Chronic signaling via the metabolic checkpoint kinase mTORC1 induces macrophage granuloma formation and marks sarcoidosis progression.

The aggregation of hypertrophic macrophages constitutes the basis of all granulomatous diseases, such as tuberculosis or sarcoidosis, and is decisive for disease pathogenesis. However, macrophage-intrinsic pathways driving granuloma initiation and maintenance remain elusive. We found that activation of the metabolic checkpoint kinase mTORC1 in macrophages by deletion of the gene encoding tuberous sclerosis 2 (Tsc2) was sufficient to induce hypertrophy and proliferation, resulting in excessive granuloma formation in vivo. TSC2-deficient macrophages formed mTORC1-dependent granulomatous structures in vitro and showed constitutive proliferation that was mediated by the neo-expression of cyclin-dependent kinase 4 (CDK4). Moreover, mTORC1 promoted metabolic reprogramming via CDK4 toward increased glycolysis while simultaneously inhibiting NF-κB signaling and apoptosis. Inhibition of mTORC1 induced apoptosis and completely resolved granulomas in myeloid TSC2-deficient mice. In human sarcoidosis patients, mTORC1 activation, macrophage proliferation and glycolysis were identified as hallmarks that correlated with clinical disease progression. Collectively, TSC2 maintains macrophage quiescence and prevents mTORC1-dependent granulomatous disease with clinical implications for sarcoidosis.

An evaluation of the effect of bortezomib on radiation-induced urinary bladder dysfunction.

PURPOSE: The urinary bladder is one major organ at risk in radiotherapy of pelvic malignancies. The radiation response manifests in early and chronic changes in bladder function. These are based on inflammatory effects and changes in urothelial cell function and proliferation. This study evaluates the effect of bortezomib as an anti-proliferative and anti-inflammatory compound in an established mouse bladder model. The early radiation-induced bladder dysfunction in the mouse occurs in two phases during the first month after irradiation (phase I: day 0-15, phase II: days 16-30). MATERIALS AND METHODS: Daily bortezomib injections (0.02 mg/ml, subcutaneously) were administered between days 0-15 or 15-30 in separate groups. Single graded radiation doses were administered in five dose groups. Cystometry was carried out before (individual control) and during the first month after irradiation. When bladder capacity was decreased by ≥50%, mice were considered as responders. Statistical analysis was performed by the SPSS software version 24. RESULTS: Daily bortezomib injections between days 0-15 resulted in a significant decrease in responders for phase I. There was no significant effect with daily bortezomib injections between days 16-30. CONCLUSION: Two separate waves of acute radiation-induced urinary bladder dysfunction have distinct mechanisms that need further biological studies.

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.

Protective effects of systemic dermatan sulfate treatment in a preclinical model of radiation-induced oral mucositis.

PURPOSE: Oral mucositis is a frequent, dose-limiting side effect of radio(chemo)therapy of head-and-neck malignancies. The epithelial radiation response is based on multiple tissue changes, which could offer targets for a biologically tailored treatment. The potential of dermatan sulfate (DS) to modulate radiation-induced oral mucositis was tested in an established preclinical mucositis model. METHODS: Irradiation was either applied alone or in combination with daily DS treatment (4 mg/kg, subcutaneously) over varying time intervals. Irradiation comprised single dose irradiation with graded doses to the lower tongue surface or daily fractionated irradiation of the whole tongue. Fractionation protocols (5â€¯× 3 Gy/week) over one (days 0-4) or two weeks (days 0-4, 7-11) were terminated by an additional local single dose irradiation to a defined treatment field on the lower tongue surface to induce the mucosal radiation response. The additional single dose irradiation (top-up) on day 7 (after one week of fractionation) or day 14 (after 2 weeks of fractionation) comprised graded doses in order to generate full dose-effect curves. Ulceration of the epithelium of the lower tongue, corresponding to confluent mucositis, was analysed as clinically relevant endpoint. Additionally, the time course parameters, latent time and ulcer duration were analysed. RESULTS: DS treatment significantly reduced the incidence of ulcerations. DS application over longer time intervals resulted in a more pronounced reduction of ulcer frequency, increased latent times and reduced ulcer duration. CONCLUSION: DS has a significant mucositis-ameliorating activity with pronounced effects on mucositis frequency as well as on time course parameters.

Systemic administration of heparin ameliorates radiation-induced oral mucositis-preclinical studies in mice.

PURPOSE: The present study investigates the impact of systemic application of heparins on the manifestation of radiation-induced oral mucositis in a well-established mouse model. MATERIALS AND METHODS: Male C3H/Neu mice were irradiated with either single-dose or fractionated irradiation protocols with 5â€¯× 3 Gy/week, given over one (days 0-4) or two (days 0-4, 7-11) weeks. All fractionation protocols were concluded by a local test irradiation (day 7/14) using graded doses to generate complete dose-effect curves. Daily doses of unfractionated or low molecular weight heparin (40 or 200 I.U./mouse, respectively) were applied subcutaneously over varying time intervals. The incidence and the time course of mucosal ulceration, corresponding to confluent mucositis in patients (RTOG/EORTC grade 3), were analysed as clinically relevant endpoints. RESULTS: Systemic application of heparins significantly increased the iso-effective doses for the induction of mucosal ulceration, particularly in combination with fractionated irradiation protocols. Moreover, a tentative prolongation of the latent time and a pronounced reduction of the ulcer duration were observed. CONCLUSION: These data provide the first evidence for a protective and/or mitigative effect of heparins for radiation-induced oral mucositis. Further studies are ongoing investigating the underlying mechanism.

Upregulated epithelial junction expression represents a novel parameter of the epithelial radiation response to fractionated irradiation in oral mucosa.

PURPOSE: During head and neck cancer treatment, the radiation response of the oral mucosa represents a frequent early side effect. Besides radiation-induced inhibition of proliferation, various other cellular responses occur. The radiation response of adherens and tight junction proteins was so far mostly investigated with large single-dose irradiation protocols, in vivo and in vitro. Therefore, the current study was initiated to investigate the impact of daily fractionated irradiation on the expression of adherens and tight junction proteins in vivo. MATERIALS AND METHODS: Fractionation with 5â€¯× 3 Gy/week (days 0-4, 7-11) was given to the snouts of mice. Groups of 5 animals per day were euthanized every second day between day 0 (unirradiated controls) and day 14, and their tongues subjected to histological processing. Adherens junction marker (ß-catenin and E­cadherin) and tight junction marker (claudin-1 and occludin) expression was analysed in the oral mucosa of unirradiated controls and during two weeks of fractionated irradiation. RESULTS: Adherens as well as tight junction marker proteins were rapidly and consistently upregulated in both the germinal as well as the functional layer of the oral mucosa. This represents a previously unknown parameter of the epithelial radiation response to clinically relevant fractionation protocols. CONCLUSION: Fractionated irradiation significantly enhanced the expression of all proteins investigated. This study revealed a new parameter of the epithelial radiation response to fractionated irradiation.

Radioprotective Effects of Dermatan Sulfate in a Preclinical Model of Oral Mucositis-Targeting Inflammation, Hypoxia and Junction Proteins without Stimulating Proliferation.

Oral mucositis is the most frequently occurring early side effect of head-and-neck cancer radiotherapy. Systemic dermatan sulfate (DS) treatment revealed a significant radioprotective potential in a preclinical model of oral mucositis. This study was initiated to elucidate the mechanistic effects of DS in the same model. Irradiation comprised daily fractionated irradiation (5 × 3 Gy/week) over two weeks, either alone (IR) or in combination with daily dermatan sulfate treatment of 4 mg/kg (IR + DS). Groups of mice (n = 5) were sacrificed every second day over the course of 14 days in both experimental arms, their tongues excised and evaluated. The response to irradiation with and without DS was analyzed on a morphological (cell numbers, epithelial thickness) as well as on a functional (proliferation and expression of inflammation, hypoxia and epithelial junction markers) level. The mucoprotective activity of DS can be attributed to a combination of various effects, comprising increased expression of epithelial junctions, reduced inflammation and reduced hypoxia. No DS-mediated effect on proliferation was observed. DS demonstrated a significant mucositis-ameliorating activity and could provide a promising strategy for mucositis treatment, based on targeting specific, radiation-induced, mucositis-associated signaling without stimulating proliferation.

Modulation of radiation-induced oral mucositis by thalidomide : Preclinical studies.

PURPOSE: Oral mucositis is a common, dose-limiting early side effect of radio(chemo)therapy for head-and-neck tumors. The epithelial radiation response is accompanied by changes in the inflammatory signaling cascades mediated by the transcription factor nuclear factor-kappa B (NF-κB). The present study was initiated to determine the effect of the NF-κB inhibitor thalidomide on the clinical manifestation of oral mucositis in the established mouse tongue model. MATERIALS AND METHODS: Treatment protocols comprised single dose irradiation and daily fractionated irradiation (5  fractions of 3 Gy/week) over 1 (days 0-4) or 2 weeks (days 0-4, 7-11), alone or in combination with daily thalidomide application (100 mg/kg intraperitoneally) over varying time intervals. Fractionation protocols were terminated by graded local radiation doses (day 7/14) to generate full dose-effect curves. Tongue epithelial ulcerations, corresponding to confluent mucositis, served as the clinically relevant endpoint. RESULTS: Thalidomide application did not show a significant radioprotective potential when administered in combination with single dose irradiation. Thalidomide in combination with one week of fractionated irradiation significantly increased the isoeffective top-up doses. Similar results were observed during two weeks of fractionated irradiation in all but one experiment. CONCLUSION: Thalidomide treatment demonstrated a significant mucositis-ameliorating effect during fractionated irradiation, which is likely to result from NF-κB inhibition. However, further mechanistic studies are required to define the underlying mechanisms of the observed mucoprotective effect.

ART for head and neck patients: On the difference between VMAT and IMPT.

UNLABELLED: Anatomical changes in the head-and-neck (H&N) region during the course of treatment can cause deteriorated dose distributions. Different replanning strategies were investigated for volumetric modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT). MATERIAL AND METHODS: For six H&N patients two repeated computed tomography (CT) and magnetic resonance (MR) (CT1/MR1 at week 2 and CT2/MR2 at week 4) scans were acquired additionally to the initial planning CT/MR. Organs-at-risk (OARs) and three targets (CTV70Gy, CTV63Gy, CTV56Gy) were delineated on MRs and transferred to respective CT data set. Simultaneously integrated boost plans were created using VMAT (two arcs) and IMPT (four beams). To assess the need of replanning the initial VMAT and IMPT plans were recalculated on repeated CTs. Furthermore, VMAT and IMPT plans were replanned on the repeated CTs. A Demon algorithm was used for deformable registration of the repeated CTs with the initial CT and utilized for dose accumulation. Total dose estimations were performed to compare ART versus standard treatment strategies. RESULTS: Dosimetric evaluation of recalculated plans on CT1 and CT2 showed increasing OAR doses for both, VMAT and IMPT. The target coverage of recalculated VMAT plans was considered acceptable in three cases, while for all IMPT plans it dropped. Adaptation of the treatment reduced D2% for brainstem by 6.7 Gy for VMAT and by 8 Gy for IMPT, for particular patients. These D2% reductions were reaching 9 Gy and 14 Gy for the spinal cord. ART improved target dose homogeneity, especially for protons, i.e. D2% decreased by up to 8 Gy while D98% increased by 1.2 Gy. CONCLUSION: ART showed benefits for both modalities. However, as IMPT is more conformal, the magnitude of dosimetric changes was more pronounced compared to VMAT. Large anatomic variations had a severe impact on treatment plan quality for both VMAT and IMPT. ART is justified in those cases irrespective of treatment modalities.

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.

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.

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.

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.

Using statistical measures for automated comparison of in-beam PET data.

PURPOSE: Positron emission tomography (PET) is considered to be the state of the art technique to monitor particle therapy in vivo. To evaluate the beam delivery the measured PET image is compared to a predicted ß(+)-distribution. Nowadays the range assessment is performed by a group of experts via visual inspection. This procedure is rather time consuming and requires well trained personnel. In this study an approach is presented to support human decisions in an automated and objective way. METHODS: The automated comparison presented uses statistical measures, namely, Pearson's correlation coefficient (PCC), to detect ion beam range deviations. The study is based on 12 in-beam PET patient data sets recorded at GSI and 70 artificial beam range modifications per data set. The range modifications were 0, 4, 6, and 10 mm water equivalent path length (WEPL) in positive and negative beam directions. The reference image to calculate the PCC was both an unmodified simulation of the activity distribution (Test 1) and a measured in-beam PET image (Test 2). Based on the PCCs sensitivity and specificity were calculated. Additionally the difference between modified and unmodified data sets was investigated using the Wilcoxon rank sum test. RESULTS: In Test 1 a sensitivity and specificity over 90% was reached for detecting modifications of ±10 and ±6 mm WEPL. Regarding Test 2 a sensitivity and specificity above 80% was obtained for modifications of ±10 and -6 mm WEPL. The limitation of the method was around 4 mm WEPL. CONCLUSIONS: The results demonstrate that the automated comparison using PCC provides similar results in terms of sensitivity and specificity compared to visual inspections of in-beam PET data. Hence the method presented in this study is a promising and effective approach to improve the efficiency in the clinical workflow in terms of particle therapy monitoring by means of PET.

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").

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.

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.