liver case. The decreased dose discrepancy may allow the relaxing of the delivery constraint for some cases, leading to greater benefits in continuous scanning. Further investigation is warranted.

Abstract Objective . UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines, however, were designed differently with various strategies, making it challenging to fairly compare the results from different studies. The objective of this study is to thoroughly evaluate the performance of UNet-based models on magnetic-resonance (MR)-Linac-based intensity-modulated radiation therapy (IMRT) dose calculations. Approach . The UNet-based models, including the standard-UNet, cascaded-UNet, dense-dilated-UNet, residual-UNet, HD-UNet, and attention-aware-UNet, were implemented. The model input is patient CT and IMRT field dose in water, and the output is patient dose calculated by DL model. The reference dose was calculated by the Monaco Monte Carlo module. Twenty training and ten test cases of prostate patients were included. The accuracy of the DL-calculated doses was measured using gamma analysis, and the calculation efficiency was evaluated by inference time. Results . All the studied models effectively corrected low-accuracy doses in water to high-accuracy patient doses in a magnetic field. The gamma passing rates between reference and DL-calculated doses were over 86% (1%/1 mm), 98% (2%/2 mm), and 99% (3%/3 mm) for all the models. The inference times ranged from 0.03 (graphics processing unit) to 7.5 (central processing unit) seconds. Each model demonstrated different strengths in calculation accuracy and efficiency; Res-UNet achieved the highest accuracy, HD-UNet offered high accuracy with the fewest parameters but the longest inference, dense-dilated-UNet was consistently accurate regardless of model levels, standard-UNet had the shortest inference but relatively lower accuracy, and the others showed average performance. Therefore, the best-performing model would depend on the specific clinical needs and available computational resources. Significance . The feasibility of using common UNet-based models for MR-Linac-based dose calculations has been explored in this study. By using the same model input type, patient training data, and computing environment, a fair assessment of the models’ performance was present.

AbstractBackgroundAbsolute dosimetry measurement is an integral part of Treatment Planning System (TPS) commissioning and it involves measuring the integrated absorbed dose to water for all energies in a pencil beam scanning delivery system. During the commissioning of Singapore's first proton therapy center, a uniform scanned field with an Advanced Markus chamber method was employed for this measurement, and a large dose fluctuation of at least 5% was observed for 10% of the energy layers during repeated measurements.PurposeThis study aims to understand the root cause of this fluctuation by relating the actual delivered spot information in the log file with the charge measurement by the ion chambers.MethodsA dedicated pencil beam dose algorithm was developed, taking into account the log file parameters, to calculate the dose for a single energy layer in a homogeneous water phantom. Three energies, 70.2, 182.7, and 228.7 MeV were used in this study, with the 182.7 MeV energy exhibiting large dose fluctuation. The dose fluctuation was investigated as a function of detector's sizes (pinpoint 3D, Advanced Markus, PTW 34070, and PTW 34089) and water depth (2 , 6, and 20 cm). Twelve ion chambers measurements were performed for each chamber and depth. The comparison of the theoretically predicted integrated dose and the charge measurement served as a validation of the algorithm.ResultsAbout 5.9% and 9.6% dose fluctuation were observed in Advanced Markus and pinpoint 3D measurements at 2 cm depth for 182.7 MeV, while fluctuation of 1.6% and 1.1% were observed in Advanced Markus with 228.7 and 70.2 MeV at similar depth. Fluctuation of less than 0.1% was observed for PTW34070 and PTW 34089 for all energies. The fluctuation was found to diminish with larger spot size at 20 cm depth to 1.3% for 182.7 MeV. The theoretical and measured charge comparison showed a high linear correlation of for all datasets, indicating the fluctuation originated from the delivered spot characteristics. The cause of fluctuation was identified to be due to the spill change occurring close to the detector, and since the spot positional deviation profiles were different between two spills, this resulted in local hot spots between columns of spots. The actual position of spill change varies randomly during measurement, which led to a random occurrence of hot spot within the detector's sensitive volume and a fluctuating dose measurement.ConclusionThis is the first report of a dose fluctuation greater than 5% in absolute dosimetry measurement with a uniform scanned field and the cause of the fluctuation has been conclusively determined. It is important to choose the MU and scanning pattern carefully to avoid spill change happening when the spot delivery is near the detector.

Abstract Background In order to compute the relative biological effectiveness (RBE) of ion radiation therapy with the Mayo Clinic Florida microdosimetric kinetic model (MCF MKM), it is necessary to process entire microdosimetric distributions. Therefore, a posteriori RBE recalculations (i.e., for a different cell line or another biological endpoint) would require whole spectral information. It is currently not practical to compute and store all this data for each clinical voxel. Purpose To develop a methodology that allows to store a limited amount of physical information without losing accuracy in the RBE calculations nor the possibility of a posteriori RBE recalculations. Methods Computer simulations for four monoenergetic 12 C ion beams and a 12 C ion spread‐out Bragg peak (SOBP) were performed to assess lineal energy distributions as a function of the depth within a water phantom. These distributions were used in combination with the MCF MKM to compute the in vitro clonogenic survival RBE for human salivary gland tumor cells (HSG cell line) and human skin fibroblasts (NB1RGB cell line). The RBE values were also calculated with a new abridged microdosimetric distribution methodology (AMDM) and compared with the reference RBE calculations using the entire distributions. Results The maximum relative deviation between the RBE values computed using the entire distributions and the AMDM was 0.61% (monoenergetic beams) and 0.49% (SOBP) for the HSG cell line, while 0.45% (monoenergetic beams) and 0.26% (SOBP) for the NB1RGB cell line. Conclusion The excellent agreement between the RBE values computed using the entire lineal energy distributions and the AMDM represents a milestone for the clinical implementation of the MCF MKM.

AbstractBackgroundDiscrete spot scanning (DSS) is the commonly used method for proton pencil beam scanning (PBS). There is lack of data on the dose‐driven continuous scanning (DDCS).PurposeTo investigate delivery benefits and dosimetric implications of DDCS versus DSS for PBS systems.MethodsThe irradiation duty factor, beam delivery time (BDT), and dose deviation were simulated for eight treatment plans in prostate, head and neck, liver, and lung, with both conventional fractionation and hypofractionation schemes. DDCS results were compared with those of DSS.ResultsThe DDCS irradiation duty factor (range, 11%–41%) was appreciably improved compared to DSS delivery (range, 4%–14%), within which, hypofractionation schemes had greater improvement than conventional fractionation. With decreasing stop ratio constraints, the DDCS BDT reduction was greater, but dose deviation also increased. With stop ratio constraints of 2, 1, 0.5, and 0, DDCS BDT reduction reached to 6%, 10%, 12%, and 15%, respectively, and dose deviation reached to 0.6%, 1.7%, 3.0%, and 5.2% root mean square error in PTV DVH, respectively. The 3%/2‐mm gamma passing rate was greater than 99% with stop ratio constraints of 2 and 1, and greater than 95% with a stop ratio of 0.5. When the stop ratio constraint was removed, five of the eight treatment plans had a 3%/2‐mm gamma passing rate greater than 95%, and the other three plans had a 3%/2‐mm gamma passing rate between 90% and 95%.ConclusionsThe irradiation duty factor was considerably improved with DDCS. Smaller stop ratio constraints led to shorter BDTs, but with the cost of larger dose deviations. Our finding suggested that a stop ratio of 1 constraint seems to yield acceptable DDCS dose deviation.

Surgical treatment of pelvic sarcoma involving the bone is the standard of care but is associated with several sequelae and reduced functional quality of life (QOL). Treatment with photon and proton radiotherapy is associated with relapse. Carbon ion radiotherapy (CIRT) may reduce both relapse rates and treatment sequelae. The PROSPER study is a tricontinental, nonrandomized, prospective, three-arm, pragmatic trial evaluating treatments of pelvic sarcoma involving the bone. Patients aged at least 15 years are eligible for inclusion. Participants must have an Eastern Cooperative Oncology Group Performance Status score of two or less, newly diagnosed disease, and histopathologic confirmation of pelvic chordoma, chondrosarcoma, osteosarcoma, Ewing sarcoma with bone involvement, rhabdomyosarcoma (RMS) with bone involvement, or non-RMS soft tissue sarcoma with bone involvement. Treatment arms include (1) CIRT (n = 30) delivered in Europe and Asia, (2) surgical treatment with or without adjuvant radiotherapy (n = 30), and (3) proton therapy (n = 30). Arms two and three will be conducted at Mayo Clinic campuses in Arizona, Florida, and Minnesota. The primary end point is to compare the 1-year change in functional QOL between CIRT and surgical treatment. Additional comparisons among the three arms will be made between treatment sequelae, local control, and other QOL measures.

AbstractBackgroundRecent advancements in Deep Learning (DL) methodologies have led to state‐of‐the‐art performance in a wide range of applications especially in object recognition, classification, and segmentation of medical images. However, training modern DL models requires a large amount of computation and long training times due to the complex nature of network structures and the large number of training datasets involved. Moreover, it is an intensive, repetitive manual process to select the optimized configuration of hyperparameters for a given DL network.PurposeIn this study, we present a novel approach to accelerate the training time of DL models via the progressive feeding of training datasets based on similarity measures for medical image segmentation. We term this approach Progressive Deep Learning (PDL).MethodsThe two‐stage PDL approach was tested on the auto‐segmentation task for two imaging modalities: CT and MRI. The training datasets were ranked according to similarity measures between each sample based on Mean Square Error (MSE), Peak Signal‐to‐Noise Ratio (PSNR), Structural Similarity Index (SSIM), and the Universal Quality Image Index (UQI) values. At the start of the training process, a relatively coarse sampling of training datasets with higher ranks was used to optimize the hyperparameters of the DL network. Following this, the samples with higher ranks were used in step 1 to yield accelerated loss minimization in early training epochs and the total dataset was added in step 2 for the remainder of training.ResultsOur results demonstrate that the PDL approach can reduce the training time by nearly half (∼49%) and can predict segmentations (CT U‐net/DenseNet dice coefficient: 0.9506/0.9508, MR U‐net/DenseNet dice coefficient: 0.9508/0.9510) without major statistical difference (Wilcoxon signed‐rank test) compared to the conventional DL approach. The total training times with a fixed cutoff at 0.95 DSC for the CT dataset using DenseNet and U‐Net architectures, respectively, were 17 h, 20 min and 4 h, 45 min in the conventional case compared to 8 h, 45 min and 2 h, 20 min with PDL. For the MRI dataset, the total training times using the same architectures were 2 h, 54 min and 52 min in the conventional case and 1 h, 14 min and 25 min with PDL.ConclusionThe proposed PDL training approach offers the ability to substantially reduce the training time for medical image segmentation while maintaining the performance achieved in the conventional case.