Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions.

Background and purpose: Autocontouring for radiotherapy has the potential to significantly save time and reduce interobserver variability. We aimed to assess the performance of a commercial autocontouring model for head and neck (H&N) patients in eight orientations relevant to particle therapy with fixed beam lines, focusing on validation and implementation for routine clinical use. Materials and methods: Autocontouring was performed on sixteen organs at risk (OARs) for 98 adult and pediatric patients with 137 H&N CT scans in eight orientations. A geometric comparison of the autocontours and manual segmentations was performed using the Hausdorff Distance 95th percentile, Dice Similarity Coefficient (DSC) and surface DSC and compared to interobserver variability where available. Additional qualitative scoring and dose-volume-histogram (DVH) parameters analyses were performed for twenty patients in two positions, consisting of scoring on a 0-3 scale based on clinical usability and comparing the mean (Dmean) and near-maximum (D2%) dose, respectively. Results: For the geometric analysis, the model performance in head-first-supine straight and hyperextended orientations was in the same range as the interobserver variability. HD95, DSC and surface DSC was heterogeneous in other orientations. No significant geometric differences were found between pediatric and adult autocontours. The qualitative scoring yielded a median score of ≥ 2 for 13/16 OARs while 7/32 DVH parameters were significantly different. Conclusions: For head-first-supine straight and hyperextended scans, we found that 13/16 OAR autocontours were suited for use in daily clinical practice and subsequently implemented. Further development is needed for other patient orientations before implementation.

Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder-decoder network.

BACKGROUND: Deep learning-based auto-planning is an active research field; however, for some tasks a treatment planning system (TPS) is still required. PURPOSE: To introduce a deep learning-based model generating deliverable DICOM RT treatment plans that can be directly irradiated by a linear accelerator (LINAC). The model was based on an encoder-decoder network and can predict multileaf collimator (MLC) motion sequences for prostate VMAT radiotherapy. METHODS: A total of 619 treatment plans from 460 patients treated for prostate cancer with single-arc VMAT were included in this study. An encoder-decoder network was trained using 465 clinical treatment plans and validated on 77 plans. The performance was analyzed on a separate test set of 77 treatment plans. Separate L1 losses were computed for the leaf and jaw positions as well as the monitor units, with the leaf loss being weighted by a factor of 100 before being added to the other losses. The generated treatment plans were recalculated in a treatment planning system and the dose-volume metrics and gamma passing rates were compared to the original dose. RESULTS: All generated treatment plans showed good agreement with the original data, with an average gamma passing rate (3%/3 mm) of 91.9 ± 7.1%. However, the coverage of the PTVs. was slightly lower for the generated plans (D98%  = 92.9 ± 2.6%) in comparison to the original plans (D98%  = 95.7 ± 2.2%). There was no significant difference in mean dose to the bladder between the predicted and original plan (Dmean of 28.0 ± 13.5 vs. 28.1 ± 13.3% of prescribed dose) or rectum (Dmean of 42.3 ± 7.4 vs. 42.6 ± 7.5%). The maximum dose to bladder was only slightly higher in the predicted plans (D2% of 100.7 ± 5.3 vs. 99.8 ± 4.0%) and for the rectum it was even lower (D2% of 100.5 ± 3.7 vs. 100.1 ± 4.3). CONCLUSIONS: The deep learning-based model could predict MLC motion sequences in prostate VMAT plans, eliminating the need for sequencing inside a TPS, thus revolutionizing autonomous treatment planning workflows. This research completes the loop in deep learning-based treatment planning processes, enabling more efficient workflows for real-time or online adaptive radiotherapy.

Possibilities and challenges when using synthetic computed tomography in an adaptive carbon-ion treatment workflow.

BACKGROUND AND PURPOSE: Anatomical surveillance during ion-beam therapy is the basis for an effective tumor treatment and optimal organ at risk (OAR) sparing. Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) can replace the X-ray based planning CT (X-rayCT) in photon radiotherapy and improve the workflow efficiency without additional imaging dose. The extension to carbon-ion radiotherapy is highly challenging; complex patient positioning, unique anatomical situations, distinct horizontal and vertical beam incidence directions, and limited training data are only few problems. This study gives insight into the possibilities and challenges of using sCTs in carbon-ion therapy. MATERIALS AND METHODS: For head and neck patients immobilised with thermoplastic masks 30 clinically applied actively scanned carbon-ion treatment plans on 15 CTs comprising 60 beams were analyzed. Those treatment plans were re-calculated on MRI based sCTs which were created employing a 3D U-Net. Dose differences and carbon-ion spot displacements between sCT and X-rayCT were evaluated on a patient specific basis. RESULTS: Spot displacement analysis showed a peak displacement by 0.2 cm caused by the immobilisation mask not measurable with the MRI. 95.7% of all spot displacements were located within 1 cm. For the clinical target volume (CTV) the median D50% agreed within -0.2% (-1.3 to 1.4%), while the median D0.01cc differed up to 4.2% (-1.3 to 25.3%) comparing the dose distribution on the X-rayCT and the sCT. OAR deviations depended strongly on the position and the dose gradient. For three patients no deterioration of the OAR parameters was observed. Other patients showed large deteriorations, e.g. for one patient D2% of the chiasm differed by 28.1%. CONCLUSION: The usage of sCTs opens several new questions, concluding that we are not ready yet for an MR-only workflow in carbon-ion therapy, as envisaged in photon therapy. Although omitting the X-rayCT seems unfavourable in the case of carbon-ion therapy, an sCT could be advantageous for monitoring, re-planning, and adaptation.

Efficient full Monte Carlo modelling and multi-energy generative model development of an advanced X-ray device.

Monte Carlo (MC) simulations of X-ray image devices require splitting the simulation into two parts (i.e. the generation of x-rays and the actual imaging). The X-ray production remains unchanged for repeated imaging and can thus be stored in phase space (PhS) files and used for subsequent MC simulations. Especially for medical images these dedicated PhS files require a large amount of data storage, which is partly why Generative Adversarial Networks (GANs) were recently introduced. We enhanced the approach by a conditional GAN to model multiple energies using one network. This study compares the use of PhSs, GANs, and conditional GANs as photon source with measurements. An X-ray -based imaging system (i.e. ImagingRing) was modelled in this study. half-value layers (HVLs), focal spot, and Heel effect were measured for subsequent comparison. MC simulations were performed with GATE-RTion v1.0 considering the geometry and materials of the imaging system with vendor specific schematics. A traditional GAN model as well as the favourable conditional GAN was implemented for PhS generation. Results of the MC simulation were in agreement with the measurements regarding HVL, focal spot, and Heel effect. The conditional GAN performed best with a non-saturated loss function with R1 regularisation and gave similarly results as the traditional GAN approach. GANs proved to be superior to the PhS approach in terms of data storage and calculation overhead. Moreover, a conditional GAN enabled an energy interpolation to separate the network training process from the final required X-ray energies.

Accelerating and improving radiochromic film calibration by utilizing the dose ratio in photon and proton beams.

PURPOSE: Radiochromic films are versatile 2D dosimeters with high-resolution and near tissue equivalence. To assure high precision and accuracy, a time-consuming calibration process is required. To improve the time efficiency, a novel calibration method utilizing the ratio of the same dose profile measured at different monitor units (MUs) is introduced and tested in a proton and photon beam. METHODS: The calibration procedure employs the dose ratio of film measurements of the same relative profile for different absolute dose values. Hence, the ratio of the dose is constant at any point of the profile, but the ratio of the net optical densities is not constant. The key idea of the method is to optimize the calibration function until the ratio of the calculated doses is constant. The proposed method was tested in the dose range between 0.25-12 and 1-6 Gy in a proton and photon beam, respectively. A radial symmetric profile and a rectangular profile were created, both having a central plateau region of about 3 cm diameter and a dose falloff of about 1.5 cm at larger distances. The dose falloff region was used as input for the optimization method and the central plateau region served as dose reference points. Only the plateau region of the highest dose entered the optimization as an additional objective. The measured data were randomly split into differently sized training and test sets. The optimization was repeated 1000 times with random start value initialization using the same start values for the standard and the gradient method. Finally, a proton plan with four dose levels was created, which were separated spatially, to test the possibility of a full calibration within a single measurement. RESULTS: Parameter estimation was possible with as low as one dose ratio used for optimization in both the photon and the proton case, yet exhibiting a high sensitivity on the dose level. The root mean squared deviation (RMSD) of the dose was less than 1% when the dose ratio was in the order of 20, whereas the median RMSD of all optimizations was 1.7%. Using four dose levels for optimization resulted in a median RMSD of 1% when randomly selecting the dose levels. Having at least one dose ratio of about 20 included in the optimization considerably improved the RMSD of the calibration function. Using six or eight dose levels reduced the sensitivity on the dose level selection and the median RMSD was 0.8%. A full calibration was possible in a single measurement having four dose levels in one plan but spatially separated. CONCLUSIONS: The number of measurements required to obtain an EBT3 film calibration function could be reduced using the proposed dose ratio method while maintaining the same accuracy as with the standard method.

Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?

PURPOSE: For image translational tasks, the application of deep learning methods showed that Generative Adversarial Network (GAN) architectures outperform the traditional U-Net networks, when using the same training data size. This study investigates whether this performance boost can also be expected for segmentation tasks with small training dataset size. MATERIALS/METHODS: Two models were trained on varying training dataset sizes ranging from 1-100 patients: a) U-Net and b) U-Net with patch discriminator (conditional GAN). The performance of both models to segment the male pelvis on CT-data was evaluated (Dice similarity coefficient, Hausdorff) with respect to training data size. RESULTS: No significant differences were observed between the U-Net and cGAN when the models were trained with the same training sizes up to 100 patients. The training dataset size had a significant impact on the models' performances, with vast improvements when increasing dataset sizes from 1 to 20 patients. CONCLUSION: When introducing GANs for the segmentation task no significant performance boost was observed in our experiments, even in segmentation models developed on small datasets.

An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy.

A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T1, T2, and contrast enhanced T1 (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128×192×192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3Houndsfield unit (HU) when trained using T1 images, 71.1/186.1HU for T2, and 82.9/236.4HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2cm and for 98% of all spots the difference was less than 1cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. "real-world data").

Reply to comment on 'Lateral response heterogeneity of Bragg peak ionization chambers for narrow-beam photon and proton dosimetry'.

Gomà (2020Phys. Med. Biol.) commented on our paper 'Lateral response heterogeneity of Bragg peak ionization chambers for narrow-beam photon and proton dosimetry' (Kuesset al2017Phys. Med. Biol.629189-206) which describes a method to determine the response pattern of large-area ionization chambers using a collimated x-ray beam. Gomà performed a simple Monte Carlo simulation to investigate the energy transferred by secondary electrons within the detector, deducing that our conclusion, that the chamber has a non-uniform response, is not supported by our results. We appreciate the work performed by Gomà very much and believe that the transport of secondary electrons in the chamber is an important contribution to understand the non-uniformity response of large-area chambers in narrow beams. However, we disagree with the conclusions drawn by Gomà that the radial response is homogenous. His simulation actually demonstrates that the response is non-uniform in an x-ray beam.

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region.

PURPOSE: In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated. METHODS: 41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( C B C T RS ) and a population-based dose calculation method ( C B C T Pop ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs. RESULTS: The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2 mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0  ±  0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0  ±  0.8% and 99.1  ±  0.8% for the C B C T RS and C B C T Pop , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4  ±  1.6% and 99.2  ±  0.6% for C B C T RS and C B C T Pop , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. CONCLUSION: The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the C B C T RS approach in an MR only workflow.

An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR.

OBJECTIVE: Recent developments on synthetically generated CTs (sCT), hybrid MRI linacs and MR-only simulations underlined the clinical feasibility and acceptance of MR guided radiation therapy. However, considering clinical application of open and low field MR with a limited field of view can result in truncation of the patient's anatomy which further affects the MR to sCT conversion. In this study an acquisition protocol and subsequent MR image stitching is proposed to overcome the limited field of view restriction of open MR scanners, for MR-only photon and proton therapy. MATERIAL AND METHODS: 12 prostate cancer patients scanned with an open 0.35T scanner were included. To obtain the full body contour an enhanced imaging protocol including two repeated scans after bilateral table movement was introduced. All required structures (patient contour, target and organ at risk) were delineated on a post-processed combined transversal image set (stitched MRI). The postprocessed MR was converted into a sCT by a pretrained neural network generator. Inversely planned photon and proton plans (VMAT and SFUD) were designed using the sCT and recalculated for rigidly and deformably registered CT images and compared based on D2%, D50%, V70Gy for organs at risk and based on D2%, D50%, D98% for the CTV and PTV. The stitched MRI and the untruncated MRI were compared to the CT, and the maximum surface distance was calculated. The sCT was evaluated with respect to delineation accuracy by comparing on stitched MRI and sCT using the DICE coefficient for femoral bones and the whole body. RESULTS: Maximum surface distance analysis revealed uncertainties in lateral direction of 1-3mm on average. DICE coefficient analysis confirms good performance of the sCT conversion, i.e. 92%, 93%, and 100% were obtained for femoral bone left and right and whole body. Dose comparison resulted in uncertainties below 1% between deformed CT and sCT and below 2% between rigidly registered CT and sCT in the CTV for photon and proton treatment plans. DISCUSSION: A newly developed acquisition protocol for open MR scanners and subsequent Sct generation revealed good acceptance for photon and proton therapy. Moreover, this protocol tackles the restriction of the limited FOVs and expands the capacities towards MR guided proton therapy with horizontal beam lines.

Beam monitor calibration of a synchrotron-based scanned light-ion beam delivery system.

PURPOSE: This paper presents the implementation and comparison of two independent methods of beam monitor calibration in terms of number of particles for scanned proton and carbon ion beams. METHODS: In the first method, called the single-layer method, dose-area-product to water (DAPw) is derived from the absorbed dose to water determined using a Roos-type plane-parallel ionization chamber in single-energy scanned beams. This is considered the reference method for the beam monitor calibration in the clinically relevant proton and carbon energy ranges. In the second method, called the single-spot method, DAPw of a single central spot is determined using a Bragg-peak (BP) type large-area plane-parallel ionization chamber. Emphasis is given to the detailed characterization of the ionization chambers used for the beam monitor calibration. For both methods a detailed uncertainty budget on the DAPw determination is provided as well as on the derivation of the number of particles. RESULTS: Both calibration methods agreed on average within 1.1% for protons and within 2.6% for carbon ions. The uncertainty on DAPw using single-layer beams is 2.1% for protons and 3.1% for carbon ions with major contributions from the available values of kQ and the average spot spacing in both lateral directions. The uncertainty using the single-spot method is 2.2% for protons and 3.2% for carbon ions with major contributions from the available values of kQ and the non-uniformity of the BP chamber response, which can lead to a correction of up-to 3.2%. For the number of particles, an additional dominant uncertainty component for the mean stopping power per incident proton (or the CEMA) needs to be added. CONCLUSION: The agreement between both methods enhances confidence in the beam monitor calibration and the estimated uncertainty. The single-layer method can be used as a reference and the single-spot method is an alternative that, when more accumulated knowledge and data on the method becomes available, can be used as a redundant dose monitor calibration method. This work, together with the overview of information from the literature provided here, is a first step towards comprehensive information on the single-spot method.

Computer-assisted beam modeling for particle therapy.

PURPOSE: To develop a computer-driven and thus less user-dependent method, allowing for a simple and straightforward generation of a Monte Carlo (MC) beam model of a scanned proton and carbon ion beam delivery system. METHODS: In a first step, experimental measurements were performed for proton and carbon ion energies in the available energy ranges. Data included depth dose profiles measured in water and spot sizes in air at various isocenter distances. Using an automated regularization-based optimization process (AUTO-BEAM), GATE/Geant4 beam models of the respective beam lines were generated. These were obtained sequentially by using least square weighting functions with and without regularization, to iteratively tune the beam parameters energy, energy spread, beam sigma, divergence, and emittance until a user-defined agreement was reached. Based on the parameter tuning for a set of energies, a beam model was semi-automatically generated. The resulting beam models were validated for all centers comparing to independent measurements of laterally integrated depth dose curves and spot sizes in air. For one representative center, three-dimensional dose cubes were measured and compared to simulations. The method was applied on one research as well as four different clinical beam lines for proton and carbon ions of three different particle therapy centers using synchrotron or cyclotron accelerator systems: (a) MedAustron ion therapy center, (b) University Proton Therapy Dresden, and (c) Center Antoine Lacassagne Nice. RESULTS: Particle beam ranges in the MC beam models agreed on average within 0.2 mm compared to measurements for all energies and beam lines. Spot sizes in air (full-width at half maximum) at all positions differed by less than 0.4% from the measurements. Dose calculation with the beam model for the clinical beam line at MedAustron agreed better than 1.7% in absolute dose for a representative clinical case treated with protons. For protons, beam model generation, including geometry creation, data conversion, and validation, was possible within three working days. The number of iterations required for the optimization process to converge, was found to be similar for all beam line geometries and particle types. CONCLUSION: The presented method was demonstrated to work independently of the beam optics behavior of the different beam lines, particle types, and geometries. Furthermore, it is suitable for non-expert users and requires only limited user interaction. Beam model validation for different beam lines based on different beam delivery systems, showed good agreement.

RBE variation in prostate carcinoma cells in active scanning proton beams: In-vitro measurements in comparison with phenomenological models.

PURPOSE: In-vitro radiobiological studies are essential for modelling the relative biological effectiveness (RBE) in proton therapy. The purpose of this study was to experimentally determine the RBE values in proton beams along the beam path for human prostate carcinoma cells (Du-145). RBE-dose and RBE-LETd (dose-averaged linear energy transfer) dependencies were investigated and three phenomenological RBE models, i.e. McNamara, Rørvik and Wilkens were benchmarked for this cell line. METHODS: Cells were placed at multiple positions along the beam path, employing an in-house developed solid phantom. The experimental setup reflected the clinical prostate treatment scenario in terms of field size, depth, and required proton energies (127.2-180.1 MeV) and the physical doses from 0.5 to 6 Gy were delivered. The reference irradiation was performed with 200 kV X-ray beams. Respective (α/ß) values were determined using the linear quadratic model and LETd was derived from the treatment planning system at the exact location of cells. RESULTS AND CONCLUSION: Independent of the cell survival level, all experimental RBE values were consistently higher in the target than the generic clinical RBE value of 1.1; with the lowest RBE value of 1.28 obtained at the beginning of the SOBP. A systematic RBE decrease with increasing dose was observed for the investigated dose range. The RBE values from all three applied models were considerably smaller than the experimental values. A clear increase of experimental RBE values with LETd parameter suggests that proton LET must be taken into consideration for this low (α/ß) tissue.

Latent space manipulation for high-resolution medical image synthesis via the StyleGAN.

INTRODUCTION: This paper explores the potential of the StyleGAN model as an high-resolution image generator for synthetic medical images. The possibility to generate sample patient images of different modalities can be helpful for training deep learning algorithms as e.g. a data augmentation technique. METHODS: The StyleGAN model was trained on Computed Tomography (CT) and T2- weighted Magnetic Resonance (MR) images from 100 patients with pelvic malignancies. The resulting model was investigated with regards to three features: Image Modality, Sex, and Longitudinal Slice Position. Further, the style transfer feature of the StyleGAN was used to move images between the modalities. The root-mean-squard error (RMSE) and the Mean Absolute Error (MAE) were used to quantify errors for MR and CT, respectively. RESULTS: We demonstrate how these features can be transformed by manipulating the latent style vectors, and attempt to quantify how the errors change as we move through the latent style space. The best results were achieved by using the style transfer feature of the StyleGAN (58.7 HU MAE for MR to CT and 0.339 RMSE for CT to MR). Slices below and above an initial central slice can be predicted with an error below 75 HU MAE and 0.3 RMSE within 4cm for CT and MR, respectively. DISCUSSION: The StyleGAN is a promising model to use for generating synthetic medical images for MR and CT modalities as well as for 3D volumes.

Characterization of the PTW-34089 type 147 mm diameter large-area ionization chamber for use in light-ion beams.

A newly-designed large-area plane-parallel ionization chamber (of type PTW 34089), denoted BPC150, with a nominal active volume diameter of 147 mm is characterized in this study. Such chambers exhibit benefits compared to smaller chambers in the field of scanned light-ion beam dosimetry because they capture a larger fraction of the laterally-spread beam fragments and ease positioning with respect to small fields. The chamber was characterized in 60Co, 200 kV x-ray, proton and carbon ion beams. The chamber-specific beam-quality correction factor kQ,Q0 was determined. To investigate the homogeneity of the chamber's response, a radial response map was acquired. An edge correction was applied when the proton beam only partly impinged on the chamber's active surface. The measured response map showed that the response in the chamber's center is 3% lower than the average response over the total active area. Furthermore, percentage depth dose (PDD) curves in carbon ions were acquired and compared to those obtained with smaller-diameter chambers (i.e. 81.6 mm and 39.6 mm) as well as with results from Monte Carlo simulations. The measured absorbed dose to water cross calibration coefficients resulted in a kQ,Q0 of 0.981 ± 0.020. Regarding carbon ion PDD curves, relative differences between the BPC150 and smaller chambers were observed, especially for higher energies and in the fragmentation tail. These differences reached 10%-22% in the fragmentation tail (compared to the 81.6 mm diameter chamber). Differences increased when comparing to a chamber with 39.6 mm diameter. The provided results characterize the BPC150 thoroughly for usage in scanned light-ion beam dosimetry and demonstrate its advantage of capturing a larger fraction of the laterally-integrated dose in the fragmentation tail.

Investigating the impact of alpha/beta and LETd on relative biological effectiveness in scanned proton beams: An in vitro study based on human cell lines.

PURPOSE: A relative biological effectiveness (RBE) of 1.1 is commonly used in clinical proton therapy, irrespective of tissue type and depth. This in vitro study was conducted to quantify the RBE of scanned protons as a function of the dose-averaged linear energy transfer (LETd ) and the sensitivity factor (α/ß)X . Additionally, three phenomenological models (McNamara, Rørvik, and Jones) and one mechanistic model (repair-misrepair-fixation, RMF) were applied to the experimentally derived data. METHODS: Four human cell lines (FaDu, HaCat, Du145, SKMel) with differential (α/ß)X ratios were irradiated in a custom-designed irradiation setup with doses between 0 and 6 Gy at proximal, central, and distal positions of a 80 mm spread-out Bragg peak (SOBP) centered at 80 mm (setup A: proton energies 66.5-135.6 MeV) and 155 mm (setup B: proton energies 127.2-185.9 MeV) depth, respectively. LETd values at the respective cell positions were derived from Monte Carlo simulations performed with the treatment planning system (TPS, RayStation). Dosimetric measurements were conducted to verify dose homogeneity and dose delivery accuracy. RBE values were derived for doses that resulted in 90 % (RBE90 ) and 10 % (RBE10 ) of cell survival, and survival after a 0.5 Gy dose (RBE0.5Gy ), 2 Gy dose (RBE2Gy ), and 6 Gy dose (RBE6Gy ). RESULTS: LETd values at sample positions were 1.9, 2.1, 2.5, 2.8, 4.1, and 4.5 keV/µm. For the cell lines with high (α/ß)X ratios (FaDu, HaCat), the LETd did not impact on the RBE. For low (α/ß)X cell lines (Du145, SKMel), LQ-derived survival curves indicated a clear correlation of LETd and RBE. RBE90 values up to 2.9 and RBE10 values between 1.4 and 1.8 were obtained. Model-derived RBE predictions slightly overestimated the RBE for the high (α/ß)X cell lines, although all models except the Jones model provided RBE values within the experimental uncertainty. For low (α/ß)X cell lines, no agreement was found between experiments and model predictions, that is, all models underestimated the measured RBE. CONCLUSIONS: The sensitivity parameter (α/ß)X was observed to be a major influencing factor for the RBE of protons and its sensitivity toward LETd changes. RBE prediction models are applicable for high (α/ß)X cell lines but do not estimate RBE values with sufficient accuracy in low (α/ß)X cell lines.

Investigating conditional GAN performance with different generator architectures, an ensemble model, and different MR scanners for MR-sCT conversion.

Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1.5T(7) and 3T(12), and 10 patients from the 0.35T scanner. The sCTs were dosimetrically compared using clinical VMAT plans for all test patients. For the same scanner (0.35T), the results from the different models were comparable on the test set, with only minor differences in the mean absolute error (MAE) (35-51HU body). Similar results were obtained for conversions of 3T GE Signa and the 3T GE Discovery images (40-62HU MAE) for three of the models. However, larger differences were observed for the 1.5T images (48-65HU MAE). The overall best model was found to be the ensemble model. All dose differences were below 1%. This study shows that it is possible to generalize models trained on images of one scanner to other scanners and different field strengths. The best metric results were achieved by the combination of all networks.

Translational Aspects of Nuclear Factor-Kappa B and Its Modulation by Thalidomide on Early and Late Radiation Sequelae in Urinary Bladder Dysfunction.

PURPOSE: This preclinical study aimed to investigate the role of nuclear factor (NF)-κB in early and late radiogenic sequelae of urinary bladder dysfunction in mice. Thalidomide was applied either during the early or late response phase to determine potential effects of NF-κB inhibition on functional bladder impairment. METHODS AND MATERIALS: After pelvic irradiation on day 0, female C3H/Neu mice were observed over a period of 360 days and radiation response was evaluated for alterations in bladder functionality and NF-κB activation. Functionality was determined in graded dose experiments (14-24 Gy) and assessed by micturition frequency analysis and transurethral cystotonometry to reveal alterations in voiding and volume. The induction of the NF-κB proteins p50 and p65 was evaluated by immunohistochemistry in response to a single dose of 23 Gy (ED90). Thalidomide (100 mg/kg/d) was applied intraperitoneally in 3 treatment groups: daily from day 1 to 15, daily from day 16 to 30, and in 2-day-intervals from day 150 to 180. RESULTS: Immunohistochemical analysis showed a biphasic activation of p50 and p65 during the early radiation cystitis phase (day 1-30). After a transient decrease, p50, but not p65, was reactivated permanently leading to increased levels, which suggests an occurrence of chronic inflammation correlated with functional impairment. Both early thalidomide treatments reduced NF-κB activation and shifted the ED50 value for early radiation cystitis and late radiation sequelae to higher doses. CONCLUSIONS: These data clearly demonstrate the involvement of NF-κB signaling in the pathogenesis of radiation-induced urinary bladder dysfunction. Additionally, this study emphasizes that biological targeting of early radiogenic processes has enormous effect on chronic symptoms. The late administration of thalidomide showed no significant effect on functionality.

Phantom design and dosimetric characterization for multiple simultaneous cell irradiations with active pencil beam scanning.

A new phantom was designed for in vitro studies on cell lines in horizontal particle beams. The phantom enables simultaneous irradiation at multiple positions along the beam path. The main purpose of this study was the detailed dosimetric characterization of the phantom which consists of various heterogeneous structures. The dosimetric measurements described here were performed under non-reference conditions. The experiment involved a CT scan of the phantom, dose calculations performed with the treatment planning system (TPS) RayStation employing both the Pencil Beam (PB) and Monte Carlo (MC) algorithms, and proton beam delivery. Two treatment plans reflecting the typical target location for head and neck cancer and prostate cancer treatment were created. Absorbed dose to water and dose homogeneity were experimentally assessed within the phantom along the Bragg curve with ionization chambers (ICs) and EBT3 films. LETd distributions were obtained from the TPS. Measured depth dose distributions were in good agreement with the Monte Carlo-based TPS data. Absorbed dose calculated with the PB algorithm was 4% higher than the absorbed dose measured with ICs at the deepest measurement point along the spread-out Bragg peak. Results of experiments using melanoma (SKMel) cell line are also presented. The study suggested a pronounced correlation between the relative biological effectiveness (RBE) and LETd, where higher LETd leads to elevated cell death and cell inactivation. Obtained RBE values ranged from 1.4 to 1.8 at the survival level of 10% (RBE10). It is concluded that dosimetric characterization of a phantom before its use for RBE experiments is essential, since a high dosimetric accuracy contributes to reliable RBE data and allows for a clearer differentiation between physical and biological uncertainties.

Reply to Comment on 'Lateral response heterogeneity of Bragg peak ionization chambers for narrow-beam photon and proton dosimetry'.

Shen (2019) commented on our paper 'Lateral response heterogeneity of Bragg peak ionization chambers for narrow-beam photon and proton dosimetry' regarding the impact of the low dose tail of the collimated x-ray beam we used to acquire individual response maps of large area ionization chambers. The behavior of this low dose tail was measured and compared to the simulations performed by Shen (2019). It was shown that the model of the tail by Shen (2019) is too simplistic and overestimates its effect.