Evaluation of a novel CBCT conversion method implemented in a treatment planning system.

BACKGROUND: To evaluate a novel CBCT conversion algorithm for dose calculation implemented in a research version of a treatment planning system (TPS). METHODS: The algorithm was implemented in a research version of RayStation (v. 11B-DTK, RaySearch, Stockholm, Sweden). CBCTs acquired for each ten head and neck (HN), gynecology (GYN) and lung cancer (LNG) patients were collected and converted using the new algorithm (CBCTc). A bulk density overriding technique implemented in the same version of the TPS was used for comparison (CBCTb). A deformed CT (dCT) was created by using deformable image registration of the planning CT (pCT) to the CBCT to reduce anatomical changes. All treatment plans were recalculated on the pCT, dCT, CBCTc and the CBCTb. The resulting dose distributions were analyzed using the MICE toolkit (NONPIMedical AB Sweden, Umeå) with local gamma analysis, with 1% dose difference and 1 mm distance to agreement criteria. A Wilcoxon paired rank sum test was applied to test the differences in gamma pass rates (GPRs). A p value smaller than 0.05 considered statistically significant. RESULTS: The GPRs for the CBCTb method were systematically lower compared to the CBCTc method. Using the 10% dose threshold and the dCT as reference the median GPRs were for the CBCTc method were 100% and 99.8% for the HN and GYN cases, respectively. Compared to that the GPRs of the CBCTb method were lower with values of 99.8% and 98.0%, for the HN and GYN cases, respectively. The GPRs of the LNG cases were 99.9% and 97.5% for the CBCTc and CBCTb method, respectively. These differences were statistically significant. The main differences between the dose calculated on the CBCTs and the pCTs were found in regions near air/tissue interfaces, which are also subject to anatomical variations. CONCLUSION: The dose distribution calculated using the new CBCTc method showed excellent agreement with the dose calculated using dCT and pCT and was superior to the CBCTb method. The main reasons for deviations of the calculated dose distribution were caused by anatomical variations between the pCT and the corrected CBCT.

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.

Left Ventricular, Left Atrial and Right Ventricular Strain Modifications after Maximal Exercise in Elite Ski-Mountaineering Athletes: A Feasibility Speckle Tracking Study.

Eleven world elite ski-mountaineering (Ski-Mo) athletes were evaluated for pronounced echocardiographic physiological remodeling as the primary aim of our feasibility speckle tracking study. In this context, sports-related cardiac remodeling was analyzed by performing two-dimensional echocardiography, including speckle tracking analysis of the left atrium (LA), right ventricle (RV) and left ventricular (LV) global longitudinal strain (LV-GLS) at rest and post-peak performance. The feasibility echocardiographic speckle tracking analysis was performed on eleven elite Ski-Mo athletes, which were obtained in 2022 during the annual medical examination. The obtained data of the professional Ski-Mo athletes (11 athletes, age: 18-26 years) were compared for different echocardiographic parameters at rest and post-exercise. Significant differences were found for LV-GLS mean (p = 0.0036) and phasic LA conduit strain pattern at rest and post-exercise (p = 0.0033). Furthermore, negative correlation between LV mass and LV-GLS (p = 0.0195, r = -0.69) and LV mass Index and LV-GLS (p = 0.0253, r = -0.66) at rest were elucidated. This descriptive reporting provided, for the first time, a sport-specific dynamic remodeling of an entire elite national team of the Ski-Mo athlete's left heart and elucidated differences in the dynamic deformation pattern of the left heart.

Physiological Effects of Training in Elite German Winter Sport Athletes: Sport Specific Remodeling Determined Using Echocardiographic Data and CPET Performance Parameters.

Nine ski mountaineering (Ski-Mo), ten Nordic-cross country (NCC), and twelve world elite biathlon (Bia) athletes were evaluated for cardiopulmonary exercise test (CPET) performance and pronounced echocardiographic physiological cardiac remodeling as a primary aim of our descriptive preliminary report. In this context, a multicenter retrospective analysis of two-dimensional echocardiographic data including speckle tracking of the left ventricle (LV-GLS) and CPET performance analysis was performed in 31 elite world winter sports athletes, which were obtained during the annual sports medicine examination between 2020 and 2021. The matched data of the elite winter sports athletes (14 women, 17 male athletes, age: 18-32 years) were compared for different CPET and echocardiographic parameters, anthropometric data, and sport-specific training schedules. Significant differences could be revealed for left atrial (LA) remodeling by LA volume index (LAVI, p = 0.0052), LV-GLS (p = 0.0003), and LV mass index (LV Mass index, p = 0.0078) between the participating disciplines. All participating athletes showed excellent performance data in the CPET analyses, whereby significant differences were revealed for highest maximum respiratory minute volume (VE maximum) and the maximum oxygen pulse level across the participating athletes. This study on sport specific physiological demands in elite winter sport athletes provides new evidence that significant differences in CPET and cardiac remodeling of the left heart can be identified based on the individual athlete's training schedule, frequency, and physique.

Physiological Aspects of World Elite Competitive German Winter Sport Athletes.

Nine Ski mountaineering (Ski-Mo), ten Nordic-Cross Country (NCC) and twelve world elite biathlon (Bia) athletes were evaluated for cardiopulmonary exercise test (CPET) performance as the primary aim of our descriptive preliminary report. A multicenter retrospective analysis of CPET data was performed in 31 elite winter sports athletes, which were obtained in 2021 during the annual medical examination. The matched data of the elite winter sports athletes (14 women, 17 male athletes, age: 18-32 years) were compared for different CPET parameters, and athlete's physique data and sport-specific training schedules. All athletes showed, as estimated in elite winter sport athletes, excellent performance data in the CPET analyses. Significant differences were revealed for VE VT2 (respiratory minute volume at the second ventilatory threshold (VT2)), highest maximum respiratory minute volume (VEmaximum), the indexed ventilatory oxygen uptake (VO2) at VT2 (VO2/kg VT2), the oxygen pulse at VT2, and the maximum oxygen pulse level between the three professional winter sports disciplines. This report provides new evidence that in different world elite winter sport professionals, significant differences in CPET parameters can be demonstrated, against the background of athlete's physique as well as training control and frequency.

Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer.

PURPOSE: In image-guided adaptive brachytherapy (IGABT) a quantitative evaluation of the dosimetric changes between fractions due to anatomical variations, can be implemented via rigid registration of images from subsequent fractions based on the applicator as a reference structure. With available treatment planning systems (TPS), this is a manual and time-consuming process. The aim of this retrospective study was to automate this process. A neural network (NN) was trained to predict the applicator structure from MR images. The resulting segmentation was used to automatically register MR-volumes. MATERIAL AND METHODS: DICOM images and plans of 56 patients treated for cervical cancer with high dose-rate (HDR) brachytherapy were used in the study. A 2D and a 3D NN were trained to segment applicator structures on clinical T2-weighted MRI datasets. Different rigid registration algorithms were investigated and compared. To evaluate a fully automatic registration workflow, the NN-predicted applicator segmentations (AS) were used for rigid image registration with the best performing algorithm. The DICE coefficient and mean distance error between dwell positions (MDE) were used to evaluate segmentation and registration performance. RESULTS: The mean DICE coefficient for the predicted AS was 0.70 ±â€¯0.07 and 0.58 ±â€¯0.04 for the 3D NN and 2D NN, respectively. Registration algorithms achieved MDE errors from 8.1 ±â€¯3.7 mm (worst) to 0.7 ±â€¯0.5 mm (best), using ground-truth AS. Using the predicted AS from the 3D NN together with the best registration algorithm, an MDE of 2.7 ±â€¯1.4 mm was achieved. CONCLUSION: Using a combination of deep learning models and state of the art image registration techniques has been demonstrated to be a promising solution for automatic image registration in IGABT. In combination with auto-contouring of organs at risk, the auto-registration workflow from this study could become part of an online-dosimetric interfraction evaluation workflow in the future.

An Automated Room Temperature Flip-Chip Mounting Process for Hybrid Printed Electronics.

Printing technology and mounting technology enable the novel field of hybrid printed electronics. To establish a hybrid printed system, one challenge is that the applied mounting process meets the requirements of functional inks and substrates. One of the most common requirements is low process temperature. Many functional inks and substrates cannot withstand the high temperatures required by traditional mounting processes. In this work, a standardized interconnection and an automated bump-less flip-chip mounting process using a room temperature curing conductive adhesive are realised. With the proposed process, the conductive adhesive selected for the standardized interconnection can be dispensed uniformly, despite its increase of viscosity already during pot time. Electrical and mechanical performance of the interconnection are characterized by four terminal resistance measurement and shear test. The herein proposed automated process allows for fabrication of hybrid printed devices in larger batch sizes than manual assembly processes used beforehand and thus, more comprehensive evaluation of device parameters. This is successfully demonstrated in a first application, a novel hybrid printed security device. The room temperature mounting process eliminates any potentially damaging thermal influence on the performance of the printed circuits that might result from other assembly techniques like soldering.

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").

Removable partial dentures retained by hybrid CAD/CAM cobalt-chrome double crowns: 1-year results from a prospective clinical study: CAD/CAM cobalt-chrome double crowns: 1-year results.

OBJECTIVES: Computer-aided design and manufacturing (CAD/CAM) has been successfully used to replace conventional steps in the fabrication of double crowns, creating hybrid-workflows that might facilitate the wider application of these restorations in the future. However, in-vivo data are still lacking. METHODS: A prospective clinical trial was designed in which 20 patients (median age = 69 years; n women = 10) with 73 abutment teeth who needed a double-crown-retained removable partial denture (RPD) were consecutively recruited. While most of the work steps were done conventionally, gypsum models were digitized with a laboratory scanner to allow CAD/CAM fabrication of primary crowns and secondary structures. DentalDesigner software (3Shape) was used in combination with milling unit PrograMillPM7 and Co-Cr- blanks (Ivoclar-vivadent). Connectors were milled from wax, transferred to Co-Cr using lost-wax technique and bonded to the secondary crowns. Clinical follow-ups were scheduled 6 and 12 months after prosthesis insertion. Outcome parameters were complication-free survival of RPDs and abutment teeth after one year. RESULTS: After 12 months, complication-free survival was 74% and 91% for the RPDs and abutment teeth, respectively. Complications comprised decementations (n = 5), abutment tooth fractures (n = 2), fracture of denture teeth (n = 1), and loss of abutment teeth (n = 1). These complications were easily manageable, resulting in 1-year survival of 100% for CAD/CAM RPDs. CONCLUSIONS: First data on short-term complication rates of CAD/CAM double-crown-retained RPDs appear promising. To gather further evidence, prospective clinical trials over a longer follow-up time and with larger patient groups are required. CLINICAL SIGNIFICANCE: Hybrid CAD/CAM double-crown retained RPDs showed a successful clinical application after one year of follow-up. Further research is needed to evaluate their performance in comparison to conventional manufacturing methods.

Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning.

PURPOSE: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the necessity of costly advanced generative adversarial network (GAN) techniques. METHODS: The model was developed based on the OpenKBP challenge dataset, consisting of 200 and 40 head-and-neck patients for training and validation, respectively. The final model is a U-Net with additional ResNet blocks between up- and down convolutions. The results were obtained by training the model with AdamW with the One Cycle scheduler. The loss function is a combination of the L1 loss with a feature loss, which uses a pretrained video classifier as a feature extractor. The performance was evaluated on another 100 patients in the OpenKBP test dataset. The DVH metrics of the test data were evaluated, where D 0.1 c c , and D mean were calculated for the organs at risk (OARs) and D 1 % , D 95 % , and D 99 % were computed for the target structures. DVH metric differences between predicted and true dose are reported in percentage. RESULTS: The model achieved 2nd and 4th place in the DVH and dose stream of the OpenKBP challenge, respectively. The dose and DVH score were 2.62 ± 1.10 and 1.52 ± 1.06, respectively. Mean dose differences for the different structures and DVH parameters were within ±1%. CONCLUSION: This straightforward approach produced excellent results. It incorporated One Cycle Learning, ResNet, and feature-based losses, which are common computer vision techniques.

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.

Athlete's Heart in Elite Biathlon, Nordic Cross-Country and Ski-Mountaineering Athletes: Cardiac Adaptions Determined Using Echocardiographic Data.

Twelve world elite Biathlon (Bia), ten Nordic Cross Country (NCC) and ten ski-mountaineering (Ski-Mo) athletes were evaluated for pronounced echocardiographic physiological cardiac remodeling as a primary aim of our descriptive preliminary report. In this context, sports-related cardiac remodeling was analyzed by performing two-dimensional echocardiography including speckle tracking analysis as left ventricular global longitudinal strain (LV-GLS). A multicenter retrospective analysis of echocardiographic data was performed in 32 elite world winter sports athletes, which were obtained between 2020 and 2021 during the annual medical examination. The matched data of the elite world winter sports athletes (14 women, 18 male athletes, age: 18-35 years) were compared for different echocardiographic parameters. Significant differences could be revealed for left ventricular systolic function (LV-EF, p = 0.0001), left ventricular mass index (LV Mass index, p = 0.0078), left atrial remodeling by left atrial volume index (LAVI, p = 0.0052), and LV-GLS (p = 0.0003) between the three professional winter sports disciplines. This report provides new evidence that resting measures of cardiac structure and function in elite winter sport professionals can identify sport specific remodeling of the left heart, against the background of training schedule and training frequency.

The natural course of urinalysis after urinary diversion.

OBJECTIVE: To evaluate the impact of urinary diversion on regular features of urinalysis and to screen for risk factors of infection-related complications. METHODS: We conducted a retrospective, single-centre study of 429 patients who underwent open radical cystectomy. Patients were followed for 12 months and data of the complete urinalyses were analysed at three pre-defined time points. RESULTS: Two weeks after surgery, dipstick testing with positive reactions for leukocyte esterase and haemoglobin were confirmed in 80.7% and 80% after ileal conduit (IC) and orthotopic ileal neobladder (NB), respectively. Every patient was positive for these parameters 12 months after surgery. Correspondingly, the microscopic examination detected leukocytes (84% vs. 85.4%), erythrocytes (82.8% vs. 83.8%) and bacteria (94.3% vs. 96.8%) following IC and NB reconstruction. After 12 months, all parameters were positive irrespective of the type of urinary diversion. Two weeks after surgery positive urine cultures were obtained in more than 50% of cases after IC (52.5%) and NB (60.5%) (p > 0.05). All urine cultures were positive after 12 months with significantly more poly-microbial results found after NB (81.3%) compared with IC (67.2%) (p = 0.018). In univariate and multivariate logistic regression analysis the presence of hydronephrosis was independently associated with the occurrence of infectious complications (OR 4.2; CI 95% 1.525-11.569; p = 0.006). CONCLUSION: A positive urinalysis is a common finding after urinary diversion. Hydronephrosis is a serious risk factor with respect to infection-related complications. The simple fact of a positive urinalysis does not warrant antimicrobial treatment.

MR-guided proton therapy: Impact of magnetic fields on the detector response.

PURPOSE: To investigate the response of detectors for proton dosimetry in the presence of magnetic fields. MATERIAL AND METHODS: Four ionization chambers (ICs), two thimble-type and two plane-parallel-type, and a diamond detector were investigated. All detectors were irradiated with homogeneous single-energy-layer fields, using 252.7 MeV proton beams. A Farmer IC was additionally irradiated in the same geometrical configuration, but with a lower nominal energy of 97.4 MeV. The beams were subjected to magnetic field strengths of 0, 0.25, 0.5, 0.75, and 1 T produced by a research dipole magnet placed at the room's isocenter. Detectors were positioned at 2 cm water equivalent depth, with their stem perpendicular to both the magnetic field lines and the proton beam's central axis, in the direction of the Lorentz force. Normality and two sample statistical Student's t tests were performed to assess the influence of the magnetic field on the detectors' responses. RESULTS: For all detectors, a small but significant magnetic field-dependent change of their response was found. Observed differences compared to the no magnetic field case ranged from +0.5% to -0.7%. The magnetic field dependence was found to be nonlinear and highest between 0.25 and 0.5 T for 252.7 MeV proton beams. A different variation of the Farmer chamber response with magnetic field strength was observed for irradiations using lower energy (97.4 MeV) protons. The largest magnetic field effects were observed for plane-parallel ionization chambers. CONCLUSION: Small magnetic field-dependent changes in the detector response were identified, which should be corrected for dosimetric applications.

Hybrid low-voltage physical unclonable function based on inkjet-printed metal-oxide transistors.

Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness.

OpenPepXL: An Open-Source Tool for Sensitive Identification of Cross-Linked Peptides in XL-MS.

Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.