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Background: The reliable and efficient estimation of uncertainty in artificial intelligence (AI) models poses an ongoing challenge in many fields such as radiation therapy. AI models are intended to automate manual steps involved in the treatment planning workflow. We focus in this study on dose prediction models that predict an optimal dose trade‐off for each new patient for a specific treatment modality. They can guide physicians in the optimization, be part of automatic treatment plan generation or support decision in treatment indication. Most common uncertainty estimation methods are based on Bayesian approximations, like Monte Carlo dropout (MCDO) or Deep ensembling (DE). These two techniques, however, have a high inference time (i.e., require multiple inference passes) and might not work for detecting out‐of‐distribution (OOD) data (i.e., overlapping uncertainty estimate for in‐distribution (ID) and OOD). Purpose: In this study, we present a direct uncertainty estimation method and apply it for a dose prediction U‐Net architecture. It can be used to flag OOD data and give information on the quality of the dose prediction. Methods: Our method consists in the addition of a branch decoding from the bottleneck which reconstructs the CT scan given as input. The input reconstruction error can be used as a surrogate of the model uncertainty. For the proof‐of‐concept, our method is applied to proton therapy dose prediction in head and neck cancer patients. A dataset of 60 oropharyngeal patients was used to train the network using a nested cross‐validation approach with 11 folds (training: 50 patients, validation: 5 patients, test: 5 patients). For the OOD experiment, we used 10 extra patients with a different head and neck sub‐location. Accuracy, time‐gain, and OOD detection are analyzed for our method in this particular application and compared with the popular MCDO and DE. Results: The additional branch did not reduce the accuracy of the dose prediction model. The median absolute error is close to zero for the target volumes and less than 1% of the dose prescription for organs at risk. Our input reconstruction method showed a higher Pearson correlation coefficient with the prediction error (0.620) than DE (0.447) and MCDO (between 0.599 and 0.612). Moreover, our method allows an easier identification of OOD (no overlap for ID and OOD data and a Z‐score of 34.05). The uncertainty is estimated simultaneously to the regression task, therefore requires less time and computational resources. Conclusions: This study shows that the error in the CT scan reconstruction can be used as a surrogate of the uncertainty of the model. The Pearson correlation coefficient with the dose prediction error is slightly higher than state‐of‐the‐art techniques. OOD data can be more easily detected and the uncertainty metric is computed simultaneously to the regression task, therefore faster than MCDO or DE. The code and pretrained model are available on the gitlab repository: https://gitlab.com/ai4miro/ct‐reconstruction‐for‐uncertainty‐quatification‐of‐hdunet [ABSTRACT FROM AUTHOR]

Understanding the diverse electrochemical reactions occurring at electrode‐electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular‐level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely‐defined EEIs and visualizing the reactivity of their individual components using single‐entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state‐of‐the‐art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well‐defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications. [ABSTRACT FROM AUTHOR]

Background: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt‐60‐based systems and linear accelerators with C‐arm, O‐ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality‐specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient‐specific basis. However, manually generating treatment plans for each modality across every patient is time‐consuming and clinically impractical. Purpose: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning‐based convolutional neural networks. The baseline network performs a single‐task (ST), predicting dose for a single modality. Our proposed multi‐task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient‐specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient‐specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. Methods: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two‐tailed Wilcoxon signed‐rank test. Results: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [–0.0865, –0.0712]). Conclusion: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre‐treatment decision‐making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site‐agnostic dose prediction model, enhancing its generalizability and applicability. [ABSTRACT FROM AUTHOR]

Rationally designing stable nonaqueous electrolytes for Mg metal anodes demands a thorough understanding of their interfacial behaviors. Here, the critical role of cation–anion pairing in improving the cathodic stabilities of amine‐based electrolytes against solvent reduction and H2 evolution is identified. It is demonstrated that strong coordination between solvating amine groups and the Mg2+ cation facilitates the dehydrogenation of the ─NH2 group, which is mainly responsible for low reversibility during Mg metal plating and stripping. Introducing ion‐pairing into the primary solvation shell can effectively weaken the amine coordination such that its reduction is suppressed. A novel interfacial behavior regarding parasitic reaction product dissolution is also identified, which is responsible for the failure of interfacial passivation. An ion‐pairing electrolyte is developed based on a weakly‐solvated amine molecule and strongly coordinating Mg2+ salt. This electrolyte composition delivers long‐term Mg metal anode cycling with 99.6% Coulombic efficiency for 800 cycles. [ABSTRACT FROM AUTHOR]

Background: Throughout a patient's course of radiation therapy, maintaining accuracy of their initial treatment plan over time is challenging due to anatomical changes‐for example, stemming from patient weight loss or tumor shrinkage. Online adaptation of their RT plan to these changes is crucial, but hindered by manual and time‐consuming processes. While deep learning (DL) based solutions have shown promise in streamlining adaptive radiation therapy (ART) workflows, they often require large and extensive datasets to train population‐based models. Purpose: This study extends our prior research by introducing a minimalist approach to patient‐specific adaptive dose prediction. In contrast to our prior method, which involved fine‐tuning a pre‐trained population model, this new method trains a model from scratch using only a patient's initial treatment data. This patient‐specific dose predictor aims to enhance clinical accessibility, thereby empowering physicians and treatment planners to make more informed, quantitative decisions in ART. We hypothesize that patient‐specific DL models will provide more accurate adaptive dose predictions for their respective patients compared to a population‐based DL model. Methods: We selected 33 patients to train an adaptive population‐based (AP) model. Ten additional patients were selected, and their respective initial RT data served as single samples for training patient‐specific (PS) models. These 10 patients contained an additional 26 ART plans that were withheld as the test dataset to evaluate AP versus PS model dose prediction performance. We assessed model performance using Mean Absolute Percent Error (MAPE) by comparing predicted doses to the originally delivered ground truth doses. We used the Wilcoxon signed‐rank test to determine statistically significant differences in terms of MAPE between the AP and PS model results across the test dataset. Furthermore, we calculated differences between predicted and ground truth mean doses for segmented structures and determined statistical significance in the differences for each of them. Results: The average MAPE across AP and PS model dose predictions was 5.759% and 4.069%, respectively. The Wilcoxon signed‐rank test yielded two‐tailed p‐value = 2.9802×10−8$2.9802\ \times \ {10}^{ - 8}$, indicating that the MAPE differences between the AP and PS model dose predictions are statistically significant, and 95% confidence interval = [−2.1610, −1.0130], indicating 95% confidence that the MAPE difference between the AP and PS models for a population lies in this range. Out of 24 total segmented structures, the comparison of mean dose differences for 12 structures indicated statistical significance with two‐tailed p‐values < 0.05. Conclusion: Our study demonstrates the potential of patient‐specific deep learning models in application to ART. Notably, our method streamlines the training process by minimizing the size of the required training dataset, as only a single patient's initial treatment data is required. External institutions considering the implementation of such a technology could package such a model so that it only requires the upload of a reference treatment plan for model training and deployment. Our single patient learning strategy demonstrates promise in ART due to its minimal dataset requirement and its utility in personalization of cancer treatment. [ABSTRACT FROM AUTHOR]

Background: The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra‐patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re‐optimization. Re‐optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re‐optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment. Purpose: Re‐optimization in its current form is time‐consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine‐tune a previously trained population model towards a patient‐specific model. Our fine‐tuned, patient‐specific (FT‐PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re‐optimize accordingly. Methods: An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT‐PS models. The previously trained AP model was utilized as the base model weights prior to re‐initializing model training for each FT‐PS model. Ten FT‐PS models were separately trained by fine‐tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT‐PS models. Each AP and FT‐PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth. Results: MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT‐PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT‐PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t‐test with two‐tailed p‐value equal to 3.851×10−9$3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [−2.483, −1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT‐PS models. Conclusion: We utilized the IDOL method to fine‐tune a population‐based dose prediction model into an adaptive, patient‐specific model. The averaged MAPE across the test dataset was 5.759% for the population‐based model versus 3.747% for the fine‐tuned, patient‐specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient‐specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Though early ASD diagnosis is highly stable, this case report describes a rare situation in which symptoms resolved without intervention over a 4 month period. We do not recommend delaying diagnosis in symptomatic children who meet criteria but when major behavioral changes are reported after diagnosis, reevaluation may be beneficial. [ABSTRACT FROM AUTHOR]

Electronically conductive hydrogels have gained popularity in bioelectronic interfaces because their mechanical properties are similar to biological tissues, potentially preventing scaring in implanted electronics. Hydrogels have low elastic moduli, due to their high water content, which facilitates their integration with biological tissues. To achieve electronically conductive hydrogels, however, requires the integration of conducting polymers or nanoparticles. These "hard" components increase the elastic modulus of the hydrogel, removing their desirable compatibility with biological tissues, or lead to the heterogeneous distribution of the conductive material in the hydrogel scaffold. A general strategy to transform hydrogels into electronically conductive hydrogels without affecting the mechanical properties of the parent hydrogel is still lacking. Herein, a two‐step method is reported for imparting conductivity to a range of different hydrogels by in‐situ polymerization of a water‐soluble and neutral conducting polymer precursor: 3,4–ethylenedioxythiophene diethylene glycol (EDOT‐DEG). The resulting conductive hydrogels are homogenous, have conductivities around 0.3 S m−1, low impedance, and maintain an elastic modulus of 5–15 kPa, which is similar to the preformed hydrogel. The simple preparation and desirable properties of the conductive hydrogels are likely to lead to new materials and applications in tissue engineering, neural interfaces, biosensors, and electrostimulation. [ABSTRACT FROM AUTHOR]

Conventional magnesium salts are generally considered to be passivating to the magnesium anode, hence chloride‐based electrolyte additives are often added to circumvent this issue. As a result, the performance of conventional magnesium salts has rarely been assessed in a chloride‐free system. This work provides a comparative study on the electrochemical performance and interfacial reaction of four commercially available magnesium salts with tetrabutylammonium borohydride as moisture scavenger. Magnesium bis(hexamethyldisilazide) was found to be the most reductively stable and enabled excellent magnesium plating/stripping without chloride additive. Investigation of the solid electrolyte interphase revealed a thin organic polyether layer that was conducive to magnesium‐ion migration. The results also showed that a minuscule amount of chloride (20 mM) improved the reversibility of magnesium plating/stripping but was still corrosive towards the current collector, thus establishing that chloride‐free electrolyte is most appropriate for future development of rechargeable magnesium batteries. [ABSTRACT FROM AUTHOR]

Purpose: Typically, the current dose prediction models are limited to small amounts of data and require retraining for a specific site, often leading to suboptimal performance. We propose a site‐agnostic, three‐dimensional dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine‐tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset. Methods: This study uses two separate datasets/treatment sites: data from patients with prostate cancer treated with intensity‐modulated radiation therapy (source data), and data from patients with head‐and‐neck cancer treated with volumetric‐modulated arc therapy (target data). We first developed a source model with 3D UNet architecture, trained from random initial weights on the source data. We evaluated the performance of this model on the source data. We then studied the generalizability of the model to the new target dataset via transfer learning. To do this, we built three more models, all with the same 3D UNet architecture: target model, adapted model, and combined model. The source and target models were trained on the source and target data from random initial weights, respectively. The adapted model fine‐tuned the source model to the target domain by using the target data. Finally, the combined model was trained from random initial weights on a combined data pool consisting of both target and source datasets. We tested all four models on the target dataset and evaluated quantitative dose‐volume histogram metrics for the planning target volume (PTV) and organs at risk (OARs). Results: When tested on the source treatment site, the source model accurately predicted the dose distributions with average (mean, max) absolute dose errors of (0.32%±0.14, 2.37%±0.93) (PTV) relative to the prescription dose, and highest mean dose error of 1.68%±0.76, and highest max dose error of 5.47%± 3.31 for femoral head right. The error in PTV dose coverage prediction is 3.21%±1.51 for D98, 3.04%±1.69 for D95, and 1.83%±1.01 for D02. Averaging across all OARs, the source model predicted the OAR mean dose within 1.38% and the OAR max dose within 3.64%. For the target treatment site, the target model average (mean, max) absolute dose errors relative to the prescription dose for the PTV were (1.08%±0.95, 2.90%±1.35). Left cochlea had the highest mean and max dose errors of 5.37%±5.82 and 8.33%±8.88, respectively. The errors in PTV dose coverage prediction for D98 and D95 were 2.88%±1.59 and 2.55%±1.28, respectively. The target model can predict the OAR mean dose within 2.43% and the OAR max dose within 4.33% on average across all OARs. Conclusion: We developed a site‐agnostic model for three‐dimensional dose prediction and tested its adaptability to a new target treatment site via transfer learning. Our proposed model can make accurate predictions with limited training data. [ABSTRACT FROM AUTHOR]

Purpose: Applications of deep learning (DL) are essential to realizing an effective adaptive radiotherapy (ART) workflow. Despite the promise demonstrated by DL approaches in several critical ART tasks, there remain unsolved challenges to achieve satisfactory generalizability of a trained model in a clinical setting. Foremost among these is the difficulty of collecting a task‐specific training dataset with high‐quality, consistent annotations for supervised learning applications. In this study, we propose a tailored DL framework for patient‐specific performance that leverages the behavior of a model intentionally overfitted to a patient‐specific training dataset augmented from the prior information available in an ART workflow—an approach we term Intentional Deep Overfit Learning (IDOL). Methods: Implementing the IDOL framework in any task in radiotherapy consists of two training stages: (1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and (2) intentionally overfitting this general model to a small training dataset‐specific the patient of interest (N+1) generated through perturbations and augmentations of the available task‐ and patient‐specific prior information to establish a personalized IDOL model. The IDOL framework itself is task‐agnostic and is, thus, widely applicable to many components of the ART workflow, three of which we use as a proof of concept here: the autocontouring task on replanning CTs for traditional ART, the MRI super‐resolution (SR) task for MRI‐guided ART, and the synthetic CT (sCT) reconstruction task for MRI‐only ART. Results: In the replanning CT autocontouring task, the accuracy measured by the Dice similarity coefficient improves from 0.847 with the general model to 0.935 by adopting the IDOL model. In the case of MRI SR, the mean absolute error (MAE) is improved by 40% using the IDOL framework over the conventional model. Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework. Conclusions: In this study, we propose a novel IDOL framework for ART and demonstrate its feasibility using three ART tasks. We expect the IDOL framework to be especially useful in creating personally tailored models in situations with limited availability of training data but existing prior information, which is usually true in the medical setting in general and is especially true in ART. [ABSTRACT FROM AUTHOR]

Purpose: Radiation therapy treatment planning is a trial‐and‐error, often time‐consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre‐trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient‐specific anatomy but also on physicians' preferred trade‐offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine‐tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade‐offs, as represented by a dose volume histogram (DVH), as inputs. Methods: In this work, we developed a modified U‐Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine‐tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients. Results: The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose. Conclusions: In this feasibility study, we have developed a 3D U‐Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan. [ABSTRACT FROM AUTHOR]

Purpose: Deep‐learning‐based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep‐learning applications such as natural language processing but is often neglected in segmentation literature. The purpose of this work is to demonstrate the significance of class imbalance in deep‐learning‐based segmentation and recommend tuning of the neural network optimization objective. Methods: An architecture and training procedure were chosen to represent common models in anatomical segmentation. A family of 5‐block 2D U‐Nets were independently trained to segment 10 structures from the Cancer Imaging Archive's Head‐Neck‐Radiomics‐HN1 dataset. We identify the optimal threshold for our models according to their Dice score on the validation datasets and consider perturbations about the optimum. A measure of structure prominence in segmentation datasets is defined, and its impact on the optimal threshold is analyzed. Finally, we consider the use of a 2D Dice objective in addition to binary cross entropy. Results: We observe significant decreases in perceived model performance with conventional 0.5‐thresholding. Perturbations of as little as ±0.05 about the optimum threshold induce a median reduction in Dice score of 11.8% for our models. There is statistical evidence to suggest a weak correlation between training dataset prominence and optimal threshold (Pearson r=0.92 and p≈10‐4). We find that network optimization with respect to the 2D Dice score itself significantly reduces variability due to thresholding but does not unequivocally create the best segmentation models when assessed with distance‐based segmentation metrics. Conclusion: Our results suggest that those practicing deep‐learning‐based contouring should consider their postprocessing procedures as a potential avenue for improved performance. For intensity‐based postprocessing, we recommend a mixed objective function consisting of the traditional binary cross entropy along with the 2D Dice score. [ABSTRACT FROM AUTHOR]

Essential oils (EOs) are known as any aromatic oily organic substances which are naturally synthesized in plants. Exhibiting a broad range of biological activities, EOs have played a key role in numerous industries for ages, including pharmaceutical, textile, and food. However, the volatility and high sensitivity to environmental influences pose challenges to the application of EOs on industrial scale. Microencapsulation via the spray-drying method is one of the promising techniques to overcome these challenges, thanks to the presence of wall materials that properly protect the core EOs from oxidation and evaporation. By optimization of key factors related to the infeed emulsion properties and spray-drying process, the encapsulation efficiency and retention of encapsulated EOs could be significantly improved, thus allowing a wide range of EO applications. This review attempts to discuss on different determining factors of the spray-drying process to develop an effective encapsulation formula for EOs. Furthermore, recent applications of encapsulated EOs in the fields of foods, pharmaceuticals, and textile industries are also thoroughly addressed. [ABSTRACT FROM AUTHOR]

Purpose: Despite the indispensable role of x‐ray computed tomography (CT) in diagnostic medicine, the associated harmful ionizing radiation dose is a major concern, as it may cause genetic diseases and cancer. Decreasing patients' exposure can reduce the radiation dose and hence the related risks, but it would inevitably induce higher quantum noise. Supervised deep learning techniques have been used to train deep neural networks for denoising low‐dose CT (LDCT) images, but the success of such strategies requires massive sets of pixel‐level paired LDCT and normal‐dose CT (NDCT) images, which are rarely available in real clinical practice. Our purpose is to mitigate the data scarcity problem for deep learning‐based LDCT denoising. Methods: To solve this problem, we devised a shift‐invariant property‐based neural network that uses only the LDCT images to characterize both the inherent pixel correlations and the noise distribution, shaping into our probabilistic self‐learning (PSL) framework. The AAPM Low‐dose CT Challenge dataset was used to train the network. Both simulated datasets and real dataset were employed to test the denoising performance as well as the model generalizability. The performance was compared to a conventional method (total variation (TV)‐based), a popular self‐learning method (noise2void (N2V)), and a well‐known unsupervised learning method (CycleGAN) by using both qualitative visual inspection and quantitative metrics including peak signal‐noise‐ratio (PSNR), structural similarity index (SSIM) and contrast‐to‐noise‐ratio (CNR). The standard deviations (STD) of selected flat regions were also calculated for comparison. Results: The PSL method can improve the averaged PSNR/SSIM values from 27.61/0.5939 (LDCT) to 30.50/0.6797. By contrast, the averaged PSNR/SSIM values were 31.49/0.7284 (TV), 29.43/0.6699 (N2V), and 29.79/0.6992 (CycleGAN). The averaged STDs of selected flat regions were calculated to be 132.3 HU (LDCT), 25.77 HU (TV), 19.95 HU (N2V), 75.06 HU (CycleGAN), 60.62 HU (PSL) and 57.28 HU (NDCT). As for the low‐contrast lesion detectability quantification, the CNR were calculated to be 0.202 (LDCT), 0.356 (TV), 0.372 (N2V), 0.383 (CycleGAN), 0.399 (PSL), and 0.359 (NDCT). By visual inspection, we observed that the proposed PSL method can deliver a noise‐suppressed and detail‐preserved image, while the TV‐based method would lead to the blocky artifact, the N2V method would produce over‐smoothed structures and CT value biased effect, and the CycleGAN method would generate slightly noisy results with inaccurate CT values. We also verified the generalizability of the PSL method, which exhibited superior denoising performance among various testing datasets with different data distribution shifts. Conclusions: A deep learning‐based convolutional neural network can be trained without paired datasets. Qualitatively visual inspection showed the proposed PSL method can achieve superior denoising performance than all the competitors, despite that the employed quantitative metrics in terms of PSNR, SSIM and CNR did not always show consistently better values. [ABSTRACT FROM AUTHOR]

Novel bio‐based microparticles and latexes are synthesized using green chemistry principles through the reaction of aconitic acid (a sugarcane byproduct) and epoxidized linseed oil (ELO). No volatile solvents have been used, and no initiators are present in this polymerization. Using different agitation techniques, we investigate the particles and latexes formed via suspension polymerization in contrast to bulk dispersion polymerization. With the addition of water and a small amount of surfactant to the ELO and aconitic system, a thermally stable material up to 250°C is created that exhibits adhesive and hydrophobic properties. These microparticle latexes are anticipated for applications in hydrophobic coating and personal care products. [ABSTRACT FROM AUTHOR]

Purpose: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity‐modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. Methods: We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35 000 plans. We studied and compared two different models, Models I and II. Although they both used the same anatomical structures — including the planning target volume (PTV), organs at risk (OARs), and body — these models were designed with two different methods for representing beam angles. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D98, D95, D50, D2, Dmax, Dmean, Paddick Conformation Number, R50, and Homogeneity index. Results: Our deep learning models predicted voxel‐level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head. Conclusions: Treatment planners who use our models will be able to use deep learning to control the trade‐offs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning. [ABSTRACT FROM AUTHOR]

In radiotherapy, a trade‐off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil‐beam convolution can be much faster than Monte‐Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low‐accuracy doses to high‐accuracy, using intensity information obtained from computed tomography (CT) images. A deep learning‐driven framework was developed to test the hypothesis by converting between two commercially available dose calculation methods: Anisotropic analytic algorithm (AAA) and Acuros XB (AXB). A hierarchically dense U‐Net model was developed to boost the accuracy of AAA dose toward the AXB level. The network contained multiple layers of varying feature sizes to learn their dose differences, in relationship to CT, both locally and globally. Anisotropic analytic algorithm and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels. For each case, the CT and the AAA dose were used as the input and the AXB dose as the "ground‐truth" output, to train and test the model. The mean squared errors (MSEs) and gamma passing rates (2 mm/2% & 1 mm/1%) were calculated between the boosted AAA doses and the "ground‐truth" AXB doses. The boosted AAA doses demonstrated substantially improved match to the "ground‐truth" AXB doses, with average (± s.d.) gamma passing rate (1 mm/1%) 97.6% (±2.4%) compared to 87.8% (±9.0%) of the original AAA doses. The corresponding average MSE was 0.11(±0.05) vs 0.31(±0.21). Deep learning is able to capture the differences between dose calculation algorithms to boost the low‐accuracy algorithms. By combining a less accurate dose calculation algorithm with a trained deep learning model, dose calculation can potentially achieve both high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Ho Chi Minh City (HCMC, Vietnam) is one of the fastest growing megacities in the world. In this paper, we attempt to analyse the dynamics of nutrients, suspended sediments, and water discharges in its aquatic systems today and in the future. The work is based on nine sampling sites along the Saigon River and one on the Dongnai River to identify the reference water status upstream from the urban area and the increase in fluxes that occur within the city and its surroundings. For the first time, the calculated fluxes allow drawing up sediment and nutrient budgets at the basin scale and the quantification of total nutrient loading to the estuarine and coastal zones (2012–2016 period). Based on both national Vietnamese and supplementary monitoring programs, we estimated the water, total suspended sediment, and nutrients (Total N, Total P, and dissolved silica: DSi) fluxes at 137 m3 year−1, 3,292 × 103 tonSS year−1, 5,323 tonN year−1, 450 tonP year−1, and 2,734 tonSi year−1 for the Saigon River and 1,693 m3 year−1, 1,175 × 103 tonSS year−1, 31,030 tonN year−1, 1,653 tonP year−1, and 31,138 tonSi year−1 for the Dongnai River, respectively. Nutrient fluxes provide an indicator of coastal eutrophication potential (indicator of coastal eutrophication potential), using nutrient stoichiometry ratios. Despite an excess of nitrogen and phosphorus over silica, estuarine waters downstream of the megacity are not heavily impacted by HCMC. Finally, we analysed scenarios of future trends (2025–2050) for the nutrient inputs on the basis of expected population growth in HCMC and improvement of wastewater treatment capacity. We observed that without the construction of a large number of additional wastewater treatment plants, the eutrophication problem is likely to worsen. The results are discussed in the context of the wastewater management policy. [ABSTRACT FROM AUTHOR]

Purpose: In the treatment planning process of intensity‐modulated radiation therapy (IMRT), a human planner operates the treatment planning system (TPS) to adjust treatment planning parameters, for example, dose volume histogram (DVH) constraints' locations and weights, to achieve a satisfactory plan for each patient. This process is usually time‐consuming, and the plan quality depends on planer's experience and available planning time. In this study, we proposed to model the behaviors of human planners in treatment planning by a deep reinforcement learning (DRL)‐based virtual treatment planner network (VTPN), such that it can operate the TPS in a human‐like manner for treatment planning. Methods and Materials: Using prostate cancer IMRT as an example, we established the VTPN using a deep neural network developed. We considered an in‐house optimization engine with a weighted quadratic objective function. Virtual treatment planner network was designed to observe an intermediate plan DVHs and decide the action to improve the plan by changing weights and threshold dose in the objective function. We trained the VTPN in an end‐to‐end DRL process in 10 patient cases. A plan score was used to measure plan quality. We demonstrated the feasibility and effectiveness of the trained VTPN in another 64 patient cases. Results: Virtual treatment planner network was trained to spontaneously learn how to adjust treatment planning parameters to generate high‐quality treatment plans. In the 64 testing cases, with initialized parameters, quality score was 4.97 (±2.02), with 9.0 being the highest possible score. Using VTPN to perform treatment planning improved quality score to 8.44 (±0.48). Conclusions: To our knowledge, this was the first time that intelligent treatment planning behaviors of human planner in external beam IMRT are autonomously encoded in an artificial intelligence system. The trained VTPN is capable of behaving in a human‐like way to produce high‐quality plans. [ABSTRACT FROM AUTHOR]

Purpose: We propose a novel domain‐specific loss, which is a differentiable loss function based on the dose‐volume histogram (DVH), and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural network for generating Pareto optimal dose distributions, and evaluate the effects of the domain‐specific loss on the model performance. Methods: In this study, three loss functions — mean squared error (MSE) loss, DVH loss, and adversarial (ADV) loss — were used to train and compare four instances of the neural network model: (a) MSE, (b) MSE + ADV, (c) MSE + DVH, and (d) MSE + DVH+ADV. The data for 70 prostate patients, including the planning target volume (PTV), and the organs at risk (OAR) were acquired as 96 × 96 × 24 dimension arrays at 5 mm3 voxel size. The dose influence arrays were calculated for 70 prostate patients, using a 7 equidistant coplanar beam setup. Using a scalarized multicriteria optimization for intensity‐modulated radiation therapy, 1200 Pareto surface plans per patient were generated by pseudo‐randomizing the PTV and OAR tradeoff weights. With 70 patients, the total number of plans generated was 84 000 plans. We divided the data into 54 training, 6 validation, and 10 testing patients. Each model was trained for a total of 100,000 iterations, with a batch size of 2. All models used the Adam optimizer, with a learning rate of 1 × 10−3. Results: Training for 100 000 iterations took 1.5 days (MSE), 3.5 days (MSE+ADV), 2.3 days (MSE+DVH), and 3.8 days (MSE+DVH+ADV). After training, the prediction time of each model is 0.052 s. Quantitatively, the MSE+DVH+ADV model had the lowest prediction error of 0.038 (conformation), 0.026 (homogeneity), 0.298 (R50), 1.65% (D95), 2.14% (D98), and 2.43% (D99). The MSE model had the worst prediction error of 0.134 (conformation), 0.041 (homogeneity), 0.520 (R50), 3.91% (D95), 4.33% (D98), and 4.60% (D99). For both the mean dose PTV error and the max dose PTV, Body, Bladder and rectum error, the MSE+DVH+ADV outperformed all other models. Regardless of model, all predictions have an average mean and max dose error <2.8% and 4.2%, respectively. Conclusion: The MSE+DVH+ADV model performed the best in these categories, illustrating the importance of both human and learned domain knowledge. Expert human domain‐specific knowledge can be the largest driver in the performance improvement, and adversarial learning can be used to further capture nuanced attributes in the data. The real‐time prediction capabilities allow for a physician to quickly navigate the tradeoff space for a patient, and produce a dose distribution as a tangible endpoint for the dosimetrist to use for planning. This is expected to considerably reduce the treatment planning time, allowing for clinicians to focus their efforts on the difficult and demanding cases. [ABSTRACT FROM AUTHOR]

Purpose: Beam orientation selection, whether manual or protocol‐based, is the current clinical standard in radiation therapy treatment planning, but it is tedious and can yield suboptimal results. Many algorithms have been designed to optimize beam orientation selection because of its impact on treatment plan quality, but these algorithms suffer from slow calculation of the dose influence matrices of all candidate beams. We propose a fast beam orientation selection method, based on deep learning neural networks (DNN), capable of developing a plan comparable to those developed by the state‐of‐the‐art column generation (CG) method. Our model's novelty lies in its supervised learning structure (using CG to teach the network), DNN architecture, and ability to learn from anatomical features to predict dosimetrically suitable beam orientations without using dosimetric information from the candidate beams. This may save hours of computation. Methods: A supervised DNN is trained to mimic the CG algorithm, which iteratively chooses beam orientations one‐by‐one by calculating beam fitness values based on Karush‐Kush‐Tucker optimality conditions at each iteration. The DNN learns to predict these values. The dataset contains 70 prostate cancer patients — 50 training, 7 validation, and 13 test patients — to develop and test the model. Each patient's data contains 6 contours: PTV, body, bladder, rectum, and left and right femoral heads. Column generation was implemented with a GPU‐based Chambolle‐Pock algorithm, a first‐order primal‐dual proximal‐class algorithm, to create 6270 plans. The DNN trained over 400 epochs, each with 2500 steps and a batch size of 1, using the Adam optimizer at a learning rate of 1 × 10−5 and a sixfold cross‐validation technique. Results: The average and standard deviation of training, validation, and testing loss functions among the six folds were 0.62 ± 0.09%, 1.04 ± 0.06%, and 1.44 ± 0.11%, respectively. Using CG and supervised DNN, we generated two sets of plans for each scenario in the test set. The proposed method took at most 1.5 s to select a set of five beam orientations and 300 s to calculate the dose influence matrices for 5 beams and finally 20 s to solve the fluence map optimization (FMO). However, CG needed around 15 h to calculate the dose influence matrices of all beams and at least 400 s to solve both the beam orientation selection and FMO problems. The differences in the dose coverage of PTV between plans generated by CG and by DNN were 0.2%. The average dose differences received by organs at risk were between 1 and 6 percent: Bladder had the smallest average difference in dose received (0.956 ± 1.184%), then Rectum (2.44 ± 2.11%), Left Femoral Head (6.03 ± 5.86%), and Right Femoral Head (5.885 ± 5.515%). The dose received by Body had an average difference of 0.10 ± 0.1% between the generated treatment plans. Conclusions: We developed a fast beam orientation selection method based on a DNN that selects beam orientations in seconds and is therefore suitable for clinical routines. In the training phase of the proposed method, the model learns the suitable beam orientations based on patients' anatomical features and omits time intensive calculations of dose influence matrices for all possible candidate beams. Solving the FMO to get the final treatment plan requires calculating dose influence matrices only for the selected beams. [ABSTRACT FROM AUTHOR]

Purpose: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the accurate dose engines are often time consuming. In this work, we try to resolve this dilemma by exploring deep learning (DL) for dose calculation. Methods: We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U‐net (HD U‐net) model and tested its feasibility with prostate intensity‐modulated radiation therapy (IMRT) cases. Mapping from an IMRT fluence map domain to a three‐dimensional (3D) dose domain requires a deep neural network of complicated architecture and a huge training dataset. To solve this problem, we first project the fluence maps to the dose domain using a broad beam ray‐tracing (RT) algorithm, and then we use the HD U‐net to map the RT dose distribution into an accurate dose distribution calculated using a collapsed cone convolution/superposition (CS) algorithm. The model is trained on 70 patients with fivefold cross validation, and tested on a separate 8 patients. Results: It takes about 1 s to compute a 3D dose distribution for a typical 7‐field prostate IMRT plan, which can be further reduced to achieve real‐time dose calculation by optimizing the network. The average Gamma passing rate between DL and CS dose distributions for the 8 test patients are 98.5% (±1.6%) at 1 mm/1% and 99.9% (±0.1%) at 2 mm/2%. For comparison of various clinical evaluation criteria (dose‐volume points) for IMRT plans between two dose distributions, the average difference for dose criteria is less than 0.25 Gy while for volume criteria is <0.16%, showing that the DL dose distributions are clinically identical to the CS dose distributions. Conclusions: We have shown the feasibility of using DL for calculating radiotherapy dose distribution with high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To achieve more translatable preclinical research results, small animal irradiation needs to more closely simulate human radiotherapy. Although the clinical gold standard is intensity‐modulated radiation therapy (IMRT), the direct translation of this method for small animals is impractical. In this study we describe the treatment planning system for a novel dose modulation device to address this challenge. Methods: Using delineated target and avoidance structures, a rectangular aperture optimization (RAO) problem was formulated to penalize deviations from a desired dose distribution and limit the number of selected rectangular apertures. RAO was used to create IMRT plans with highly concave targets in the mouse brain, and the plan quality was compared to that using a hypothetical miniaturized multileaf collimator (MLC). RAO plans were also created for a realistic application of mouse whole liver irradiation and for a highly complex two‐dimensional (2D) dose distribution as a proof‐of‐principle. Beam commissioning data, including output and off‐axis factors and percent depth dose (PDD) curves, were acquired for our small animal irradiation system and incorporated into the treatment planning system. A plan post‐processing step was implemented for aperture size‐specific dose recalculation and aperture weighting reoptimization. Results: The first RAO test case achieved highly conformal doses to concave targets in the brain, with substantially better dose gradient, conformity, and target dose homogeneity than the hypothetical miniaturized MLC plans. In the second test case, a highly conformal dose to the liver was achieved with significant sparing of the kidneys. RAO also successfully replicated a complex 2D dose distribution with three prescription dose levels. Energy spectra for field sizes 1 to 20 mm were calculated to match the measured PDD curves, with maximum and mean dose deviations of 4.47 ± 0.30% and 1.71 ± 0.18%. The final reoptimization of aperture weightings for the complex RAO test plan was able to reduce the maximum and mean dose deviations between the optimized and recalculated dose distributions from 10.3% to 6.6% and 4.0% to 2.8%, respectively. Conclusions: Using the advanced optimization techniques, complex IMRT plans were achieved using a simple dose modulation device. Beam commissioning data were incorporated into the treatment planning process to more accurately predict the resulting dose distribution. This platform substantially reduces the gap in treatment plan quality between clinical and preclinical radiotherapy, potentially increasing the value and flexibility of small animal studies. [ABSTRACT FROM AUTHOR]

Purpose: A dose‐modulation device for small animal radiotherapy is required to use clinically analogous treatment techniques, which will likely increase the translatability of preclinical research results. Because the clinically used multileaf collimator (MLC) is impractical for miniaturization, we have developed a simpler, better‐suited sparse orthogonal collimator (SOC) for delivering small animal intensity‐modulated radiation therapy (IMRT) using a rectangular aperture optimization (RAO) treatment planning system. Methods: The SOC system was modeled in computer‐aided design software and fabricated with machined tungsten leaves and three‐dimensional (3D) printed leaf housing. A graphical user interface was developed for controlling and calibrating the SOC leaves, which are driven by Arduino‐controlled stepper motors. A Winston‐Lutz test was performed to assess mechanical alignment, and abutting field and grid dose patterns were created to analyze intra‐ and intercalibration leaf positioning error. Leaf transmission and penumbra were measured over the full range of gantry angles and leaf positions, respectively. Three SOC test plans were delivered, and film measurements were compared to the intended dose distributions. The differences in maximum, mean, and minimum, as well as pixelwise absolute dose differences, were compared for each structure, and a gamma analysis was performed for the target structures using criteria of 4% dose difference and 0.3 mm distance to agreement. Results: The Winston‐Lutz test revealed maximum directional offsets between the SOC and primary collimator axes of 0.53 mm at 0° and 0.68 mm over the full 360°. Upper and lower abutting field patterns had maximum dose deviations of 18.8 ± 3.1% and 15.5 ± 2.9%, respectively, and grid patterns showed intra‐ and intercalibration repeatability of 93% and 91%, respectively. Extremely low midleaf (0.15 ± 0.05%) and interleaf (0.27 ± 0.22%) transmission was measured, with no significant rotational variation. The average penumbra was ~0.8 mm for all leaves at field center, with a range of 0.17 mm for all leaf positions. A highly concave test plan was delivered with a ~ 95% gamma analysis pass rate, and a realistic mouse phantom liver irradiation plan achieved a pass rate of ~98%. A highly complex dose distribution was also created with 551 SOC apertures averaging 2.4 mm in size. Conclusions: A sparse orthogonal collimator was developed and commissioned, with promising preliminary dosimetry results. The SOC design, with its limited moving components and high dose‐modulation resolution, is ideal for delivering high‐quality small animal IMRT with our RAO‐based treatment planning system. [ABSTRACT FROM AUTHOR]

Purpose: The use of neural networks to directly predict three‐dimensional dose distributions for automatic planning is becoming popular. However, the existing methods use only patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work was to develop a more general model that considers variable beam configurations in addition to patient anatomy to achieve more comprehensive automatic planning with a potentially easier clinical implementation, without the need to train specific models for different beam settings. Methods: The proposed anatomy and beam (AB) model is based on our newly developed deep learning architecture, and hierarchically densely connected U‐Net (HD U‐Net), which combines U‐Net and DenseNet. The AB model contains 10 input channels: one for beam setup and the other 9 for anatomical information (PTV and organs). The beam setup information is represented by a 3D matrix of the non‐modulated beam's eye view ray‐tracing dose distribution. We used a set of images from 129 patients with lung cancer treated with IMRT with heterogeneous beam configurations (4–9 beams of various orientations) for training/validation (100 patients) and testing (29 patients). Mean squared error was used as the loss function. We evaluated the model's accuracy by comparing the mean dose, maximum dose, and other relevant dose–volume metrics for the predicted dose distribution against those of the clinically delivered dose distribution. Dice similarity coefficients were computed to address the spatial correspondence of the isodose volumes between the predicted and clinically delivered doses. The model was also compared with our previous work, the anatomy only (AO) model, which does not consider beam setup information and uses only 9 channels for anatomical information. Results: The AB model outperformed the AO model, especially in the low and medium dose regions. In terms of dose–volume metrics, AB outperformed AO by about 1–2%. The largest improvement was found to be about 5% in lung volume receiving a dose of 5Gy or more (V5). The improvement for spinal cord maximum dose was also important, that is, 3.6% for cross‐validation and 2.6% for testing. The AB model achieved Dice scores for isodose volumes as much as 10% higher than the AO model in low and medium dose regions and about 2–5% higher in high dose regions. Conclusions: The AO model, which does not use beam configuration as input, can still predict dose distributions with reasonable accuracy in high dose regions but introduces large errors in low and medium dose regions for IMRT cases with variable beam numbers and orientations. The proposed AB model outperforms the AO model substantially in low and medium dose regions, and slightly in high dose regions, by considering beam setup information through a cumulative non‐modulated beam's eye view ray‐tracing dose distribution. This new model represents a major step forward towards predicting 3D dose distributions in real clinical practices, where beam configuration could vary from patient to patient, from planner to planner, and from institution to institution. [ABSTRACT FROM AUTHOR]

Fabrication and high‐rate performance of a safe lithium ion–sulfur battery (LISB) with sulfur‐copolymer [poly(S‐co‐divinylbenzene (DVB)] cathode having a sulfur content higher than 90 wt %, a carbon‐fiber interlayer, and a prelithiated hard‐carbon (Li‐HC) anode are reported, which mitigates problems of lithium–sulfur cells such as performance fade and safety issues due to dissolution of polysulfides and lithium‐dendrite growth. The poly(S‐co‐DVB) cathode offers scalability owing to the abundance and low cost of DVB. The Li‐HC anode, the surface of which is passivated by a solid electrolyte interphase, inhibits the deposition of polysulfides. As a result, the LISB exhibits reversible and stable cycling performance at high rates up to 3 C, which enables quick charging within 20 min, and delivers a reversible capacity of approximately 400 mAh g−1 at 3 C for 500 cycles. The results give insight into the design principle of promising, quickly charged, and safe LISBs. Faster and safer: Safe and stable Li ion–S batteries with sulfur–divinylbenzene (S‐DVB) copolymer cathode, carbon‐fiber (CF) interlayer, and prelithiated hard‐carbon anode exhibit stable high‐rate cycling performance up to 3 C by mitigating problems of Li–S cells such as performance fade and safety issues due to dissolution of polysulfides and lithium‐dendrite growth. [ABSTRACT FROM AUTHOR]

Purpose: Although collimator rotation is an optimization variable that can be exploited for dosimetric advantages, existing Volumetric Modulated Arc Therapy (VMAT) optimization uses a fixed collimator angle in each arc and only rotates the collimator between arcs. In this study, we develop a novel integrated optimization method for VMAT, accounting for dynamic collimator angles during the arc motion. Methods: Direct Aperture Optimization (DAO) for Dynamic Collimator in VMAT (DC‐VMAT) was achieved by adding to the existing dose fidelity objective an anisotropic total variation term for regulating the fluence smoothness, a binary variable for forming simple apertures, and a group sparsity term for controlling collimator rotation. The optimal collimator angle for each beam angle was selected using the Dijkstra's algorithm, where the node costs depend on the estimated fluence map at the current iteration and the edge costs account for the mechanical constraints of multi‐leaf collimator (MLC). An alternating optimization strategy was implemented to solve the DAO and collimator angle selection (CAS). Feasibility of DC‐VMAT using one full‐arc with dynamic collimator rotation was tested on a phantom with two small spherical targets, a brain, a lung and a prostate cancer patient. The plan was compared against a static collimator VMAT (SC‐VMAT) plan using three full arcs with 60 degrees of collimator angle separation in patient studies. Results: With the same target coverage, DC‐VMAT achieved 20.3% reduction of R50 in the phantom study, and reduced the average max and mean OAR dose by 4.49% and 2.53% of the prescription dose in patient studies, as compared with SC‐VMAT. The collimator rotation co‐ordinated with the gantry rotation in DC‐VMAT plans for deliverability. There were 13 beam angles in the single‐arc DC‐VMAT plan in patient studies that requires slower gantry rotation to accommodate multiple collimator angles. Conclusions: The novel DC‐VMAT approach utilizes the dynamic collimator rotation during arc delivery. In doing so, DC‐VMAT affords more sophisticated intensity modulation, alleviating the limitation previously imposed by the square beamlet from the MLC leaf thickness and achieves higher effective modulation resolution. Consequently, DC‐VMAT with a single arc manages to achieve superior dosimetry than SC‐VMAT with three full arcs. [ABSTRACT FROM AUTHOR]

Purpose: Intensity‐Modulated Proton Therapy (IMPT) is the state‐of‐the‐art method of delivering proton radiotherapy. Previous research has been mainly focused on optimization of scanning spots with manually selected beam angles. Due to the computational complexity, the potential benefit of simultaneously optimizing beam orientations and spot pattern could not be realized. In this study, we developed a novel integrated beam orientation optimization (BOO) and scanning‐spot optimization algorithm for intensity‐modulated proton therapy (IMPT). Methods: A brain chordoma and three unilateral head‐and‐neck patients with a maximal target size of 112.49 cm3 were included in this study. A total number of 1162 noncoplanar candidate beams evenly distributed across 4 π steradians were included in the optimization. For each candidate beam, the pencil‐beam doses of all scanning spots covering the PTV and a margin were calculated. The beam angle selection and spot intensity optimization problem was formulated to include three terms: a dose fidelity term to penalize the deviation of PTV and OAR doses from ideal dose distribution; an L1‐norm sparsity term to reduce the number of active spots and improve delivery efficiency; a group sparsity term to control the number of active beams between 2 and 4. For the group sparsity term, convex L2,1‐norm and nonconvex L2,1/2‐norm were tested. For the dose fidelity term, both quadratic function and linearized equivalent uniform dose (LEUD) cost function were implemented. The optimization problem was solved using the Fast Iterative Shrinkage‐Thresholding Algorithm (FISTA). The IMPT BOO method was tested on three head‐and‐neck patients and one skull base chordoma patient. The results were compared with IMPT plans created using column generation selected beams or manually selected beams. Results: The L2,1‐norm plan selected spatially aggregated beams, indicating potential degeneracy using this norm. L2,1/2‐norm was able to select spatially separated beams and achieve smaller deviation from the ideal dose. In the L2,1/2‐norm plans, the [mean dose, maximum dose] of OAR were reduced by an average of [2.38%, 4.24%] and[2.32%, 3.76%] of the prescription dose for the quadratic and LEUD cost function, respectively, compared with the IMPT plan using manual beam selection while maintaining the same PTV coverage. The L2,1/2 group sparsity plans were dosimetrically superior to the column generation plans as well. Besides beam orientation selection, spot sparsification was observed. Generally, with the quadratic cost function, 30%~60% spots in the selected beams remained active. With the LEUD cost function, the percentages of active spots were in the range of 35%~85%.The BOO‐IMPT run time was approximately 20 min. Conclusion: This work shows the first IMPT approach integrating noncoplanar BOO and scanning‐spot optimization in a single mathematical framework. This method is computationally efficient, dosimetrically superior and produces delivery‐friendly IMPT plans. [ABSTRACT FROM AUTHOR]

Purpose Direct aperture optimization (DAO) attempts to incorporate machine constraints in the inverse optimization to eliminate the post-processing steps in fluence map optimization (FMO) that degrade plan quality. Current commercial DAO methods utilize a stochastic or greedy approach to search a small aperture solution space. In this study, we propose a novel deterministic direct aperture optimization that integrates the segmentation of fluence map in the optimization problem using the multiphase piecewise constant Mumford-Shah formulation. Methods The Mumford-Shah based direct aperture optimization problem was formulated to include an L2-norm dose fidelity term to penalize differences between the projected dose and the prescribed dose, an anisotropic total variation term to promote piecewise continuity in the fluence maps, and the multiphase piecewise constant Mumford-Shah function to partition the fluence into pairwise discrete segments. A proximal-class, first-order primal-dual solver was implemented to solve the large scale optimization problem, and an alternating module strategy was implemented to update fluence and delivery segments. Three patients of varying complexity-one glioblastoma multiforme (GBM) patient, one lung (LNG) patient, and one bilateral head and neck (H&N) patient with 3 PTVs-were selected to test the new DAO method. For each patient, 20 non-coplanar beams were first selected using column generation, followed by the Mumford-Shah based DAO (DAOMS). For comparison, a popular and successful approach to DAO known as simulated annealing-a stochastic approach-was replicated. The simulated annealing DAO (DAOSA) plans were then created using the same beam angles and maximum number of segments per beam. PTV coverage, PTV homogeneity [ABSTRACT FROM AUTHOR]

Electrochemical cycling performance of microsize bulk and thin film Sn electrodes up to 50 cycles in Mg half-cell configuration, as well as Mg diffusivity and interfacial reaction behavior in a phenylmagnesium chloride (PhMgCl)/THF electrolyte are studied. The bulk Sn electrode delivers capacities of 321-289 mAh g−1 with capacity retention of 90 % and coulombic efficiencies higher than 99 % over 30 cycles, and thus demonstrates the potential of Sn anodes. The Sn film electrode shows good cycling ability. Surface analysis by XPS reveals that the surface of cycled electrodes is composed of a mixture of various inorganic salts of Sn and Mg formed by interfacial reactions between Sn and PhMgCl, and organic functionality produced by decomposition of THF, indicating surface film formation. The Mg2+ diffusivity of Sn is on the order of 10−11 cm2 s−1, which is four orders of magnitude lower than the Li+ diffusivity of Sn in Li-ion batteries. Advanced material design for enhanced electronic conductivity and control of the interfacial chemistry for formation of a surface protective film are believed to be the keys to overcome such sluggish kinetics, increase the capacity, and improve the cycling performance. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic resonance imaging (MRI)-guided Co-60 provides daily and intrafractional MRI soft tissue imaging for improved target and critical organ tracking. To increase delivery efficiency, the system uses three Co-60 sources at 120? apart, allowing up to 600 cGy combined dose rate at isocenter. Despite the potential tripling in output, creating a delivery plan that uses all three sources is considerably unintuitive. Here, the authors computerize the triplet orientation optimization using column generation, an approach that was demonstrated effective in integrated beam orientation and fluence optimization for noncoplanar therapies. To achieve a better plan quality without increasing the treatment time, the authors then solve a fluence map optimization (FMO) problem while regularizing the fluence maps to reduce the number of deliverable MLC segments. Methods: Three patients--one prostate, one lung, and one head and neck boost plan (H&NBoost)-- were evaluated in this study. For each patient, the beamlet doses were calculated using Monte Carlo, under a 0.35 T magnetic field, for 180 equally spaced coplanar beams grouped into 60 triplets. The beamlet size is 1.05×0.5 cm determined by the MLC leaf thickness and step size. The triplets were selected using the column generation algorithm. The FMO problem was formulated using an L2-norm dose fidelity term and an L1-norm anisotropic total variation regularization term, which allows controlling the number of MLC segments, and hence the treatment time, with minimal degradation to the dose. The authors' Fluence Regularization and Optimized Selection of Triplets (FROST) plans were compared against the clinical treatment plans (CLNs) produced by an experienced dosimetrist. PTV homogeneity, max dose, mean dose, D95, D98, and D99 were evaluated. OAR max and mean doses, as well as R50, defined as the ratio of the 50% isodose volume over the planning target volume were investigated. Results: The mean PTV D95, D98, and D99 differ by +0.04%, +0.07%, and +0.25% of the prescription dose between planning methods. The mean PTV homogeneity was virtually same with values at 0.8788 (FROST) and 0.8812 (CLN). R50 decreased by 0.67 comparing FROST to CLN. On average, FROST reduced Dmax and Dmean of OARs by 7.30% and 6.08% of the prescription dose, respectively. The manual CLN planning processes required numerous trial and error runs. The FROST plans on the other hand required minimal human intervention. Conclusions: Efficient delivery of MRI-guided Co-60 therapy needs the output of multiple sources yet suffers from unintuitive and laborious manual beam selection processes. Computerized triplet orientation optimization improves both planning efficiency and plan dosimetry. The novel fluence map regularization provides additional controls over the number of MLC segments and treatment time. [ABSTRACT FROM AUTHOR]

Purpose: Volumetric modulated arc therapy (VMAT) is a widely employed radiation therapy technique, showing comparable dosimetry to static beam intensity modulated radiation therapy (IMRT) with reduced monitor units and treatment time. However, the current VMAT optimization has various greedy heuristics employed for an empirical solution, which jeopardizes plan consistency and quality. The authors introduce a novel direct aperture optimization method for VMAT to overcome these limitations. Methods: The comprehensive VMAT (comVMAT) planning was formulated as an optimization problem with an L2-norm fidelity term to penalize the difference between the optimized dose and the prescribed dose, as well as an anisotropic total variation term to promote piecewise continuity in the fluence maps, preparing it for direct aperture optimization. A level set function was used to describe the aperture shapes and the difference between aperture shapes at adjacent angles was penalized to control MLC motion range. A proximal-class optimization solver was adopted to solve the large scale optimization problem, and an alternating optimization strategy was implemented to solve the fluence intensity and aperture shapes simultaneously. Single arc comVMAT plans, utilizing 180 beams with 2? angular resolution, were generated for a glioblastoma multiforme case, a lung (LNG) case, and two head and neck cases--one with three PTVs (H&N3PTV) and one with foue PTVs (H&N4PTV)--to test the efficacy. The plans were optimized using an alternating optimization strategy. The plans were compared against the clinical VMAT (clnVMAT) plans utilizing two overlapping coplanar arcs for treatment. Results: The optimization of the comVMAT plans had converged within 600 iterations of the block minimization algorithm. comVMAT plans were able to consistently reduce the dose to all organs-at-risk (OARs) as compared to the clnVMAT plans. On average, comVMAT plans reduced the max and mean OAR dose by 6.59% and 7.45%, respectively, of the prescription dose. Reductions in max dose and mean dose were as high as 14.5 Gy in the LNG case and 15.3 Gy in the H&N3PTV case. PTV coverages measured by D95, D98, and D99 were within 0.25% of the prescription dose. By comprehensively optimizing all beams, the comVMAT optimizer gained the freedom to allow some selected beams to deliver higher intensities, yielding a dose distribution that resembles a static beam IMRT plan with beam orientation optimization. Conclusions: The novel nongreedy VMAT approach simultaneously optimizes all beams in an arc and then directly generates deliverable apertures. The single arc VMAT approach thus fully utilizes the digital Linac's capability in dose rate and gantry rotation speed modulation. In practice, the new single VMAT algorithm generates plans superior to existing VMAT algorithms utilizing two arcs. [ABSTRACT FROM AUTHOR]

Purpose: To deliver high quality intensity modulated radiotherapy (IMRT) using a novel generalized sparse orthogonal collimators (SOCs), the authors introduce a novel direct aperture optimization (DAO) approach based on discrete rectangular representation. Methods: A total of seven patients--two glioblastoma multiforme, three head & neck (including one with three prescription doses), and two lung--were included. 20 noncoplanar beams were selected using a column generation and pricing optimization method. The SOC is a generalized conventional orthogonal collimators with N leaves in each collimator bank, where N = 1, 2, or 4. SOC degenerates to conventional jaws when N = 1. For SOC-based IMRT, rectangular aperture optimization (RAO) was performed to optimize the fluence maps using rectangular representation, producing fluence maps that can be directly converted into a set of deliverable rectangular apertures. In order to optimize the dose distribution and minimize the number of apertures used, the overall objective was formulated to incorporate an L2 penalty reflecting the difference between the prescription and the projected doses, and an L1 sparsity regularization term to encourage a low number of nonzero rectangular basis coefficients. The optimization problem was solved using the Chambolle-Pock algorithm, a first-order primal-dual algorithm. Performance of RAO was compared to conventional two-step IMRT optimization including fluence map optimization and direct stratification for multileaf collimator (MLC) segmentation (DMS) using the same number of segments. For the RAO plans, segment travel time for SOC delivery was evaluated for the N = 1, N = 2, and N = 4 SOC designs to characterize the improvement in delivery efficiency as a function of N. Results: Comparable PTV dose homogeneity and coverage were observed between the RAO and the DMS plans. The RAO plans were slightly superior to the DMS plans in sparing critical structures. On average, the maximum and mean critical organ doses were reduced by 1.94% and 1.44% of the prescription dose. The average number of delivery segments was 12.68 segments per beam for both the RAO and DMS plans. The N = 2 and N = 4 SOC designs were, on average, 1.56 and 1.80 times more efficient than the N = 1 SOC design to deliver. The mean aperture size produced by the RAO plans was 3.9 times larger than that of the DMS plans. Conclusions: The DAO and dose domain optimization approach enabled high quality IMRT plans using a low-complexity collimator setup. The dosimetric quality is comparable or slightly superior to conventional MLC-based IMRT plans using the same number of delivery segments. The SOC IMRT delivery efficiency can be significantly improved by increasing the leaf numbers, but the number is still significantly lower than the number of leaves in a typical MLC. [ABSTRACT FROM AUTHOR]

Purpose: Significant dosimetric benefits had been previously demonstrated in highly noncoplanar treatment plans. In this study, the authors developed and verified an individualized collision model for the purpose of delivering highly noncoplanar radiotherapy and tested the feasibility of total delivery automation with Varian TrueBeam developer mode. Methods: A hand-held 3D scanner was used to capture the surfaces of an anthropomorphic phantom and a human subject, which were positioned with a computer-aided design model of a TrueBeam machine to create a detailed virtual geometrical collision model. The collision model included gantry, collimator, and couch motion degrees of freedom. The accuracy of the 3D scanner was validated by scanning a rigid cubical phantom with known dimensions. The collision model was then validated by generating 300 linear accelerator orientations corresponding to 300 gantry-to-couch and gantry-to-phantom distances, and comparing the corresponding distance measurements to their corresponding models. The linear accelerator orientations reflected uniformly sampled noncoplanar beam angles to the head, lung, and prostate. The distance discrepancies between measurements on the physical and virtual systems were used to estimate treatment-site-specific safety buffer distances with 0.1%, 0.01%, and 0.001% probability of collision between the gantry and couch or phantom. Plans containing 20 noncoplanar beams to the brain, lung, and prostate optimized via an in-house noncoplanar radiotherapy platform were converted into XML script for automated delivery and the entire delivery was recorded and timed to demonstrate the feasibility of automated delivery. Results: The 3D scanner measured the dimension of the 14 cm cubic phantom within 0.5 mm. The maximal absolute discrepancy between machine and model measurements for gantry-to-couch and gantry-to-phantom was 0.95 and 2.97 cm, respectively. The reduced accuracy of gantry-to-phantom measurements was attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries. [ABSTRACT FROM AUTHOR]

Purpose: The advent of automated beam orientation and fluence optimization enables more complex intensity modulated radiation therapy (IMRT) planning using an increasing number of fields to exploit the expanded solution space. This has created a challenge in converting complex fluences to robust multileaf collimator (MLC) segments for delivery. A novel method to regularize the fluence map and simplify MLC segments is introduced to maximize delivery efficiency, accuracy, and plan quality. Methods: In this work, we implemented a novel approach to regularize optimized fluences in the dose domain. The treatment planning problem was formulated in an optimization framework to minimize the segmentation-induced dose distribution degradation subject to a total variation regularization to encourage piecewise smoothness in fluence maps. The optimization problem was solved using a first-order primal-dual algorithm known as the Chambolle-Pock algorithm. Plans for 2 GBM, 2 head and neck, and 2 lung patients were created using 20 automatically selected and optimized noncoplanar beams. The fluence was first regularized using Chambolle-Pock and then stratified into equal steps, and the MLC segments were calculated using a previously described level reducing method. Isolated apertures with sizes smaller than preset thresholds of 1-3 bixels, which are square units of an IMRT fluence map from MLC discretization, were removed from the MLC segments. Performance of the dose domain regularized (DDR) fluences was compared to direct stratification and direct MLC segmentation (DMS) of the fluences using level reduction without dose domain fluence regularization. Results: For all six cases, the DDR method increased the average planning target volume dose homogeneity (D95/D5) from 0.814 to 0.878 while maintaining equivalent dose to organs at risk (OARs). Regularized fluences were more robust to MLC sequencing, particularly to the stratification and small aperture removal. The maximum and mean aperture sizes using the DDR were consistently larger than those from DMS for all tested number of segments. Conclusions: The fluence map to MLC segmentation conversion problem was formulated as a secondary optimization problem in the dose domain to minimize the smoothness-regularized dose discrepancy. The large scale optimization problem was solved using a primal-dual algorithm that transformed complicated fluences into maps that were more robust to the MLC segmentation and sequencing, affording fewer and larger segments with minimal degradation to dose distribution. [ABSTRACT FROM AUTHOR]

Background To evaluate the effect of phacoemulsification on trabeculectomy function. Design Retrospective case-control study. Participants Forty-eight patients who underwent trabeculectomy surgery and had at least 2 years of follow up. Methods Patients were classified into two groups: patients who had phacoemulsification subsequent to trabeculectomy ( Trab_phaco, n = 18) and patients who were pseudophakic for greater than 6 months preceding their trabeculectomy ( Phaco_trab, n = 30). Groups were matched for length of follow up of 2 years from time of trabeculectomy. Main Outcome Measures The primary outcome measures were target intraocular pressure of criteria A, ≤12 mm Hg; B, ≤15 mm Hg; C, ≤18 mm Hg with or without additional topical treatment. A separate measure for bleb function failure was also used; with failure defined as the need for additional topical antiglaucoma therapy or surgical intervention to achieve control of intraocular pressure. Results There was no significant difference in achieving each intraocular pressure criterion between groups (12 months, P = 1.0; 24 months, P = 0.330). In the first 12 months, significantly more trabeculectomies in the Trab_phaco group failed, requiring additional intervention to control the IOP (39%) compared with the Phaco_trab (10%) group ( P = 0.028). Although this trend continued at 24 months, there were no significant differences in failure rates ( P = 0.522). Conclusions Phacoemulsification performed after trabeculectomy significantly increased rates of bleb failure in the following 12 months but not at 24 months. [ABSTRACT FROM AUTHOR]

Background There is increasing emphasis on the importance of intraocular pressure peaks and fluctuations as risk factors for glaucoma progression. It is well recognized that the water drinking test raises intraocular pressure and there is reasonable evidence that caffeine can also raise intraocular pressure. The aim of this study is to directly compare the effect of a caffeine test to that of the water drinking test on intraocular pressure, in patients with glaucoma. Design Prospective, observer-masked, cross-over study. Participants Fourteen eyes of 14 patients with primary open-angle glaucoma. Methods Patients were initially randomized to either caffeine test or water drinking test. Intraocular pressure was measured in both eyes with a Goldmann applanation tonometer at baseline and every 15 min for 1 h, by a masked examiner. This was repeated the following week at the same time of day for the other test (the cross-over). Peak intraocular pressure and maximum fluctuation from baseline were compared between groups using paired t-tests. Main Outcome Measures Peak and fluctuation of intraocular pressure, time of maximum frequency of peak intraocular pressure. Results The maximum intraocular pressure from the water drinking test (19.7 ± 4.1) was greater than the caffeine test (16.7 ± 4.1) and showed greater fluctuation in intraocular pressure (4.3 ± 2.7 mmHg [27.7%]; P < 0.0001) compared with the caffeine test (1.8 ± 1.9 mmHg [11.7%]); P = 0.004). Conclusion The rise in intraocular pressure was greater with water drinking test than the caffeine test. Caffeine does not appear to provide an alternative for patients unable to tolerate the water drinking test. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of using intermediate energy 2 MV x-rays for extracranial robotic intensity modulated radiation therapy. Methods: Two megavolts flattening filter free x-rays were simulated using the Monte Carlo code MCNP (v4c). A convolution/superposition dose calculation program was tuned to match the Monte Carlo calculation. The modeled 2 MV x-rays and actual 6 MV flattened x-rays from existing Varian Linacs were used in integrated beam orientation and fluence optimization for a head and neck, a liver, a lung, and a partial breast treatment. A column generation algorithm was used for the intensity modulation and beam orientation optimization. Identical optimization parameters were applied in three different planning modes for each site: 2, 6 MV, and dual energy 2/6 MV. Results: Excellent agreement was observed between the convolution/superposition and the Monte Carlo calculated percent depth dose profiles. For the patient plans, overall, the 2/6 MV x-ray plans had the best dosimetry followed by 2 MV only and 6 MV only plans. Between the two single energy plans, the PTV coverage was equivalent but 2 MV x-rays improved organs-at-risk sparing. For the head and neck case, the 2MV plan reduced lips, mandible, tongue, oral cavity, brain, larynx, left and right parotid gland mean doses by 14%, 8%, 4%, 14%, 24%, 6%, 30% and 16%, respectively. For the liver case, the 2 MV plan reduced the liver and body mean doses by 17% and 18%, respectively. For the lung case, lung V20, V10, and V5 were reduced by 13%, 25%, and 30%, respectively. V10 of heart with 2 MV plan was reduced by 59%. For the partial breast treatment, the 2 MV plan reduced the mean dose to the ipsilateral and contralateral lungs by 27% and 47%, respectively. The mean body dose was reduced by 16%. Conclusions: The authors showed the feasibility of using flattening filter free 2 MV x-rays for extracranial treatments as evidenced by equivalent or superior dosimetry compared to 6MV plans using the same inverse noncoplanar intensity modulated planning method. [ABSTRACT FROM AUTHOR]

Purpose: Automated planning and delivery of non-coplanar plans such as 4π radiotherapy involving a large number of fields have been developed to take advantage of the newly available automated couch and gantry on C-arm gantry linacs. However, there is an increasing concern regarding the potential changes in the integral dose that needs to be investigated. Methods: A digital torso phantom and 22 lung and liver stereotactic body radiation therapy (SBRT) patients were included in the study. The digital phantom was constructed as a water equivalent elliptical cylinder with a major axis length of 35.4 cm and minor axis of 23.6 cm. A 4.5 cm diameter target was positioned at varying depths along the major axis. Integral doses from intensity modulated, non-coplanar beams forming a conical pattern were compared against the equally spaced coplanar beam plans. Integral dose dependence on the phantom geometry and the beam number was also quantified. For the patient plans, the non-coplanar and coplanar beams and fluences were optimized using a column generation and pricing approach and compared against clinical VMAT plans using two full (lung) or partial coplanar arcs (liver) entering at the side proximal to the tumor. Both the average dose to the normal tissue volume and the total volumes receiving greater than 2 Gy (V2) and 5 Gy (V5) were evaluated and compared. Results: The ratio of integral dose from the non-coplanar and coplanar plans depended on the tumor depth for the phantom; for tumors shallower than 10 cm, the non-coplanar integral doses were lower than coplanar integral doses for non-coplanar angles less than 60°. Similar patterns were observed in the patient plans. The smallest non-coplanar integral doses were observed for tumor 6-8 cm deep. For the phantom, the integral dose was independent of the number of beams, consistent with the liver SBRT patients but the lung SBRT patients showed slight increase in the integral dose when more beams were used. Larger tumor size and larger patient body size did not change the overall relationship of integral doses between non-coplanar and coplanar cases. However, the thin disk-shaped tumor received at least 40% greater integral doses with the non-coplanar plans. Overall, patient non-coplanar integral doses and V5 were comparable to those of coplanar doses from the same optimization engine and 15%-20% lower than state of the art VMAT plans. However, non-coplanar beams significantly increased V2 in both the phantom and patients. On average, the lung and liver SBRT patient normal tissue volumes receiving dose greater than 2 Gy were increased by 749 and 532 cm3, respectively. Conclusions: The authors used a digital phantom simulating a patient torso and 22 SBRT patients to show that the integral doses from the plans employing optimized non-coplanar beams are comparable to those of the coplanar plans using an equal number of discrete beams and are significantly lower than those of VMAT plans. The non-coplanar beams expose a larger normal tissue volume to non-zero doses, whose impact will need to be evaluated individually to determine the risk/benefit ratio of the non-coplanar plans. [ABSTRACT FROM AUTHOR]

Background and Purpose. In pyogenic brain abscess, hemorrhage in the walls is considered exceptional. Recently, hemorrhagic changes in the walls of pyogenic abscess have been demonstrated on susceptibility weighted imaging with 3T MRI. Here, we report hemorrhagic changes in the walls of pyogenic brain abscess on susceptibility weighted imaging with 1.5T MRI. Method. MRI of brain was done using 1.5T MRI with diffusion weighted sequence, susceptibility weighted sequence, and other standard sequences in 3 consecutive cases of pyogenic brain abscess. Stereotactic biopsy and cultures were obtained in 2 cases. One case was treated empirically with antibiotics. Results. Susceptibility sequence demonstrated hemorrhage in the wall of brain abscess in all three cases. All three cases also demonstrated restricted diffusion on diffusion weighted imaging. Conclusion. Susceptibility weighted imaging can demonstrate hemorrhagic changes in the walls of pyogenic brain abscess on 1.5T MRI. Presence of hemorrhage in the walls of ring enhancing lesions should not automatically lead to a diagnosis of tumor. [ABSTRACT FROM AUTHOR]

Aging is associated with impaired fasted oxidation of nonesterified fatty acids ( NEFA) suggesting a mitochondrial defect. Aging is also associated with deficiency of glutathione ( GSH), an important mitochondrial antioxidant, and with insulin resistance. This study tested whether GSH deficiency in aging contributes to impaired mitochondrial NEFA oxidation and insulin resistance, and whether GSH restoration reverses these defects. Three studies were conducted: (i) in 82-week-old C57 BL/6 mice, the effect of naturally occurring GSH deficiency and its restoration on mitochondrial 13C1-palmitate oxidation and glucose metabolism was compared with 22-week-old C57 BL/6 mice; (ii) in 20-week C57 BL/6 mice, the effect of GSH depletion on mitochondrial oxidation of 13C1-palmitate and glucose metabolism was studied; (iii) the effect of GSH deficiency and its restoration on fasted NEFA oxidation and insulin resistance was studied in GSH-deficient elderly humans, and compared with GSH-replete young humans. Chronic GSH deficiency in old mice and elderly humans was associated with decreased fasted mitochondrial NEFA oxidation and insulin resistance, and these defects were reversed with GSH restoration. Acute depletion of GSH in young mice resulted in lower mitochondrial NEFA oxidation, but did not alter glucose metabolism. These data suggest that GSH is a novel regulator of mitochondrial NEFA oxidation and insulin resistance in aging. Chronic GSH deficiency promotes impaired NEFA oxidation and insulin resistance, and GSH restoration reverses these defects. Supplementing diets of elderly humans with cysteine and glycine to correct GSH deficiency could provide significant metabolic benefits. [ABSTRACT FROM AUTHOR]

A bstract Background: The aim of this study is to examine the relationship between sociodemographic factors and utilization of eye care services in patients presenting in acute angle-closure (AAC). Design: A hospital-based retrospective, case-control study. Participants: Fifty-five patients consecutively presenting to the emergency department of the Royal Victorian Eye and Ear Hospital with AAC (cases), and 43 patients consecutively referred to the outpatient department for prophylactic laser peripheral iridotomy (controls) over a 3-year period. Methods: Standardized telephone questionnaires. Main Outcome Measures: Comparisons were made for sociodemographic factors, utilization of eye care services and provision of information on glaucoma and premonitory symptoms of AAC. Results: No significant differences across a range of socioeconomic and demographic factors were found. Fewer cases reported having attended an eye care professional ever ( P = 0.02), or in the 12 months preceding their acute hospital attendance ( P = 0.002), and had less awareness of angle closure glaucoma ( P = 0.001). Logistic regression modelling demonstrated premonitory symptoms of AAC (odds ratio 3.96, [95% confidence interval 1.52-10.32], P < 0.001) and a period of greater than 12 months since the last eye examination (odds ratio 3.89, [95% confidence interval 1.64-9.21]) were significantly associated with the risk of AAC. Conclusions: No significant differences in socioeconomic or demographic parameters between cases and controls were identified. Control subjects had a history of more frequent and recent access to eye care services than cases. The finding that more than one-third of patients presenting with AAC had consulted an eye care provider in the preceding year suggests that a significant proportion of individuals at risk of AAC remain undetected. [ABSTRACT FROM AUTHOR]

A bstract Background: To determine whether postoperative subconjunctival bevacizumab significantly alters bleb vascularity. Design: A randomized, prospective interventional study. Participants: Forty-three eyes from 39 patients were recruited, with 21 eyes randomized to subconjunctival injections of 5-fluorouracil, and 22 eyes to combined 5-fluorouracil/bevacizumab. Methods: All patients who underwent uncomplicated primary antimetabolite augmented trabeculectomy who subsequently required postoperative subconjunctival 5-fluorouracil injection within 4 weeks of surgery were eligible. Patients were randomized to receive subconjunctival 5-fluorouracil only (7.5 mg/0.15 mL) or 5-fluorouracil plus bevacizumab (1.25 mg/0.05 mL). Main Outcome Measures: Primary outcome was bleb vascularity with secondary endpoints including visual acuity, intraocular pressure, bleb morphology, complications and total numbers of 5-fluorouracil injections were recorded at baseline, week 12 and 18 months. Results: At week 12, there was no significant difference between groups for visual acuity, intraocular pressure, bleb vascularity and morphology, or total number of 5-fluorouracil injections. By 18 months, 47.4% of the 5-fluorouracil/bevacizumab group exhibited central bleb avascularity compared with 21.1% of the 5-fluorouracil group (Fisher's exact test, P = 0.17). Two bleb complications (one blebitis; one suture abscess) recorded in the 5-fluorouracil/bevacizumab group. Conclusions: After a single combined injection, a trend for increased central bleb avascularity was observed, although this effect was not sufficient to reach statistical significance. This, in addition to the occurrence of two bleb-related complications in the bevacizumab group, suggests the need for a larger clinical trial to further evaluate the safety and efficacy of bevacizumab as a modulating agent in glaucoma filtration surgery. [ABSTRACT FROM AUTHOR]

Acta Ophthalmol. 2010: 88: 695–699 Purpose: To describe the clinical phenotype in a family with posterior polymorphous corneal dystrophy (PPCD) and a novel mutation in the ZEB1 gene. Methods: Clinical examination, anterior segment photography, specular microscopy and electrophysiological investigations were performed and quantified. Genomic DNA extracted from peripheral blood was sequenced for ZEB1 exons. Cosegregation of identified mutation with the disease status in the family was confirmed using polymerase chain reaction and restriction fragment length polymorphism. Results: Ocular examination was performed on five family members from two generations. Three had anomalies of the corneal endothelium that were consistent with PPCD. Endothelial cell counts ranged from 2306 to 2987 mm2 (ref. 2000–4000 cells/mm2). No evidence of glaucoma or retinal abnormalities was observed. Extraocular abnormalities such as inguinal herniation, hydrocoele and possible bony or connective tissue anomalies were part of the disease spectrum in this family. Mutation analysis revealed a novel change in exon 5 of ZEB1 (c.672delA) that cosegregated with the affected disease status. Conclusion: The detailed clinical features of PPCD associated with a novel ZEB1 mutation are supportive of the previously proposed range of phenotype parameters. Further phenotype–genotype correlations may provide insights into the clinical variability and pathological processes affecting the corneal endothelium, Descemet’s membrane, retinal photoreceptor function and extraocular tissues of some patients. [ABSTRACT FROM AUTHOR]