Potential of Progressive and Disruptive Innovation-Driven Cost Reductions of Green Hydrogen Production.

Green hydrogen from water electrolysis is a key driver for energy and industrial decarbonization. The prediction of the future green hydrogen cost reduction is required for investment and policy-making purposes but is complicated due to a lack of data, incomplete accounting for costs, and difficulty justifying trend predictions. A new AI-assisted data-driven prediction model is developed for an in-depth analysis of the current and future levelized costs of green hydrogen, driven by both progressive and disruptive innovations. The model uses natural language processing to gather data and generate trends for the technological development of key aspects of electrolyzer technology. Through an uncertainty analysis, green hydrogen costs have been shown to likely reach the key target of <$2.5 kg-1 by 2030 via progressive innovations, and beyond this point, disruptive technological developments are required to affect significantly further decease cost. Additionally, the global distribution of green hydrogen costs has been calculated. This work creates a comprehensive analysis of the levelized cost of green hydrogen, including the important balance of plant components, both now and as electrolyzer technology develops, and offers a likely prediction for how the costs will develop over time.

A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac.

PURPOSE: This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits. METHODS: The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated. RESULTS: The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102. CONCLUSION: The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.

Radio-luminescent imaging for rapid, high-resolution eye plaque loading verification.

BACKGROUND: Eye plaque brachytherapy is currently an optimal therapy for intraocular cancers. Due to the lack of an effective and practical technique to measure the seed radioactivity distribution, current quality assurance (QA) practice according to the American Association of Physicists in Medicine TG129 only stipulates that the plaque assembly be visually inspected. Consequently, uniform seed activity is routinely adopted to avoid possible loading mistakes of differential seed loading. However, modulated dose delivery, which represents a general trend in radiotherapy to provide more personalized treatment for a given tumor and patient, requires differential activities in the loaded seeds. PURPOSE: In this study, a fast and low-cost radio-luminescent imaging and dose calculating system to verify the seed activity distribution for differential loading was developed. METHODS: A proof-of-concept system consisting of a thin scintillator sheet coupled to a camera/lens system was constructed. A seed-loaded plaque can be placed directly on the scintillator surface with the radioactive seeds facing the scintillator. The camera system collects the radioluminescent signal generated by the scintillator on its opposite side. The predicted dose distribution in the scintillator's sensitive layer was calculated using a Monte Carlo simulation with the planned plaque loading pattern of I-125 seeds. Quantitative comparisons of the distribution of relative measured signal intensity and that of the relative predicted dose in the sensitive layer were performed by gamma analysis, similar to intensity-modulated radiation therapy QA. RESULTS: Data analyses showed high gamma (3%/0.3 mm, global, 20% threshold) passing rates for correct seed loadings and low passing rates with distinguished high gamma value area for incorrect loadings, indicating that possible errors may be detected. The measurement and analysis only required a few extra minutes, significantly shorter than the time to assay the extra verification seeds the physicist already must perform as recommended by TG129. CONCLUSIONS: Radio-luminescent QA can be used to facilitate and assure the implementation of intensity-modulated, customized plaque loading.

Construction of Confined Bifunctional 2D Material for Efficient Sulfur Resource Recovery and Hg2+ Adsorption in Desulfurization.

Substantial energy penalty of valuable sulfate recovery restricts the efficiency of wet desulfurization and increases the risk of Hg0 reemission. Although the enhanced sulfite oxidation rate with cobalt-based materials can increase the energy efficiency, inactivation and poisoning of catalyst due to the competition of reactant must be addressed. Here we obtained a superwetting two-dimensional cobalt-nitrogen-doped carbon (2D Co-N-C) nanosheet featuring confined catalysis/adsorption sites for the energy-efficient sulfite oxidation and Hg2+ adsorption. The designed structure exhibits enhanced surface polarity, availability and short reactant diffusion path, thus enabling the significant catalytic TOF value of 0.085 s-1 and simultaneous mercury removal ability of 143.26 mg·g-1. The catalyst nanosheets present regenerating stabilities to improve cost-efficiency. By deployment of the Co-N-C catalysts, a marked reduction of heat penalty up to 69% can be achieved, which makes this catalytic pathway for sulfur resource recovery economically feasible in real industry scenario.

Small-field measurement and Monte Carlo model validation of a novel image-guided radiotherapy system.

PURPOSE: The RefleXion™ X1 is a novel radiotherapy system that is designed for image-guided radiotherapy, and eventually, biology-guided radiotherapy (BgRT). BgRT is a treatment paradigm that tracks tumor motion using real-time positron emission signals. This study reports the small-field measurement results and the validation of a Monte Carlo (MC) model of the first clinical RefleXion unit. METHODS: The RefleXion linear accelerator (linac) produces a 6 MV flattening filter free (FFF) photon beam and consists of a binary multileaf collimator (MLC) system with 64 leaves and two pairs of y-jaws. The maximum clinical field size achievable is 400 × 20 mm2 . The y-jaws provide either a 10 or 20 mm opening at source-to-axis distance (SAD) of 850 mm. The width of each MLC leaf at SAD is 6.25 mm. Percentage depth doses (PDDs) and relative beam profiles were acquired using an Edge diode detector in a water tank for field sizes from 12.5 × 10 to 100 × 20 mm2 . Beam profiles were also measured using films. Output factors of fields ranging from 6.25 × 10 to 100 × 20 mm2 were measured using W2 scintillator detector, Edge detector, and films. Output correction factors k of the Edge detector for RefleXion were calculated. An MC model of the linac including pre-MLC beam sources and detailed structures of MLC and lower y-jaws was validated against the measurements. Simulation codes BEAMnrc and GATE were utilized. RESULTS: The diode measured PDD at 10 cm depth (PDD10) increases from 53.6% to 56.9% as the field opens from 12.5 × 10 to 100 × 20 mm2 . The W2-measured output factor increases from 0.706 to 1 as the field opens from 6.25 × 10 to 100 × 20 mm2 (reference field size). The output factors acquired by diode and film differ from the W2 results by 1.65% (std = 1.49%) and 2.09% (std = 1.41%) on average, respectively. The profile penumbra and full-width half-maximum (FWHM) measured by diode agree well with the film results with a deviation of 0.60 mm and 0.73% on average, respectively. The averaged beam profile consistency calculated between the diode- and film-measured profiles among different depths is within 1.72%. By taking the W2 measurements as the ground truth, the output correction factors k for Edge detector ranging from 0.958 to 1 were reported. For the MC model validation, the simulated PDD10 agreed within 0.6% to the diode measurement. The MC-simulated output factor differed from the W2 results by 2.3% on average (std = 3.7%), while the MC simulated beam penumbra differed from the diode results by 0.67 mm on average (std = 0.42 mm). The MC FWHM agreed with the diode results to within 1.40% on average. The averaged beam profile consistency calculated between the diode and MC profiles among different depths is less than 1.29%. CONCLUSIONS: This study represents the first small-field dosimetry of a clinical RefleXion system. A complete and accurate MC model of the RefleXion linac has been validated.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 s with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.

Modified fast adaptive scatter kernel superposition (mfASKS) correction and its dosimetric impact on CBCT-based proton therapy dose calculation.

PURPOSE: While cone beam computed tomography (CBCT) is able to provide patient anatomical information, its image quality is severely degraded due to scatter contamination, which degrades the accuracy of CBCT-based dose distribution estimation in proton therapy. In this work, we combined two existing scatter kernel correction methods: the point-spread function (PSF)-based scatter kernel derivation method and the fast adaptive scatter kernel superposition (fASKS) model, and evaluated the impact of the modified fASKS (mfASKS) correction on the accuracy of proton dose distribution estimation. To evaluate feasibility of the mfASKS approach using accurate scatter distributions, both Monte Carlo simulations and experiments were performed for an on-board CBCT machine integrated with a proton therapy machine. METHODS: We developed a strategy to modify central intensity, constant intensity, and amplitude of the scatter kernels derived from PSFs for the fASKS model. A parameter required for the fASKS model was derived by optimizing uniformity in the mfASKS-corrected reconstructed images. Subsequently, the mfASKS model was used to remove scatter in CBCT imaging. We quantitatively compared the Hounsfield Unit (HU) and proton stopping power ratio (SPR) images for five different phantoms. To assess improvement of dose calculation accuracy, a series of proton treatment plans were produced using the CBCT images with and without the mfASKS correction. RESULTS: The accuracies of both HU and SPR intensity quantifications are improved as a result of the mfASKS correction. Mean absolute water-equivalent path length difference to the true value decreases from 10.3 to 0.934 mm for the Gammex phantom (simulation). At the same time, mfASKS is able to offer more accurate dose distributions, especially at the distal fall-off region where noticeable dose overestimation is observed in the uncorrected scenario. Mean absolute relative error of proton range in the pelvic phantom improves from 5.03% to 2.57% (experiment). CONCLUSIONS: mfASKS enables more accurate CBCT-based proton dose calculation. This technique has significant implications in image-guided radiotherapy and dose verifications in adaptive proton therapy.

Copy number variation (CNV) in the IGF1R gene across four cattle breeds and its association with economic traits.

The insulin-like growth factor 1 receptor (IGF1R) plays a vital role in immunomodulation and muscle and bone growth. The copy number variation (CNV) is believed to the reason for many complex phenotypic variations. In this paper, we statistically analyzed the copy number and the expression profiling in different tissue types of the IGF1R gene using the 422 samples from four Chinese beef cattle breeds, and the mRNA of IGF1R was widely expressed in nine tissue types of adult cattle (heart, liver, kidney, muscle, fat, stomach, spleen, lung and testis). Results of CNV and growth traits indicated that the IGF1R CNV was significantly associated with body weight and body height of Jinnan (JN) cattle and was significantly associated with body height and hucklebone width of Qinchuan (QC) cattle, making IGF1R CNV a promising molecular marker to improve meat production in beef cattle breeding. Bioinformatics predictions show that the CNV region is highly similar to the human genome, and there are a large number of transcription factors, DNase I hypersensitive sites, and high levels of histone acetylation, suggesting that this region may play a role in transcriptional regulation, providing directions for further study of the role of bovine CNV and economic traits.

Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the presence of noise, machine learning approaches were proposed to establish their relationship. MATERIALS/METHODS: Simulations were carried out with a spot-scanning proton system using GATE-8.0 and Geant4-10.3 toolkit with a computed tomography (CT)-based patient phantom. The one-dimensional (1D) distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising two hidden layers, was developed to estimate whether the range is within a preset threshold. A recurrent neural network (RNN) regression model comprising three layers and ten neurons in each hidden layer was developed to estimate dose distribution. The performance was quantitatively studied in terms of mean squared error (MSE) and mean absolute error (MAE) under different signal-to-noise ratio (SNR) values. RESULTS: The feasibility of proton range and dose verification using the proposed neural network framework was demonstrated. The feedforward NN model achieves high classification accuracy close to 100% for individual classes without bias. The RNN model is able to accurately predict the 1D dose distribution for different energies and irradiation positions. When the SNR of the input activity profiles is above 4, the framework is able to predict with an MAE of ~0.60 mm and an MSE of ~0.066. Moreover, the model demonstrates a good capability of generalization. CONCLUSIONS: The RNN model is found to be effective in identifying the relationship between the distributions of dose and positron emitters. The machine learning-based framework and RNN models may be a useful tool to allow for accurate online range and dose verification based on proton-induced positron emitters.

Simulation studies of time reversal-based protoacoustic reconstruction for range and dose verification in proton therapy.

PURPOSE: In vivo range verification in proton therapy is a critical step to help minimize range and dose uncertainty. We propose to employ a time reversal (TR)-based approach using proton-induced acoustics (protoacoustics) to reconstruct pressure/dose distribution in heterogeneous tissues. METHODS: The dose distribution of mono-energetic proton pencil beam in a CT-based patient phantom was calculated by Monte Carlo simulation. K-wave toolbox was used to investigate protoacoustic pressurization, propagation and reconstruction in 2D. To address the tissue heterogeneity effect, a number of physical parameters, including mass density (ρ), speed of sound (c), volumetric thermal expansion coefficient (αV ), isobaric specific heat capacity (Cp ) and attenuation power law prefactor (α0 ), were empirically converted from CT number. The performance was evaluated using two figures of merit: mean square error (MSE) of pressure profiles and Bragg peak localization error (ΔBP ). The impact of six parameters of the TR inversion was examined, including number of sensors, sampling duration, sampling timestep, spill time, noise level and number of iterations. RESULTS: The quantitative accuracy of TR reconstruction and its dependency on the selected parameters is presented. Under optimum conditions, the positioning accuracy of the Bragg peak can be controlled below 1 mm. For instance, MSE is 0.0123 and ΔBP is 0.59 mm under the following conditions (32 sensors, sampling duration: 600 µs, sampling timestep: 40 ns, spill time: 1 µs, no noise). CONCLUSIONS: The feasibility of TR-based protoacoustic reconstruction in 2D for proton range verification was first demonstrated. The approach is not only applicable to pencil beam, but also has potential to be extended to passive scattering systems.

Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.

PURPOSE: Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatter removal. This study aims to develop an effective scatter correction method using a residual convolutional neural network (CNN). METHODS: A U-net based 25-layer CNN was constructed for CBCT scatter correction. The establishment of the model consists of three steps: model training, validation, and testing. For model training, a total of 1800 pairs of x-ray projection and the corresponding scatter-only distribution in nonanthropomorphic phantoms taken in full-fan scan were generated using Monte Carlo simulation of a CBCT scanner installed with a proton therapy system. An end-to-end CNN training was implemented with two major loss functions for 100 epochs with a mini-batch size of 10. Image rotations and flips were randomly applied to augment the training datasets during training. For validation, 200 projections of a digital head phantom were collected. The proposed CNN-based method was compared to a conventional projection-domain scatter correction method named fast adaptive scatter kernel superposition (fASKS) method using 360 projections of an anthropomorphic head phantom. Two different loss functions were applied for the same CNN to evaluate the impact of loss functions on the final results. Furthermore, the CNN model trained with full-fan projections was fine-tuned for scatter correction in half-fan scan by using transfer learning with additional 360 half-fan projection pairs of nonanthropomorphic phantoms. The tuned-CNN model for half-fan scan was compared with the fASKS method as well as the CNN-based method without the fine-tuning using additional lung phantom projections. RESULTS: The CNN-based method provides projections with significantly reduced scatter and CBCT images with more accurate Hounsfield Units (HUs) than that of the fASKS-based method. Root mean squared error of the CNN-corrected projections was improved to 0.0862 compared to 0.278 for uncorrected projections or 0.117 for the fASKS-corrected projections. The CNN-corrected reconstruction provided better HU quantification, especially in regions near the air or bone interfaces. All four image quality measures, which include mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), indicated that the CNN-corrected images were significantly better than that of the fASKS-corrected images. Moreover, the proposed transfer learning technique made it possible for the CNN model trained with full-fan projections to be applicable to remove scatters in half-fan projections after fine-tuning with only a small number of additional half-fan training datasets. SSIM value of the tuned-CNN-corrected images was 0.9993 compared to 0.9984 for the non-tuned-CNN-corrected images or 0.9990 for the fASKS-corrected images. Finally, the CNN-based method is computationally efficient - the correction time for the 360 projections only took less than 5 s in the reported experiments on a PC (4.20 GHz Intel Core-i7 CPU) with a single NVIDIA GTX 1070 GPU. CONCLUSIONS: The proposed deep learning-based method provides an effective tool for CBCT scatter correction and holds significant value for quantitative imaging and image-guided radiation therapy.

Generalized Adaptive Gaussian Markov Random Field for X-Ray Luminescence Computed Tomography.

OBJECTIVE: X-ray luminescence computed tomography (XLCT) is an emerging and promising modality, but suffers from inferior reconstructions and smoothed target shapes. This work aims to improve the image quality with new mathematical framework. METHODS: We present a Bayesian local regularization framework to tackle the ill-conditioness of XLCT. Different from traditional overall regularization strategies, the proposed method utilizes correlations of neighboring voxels to regularize the solution locally based on generalized adaptive Gaussian Markov random field (GAGMRF), and provides an adjustable parameter to facilitate the edge-preserving property. RESULTS: Numerical simulations and phantom experiments show that the GAGMRF method yields both high image quality and accurate target shapes. CONCLUSION: Compared to conventional L2 and L1 regularizations, GAGMRF provides a new and efficient model for high quality imaging based on the Bayesian framework. SIGNIFICANCE: The GAGMRF method offers a flexible regularization framework to adapt to a wide range of biomedical applications.

Differences in coronary plaque morphology between East Asian and Western White patients: an optical coherence tomography study.

AIMS: Prevalence of coronary artery disease as well as cardiac mortality varies between Asian and White patients. However, the link between race and plaque characteristics in patients with coronary artery disease remains largely unexplored. Thus, we aimed to investigate the detailed culprit plaque characteristics between East Asian and White patients using optical coherence tomography. PATIENTS AND METHODS: A total of 101 East Asians were matched to 101 White patients. Matching parameters included age, sex, clinical presentation, hyperlipidemia, diabetes mellitus, and lesion location. RESULTS: There were no differences in underlying pathology (rupture vs. erosion) of acute coronary syndrome (P=0.935). Lesion length was longer (18.0±6.0 vs. 14.6±5.4 mm; P<0.002), lipid length was greater (9.4±4.6 vs. 7.2±3.8 mm; P<0.023), lipid index was higher (1635±987 vs. 1104±730; P=0.002), and mean reference area was larger (8.1±3.0 vs. 6.5±2.4 mm; P<0.021) in White patients compared with East Asian patients. CONCLUSION: There are significant differences in plaque morphology between East Asian and White patients even after controlling for confounders. Our findings underscore key differences in atherosclerosis between East Asian and White populations, and may have to be taken into consideration when interpreting the results of future research.

Monte Carlo tree search -based non-coplanar trajectory design for station parameter optimized radiation therapy (SPORT).

An important yet challenging problem in LINAC-based rotational arc radiation therapy is the design of beam trajectory, which requires simultaneous consideration of delivery efficiency and final dose distribution. In this work, we propose a novel trajectory selection strategy by developing a Monte Carlo tree search (MCTS) algorithm during the beam trajectory selection process. To search through the vast number of possible trajectories, the MCTS algorithm was implemented. In this approach, a candidate trajectory is explored by starting from a leaf node and sequentially examining the next level of linked nodes with consideration of geometric and physical constraints. The maximum Upper Confidence Bounds for Trees, which is a function of average objective function value and the number of times the node under testing has been visited, was employed to intelligently select the trajectory. For each candidate trajectory, we run an inverse fluence map optimization with an infinity norm regularization. The ranking of the plan as measured by the corresponding objective function value was then fed back to update the statistics of the nodes on the trajectory. The method was evaluated with a chest wall and a brain case, and the results were compared with the coplanar and noncoplanar 4pi beam configurations. For both clinical cases, the MCTS method found effective and easy-to-deliver trajectories within an hour. As compared with the coplanar plans, it offers much better sparing of the OARs while maintaining the PTV coverage. The quality of the MCTS-generated plan is found to be comparable to the 4pi plans. Artificial intelligence based on MCTS is valuable to facilitate the design of beam trajectory and paves the way for future clinical use of non-coplanar treatment delivery.

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS: Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points, and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remove cavities of the component, which resulted in segmentation of the OAR in the test image. RESULTS: The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves, and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes, and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm. CONCLUSION: We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.

Lipid-lowering therapy stabilizes the complexity of non-culprit plaques in human coronary artery: a quantitative assessment using OCT bright spot algorithm.

To quantitatively evaluate the change of plaque complexity with cholesterol lowering therapy. A total of 44 non-culprit plaques from 30 patients who had serial image acquisition at baseline, 6-months, and 12-months by both optical coherence tomography (OCT) and intravascular ultrasound (IVUS) were included. Patients were treated with atorvastatin 60 mg (AT60, n = 16) or 20 mg (AT20, n = 14). We applied an OCT bright spot algorithm, which identifies a variety of plaque components including macrophages. The density of bright spot was measured within the superficial 250 µm of the vessel wall. Significant reduction of bright spot density was observed from baseline to 12-months [-0.49% (-0.95, -0.20), p < 0.001], particularly during the second 6 months [first 6 months: -0.01% (-0.57, 0.60), p = 0.939; second 6 months: -0.49% (-0.98, 0.14), p < 0.001]. Although there was no significant difference at 12 months in the reduction of bright spot density between plaques with acute coronary syndrome (ACS, n = 33) and those with stable angina (n = 11) [-0.49% (-0.93, -0.19) vs. -0.39% (-1.01, -0.21), p = 0.748], a significant reduction of bright spot density during the first 6 months was observed only in plaques with ACS. There was no significant difference in the change of bright spot density between the AT60 group (n = 22) and AT20 group (n = 22) [-0.61% (-0.93, -0.34) vs. -0.41% (-0.98, -0.19), p = 0.483]. Coronary plaque complexity evaluated by a quantitative OCT algorithm significantly decreased with 12 month atorvastatin therapy irrespective of the dose and initial clinical presentation.

Feasibility study of Compton cameras for x-ray fluorescence computed tomography with humans.

X-ray fluorescence imaging is a promising imaging technique able to depict the spatial distributions of low amounts of molecular agents in vivo. Currently, the translation of the technique to preclinical and clinical applications is hindered by long scanning times as objects are scanned with flux-limited narrow pencil beams. The study presents a novel imaging approach combining x-ray fluorescence imaging with Compton imaging. Compton cameras leverage the imaging performance of XFCT and abolish the need for pencil beam excitation. The study examines the potential of this new imaging approach on the base of Monte-Carlo simulations. In the work, it is first presented that the particular option of slice/fan-beam x-ray excitation has advantages in image reconstruction in regard of processing time and image quality compared to traditional volumetric Compton imaging. In a second experiment, the feasibility of the approach for clinical applications with tracer agents made from gold nano-particles is examined in a simulated lung scan scenario. The high energy of characteristic x-ray photons from gold is advantageous for deep tissue penetration and has lower angular blurring in the Compton camera. It is found that Doppler broadening in the first detector stage of the Compton camera adds the largest contribution on the angular blurring; physically limiting the spatial resolution. Following the analysis of the results from the spatial resolution test, resolutions in the order of one centimeter are achievable with the approach in the center of the lung. The concept of Compton imaging allows one to distinguish to some extent between scattered photons and x-ray fluorescent photons based on their difference in emission position. The results predict that molecular sensitivities down to 240 pM l-1 for 5 mm diameter lesions at 15 mGy for 50 nm diameter gold nano-particles are achievable. A 45-fold speed up time for data acquisition compared to traditional pencil beam XFCT could be achieved for lung imaging at the cost of a small sensitivity decrease.

A model-based scatter artifacts correction for cone beam CT.

PURPOSE: Due to the increased axial coverage of multislice computed tomography (CT) and the introduction of flat detectors, the size of x-ray illumination fields has grown dramatically, causing an increase in scatter radiation. For CT imaging, scatter is a significant issue that introduces shading artifact, streaks, as well as reduced contrast and Hounsfield Units (HU) accuracy. The purpose of this work is to provide a fast and accurate scatter artifacts correction algorithm for cone beam CT (CBCT) imaging. METHODS: The method starts with an estimation of coarse scatter profiles for a set of CBCT data in either image domain or projection domain. A denoising algorithm designed specifically for Poisson signals is then applied to derive the final scatter distribution. Qualitative and quantitative evaluations using thorax and abdomen phantoms with Monte Carlo (MC) simulations, experimental Catphan phantom data, and in vivo human data acquired for a clinical image guided radiation therapy were performed. Scatter correction in both projection domain and image domain was conducted and the influences of segmentation method, mismatched attenuation coefficients, and spectrum model as well as parameter selection were also investigated. RESULTS: Results show that the proposed algorithm can significantly reduce scatter artifacts and recover the correct HU in either projection domain or image domain. For the MC thorax phantom study, four-components segmentation yields the best results, while the results of three-components segmentation are still acceptable. The parameters (iteration number K and weight ß) affect the accuracy of the scatter correction and the results get improved as K and ß increase. It was found that variations in attenuation coefficient accuracies only slightly impact the performance of the proposed processing. For the Catphan phantom data, the mean value over all pixels in the residual image is reduced from -21.8 to -0.2 HU and 0.7 HU for projection domain and image domain, respectively. The contrast of the in vivo human images is greatly improved after correction. CONCLUSIONS: The software-based technique has a number of advantages, such as high computational efficiency and accuracy, and the capability of performing scatter correction without modifying the clinical workflow (i.e., no extra scan/measurement data are needed) or modifying the imaging hardware. When implemented practically, this should improve the accuracy of CBCT image quantitation and significantly impact CBCT-based interventional procedures and adaptive radiation therapy.

Proton-induced x-ray fluorescence CT imaging.

PURPOSE: To demonstrate the feasibility of proton-induced x-ray fluorescence CT (pXFCT) imaging of gold in a small animal sized object by means of experiments and Monte Carlo (MC) simulations. METHODS: First, proton-induced gold x-ray fluorescence (pXRF) was measured as a function of gold concentration. Vials of 2.2 cm in diameter filled with 0%-5% Au solutions were irradiated with a 220 MeV proton beam and x-ray fluorescence induced by the interaction of protons, and Au was detected with a 3 × 3 mm(2) CdTe detector placed at 90° with respect to the incident proton beam at a distance of 45 cm from the vials. Second, a 7-cm diameter water phantom containing three 2.2-diameter vials with 3%-5% Au solutions was imaged with a 7-mm FWHM 220 MeV proton beam in a first generation CT scanning geometry. X-rays scattered perpendicular to the incident proton beam were acquired with the CdTe detector placed at 45 cm from the phantom positioned on a translation/rotation stage. Twenty one translational steps spaced by 3 mm at each of 36 projection angles spaced by 10° were acquired, and pXFCT images of the phantom were reconstructed with filtered back projection. A simplified geometry of the experimental data acquisition setup was modeled with the MC TOPAS code, and simulation results were compared to the experimental data. RESULTS: A linear relationship between gold pXRF and gold concentration was observed in both experimental and MC simulation data (R(2) > 0.99). All Au vials were apparent in the experimental and simulated pXFCT images. Specifically, the 3% Au vial was detectable in the experimental [contrast-to-noise ratio (CNR) = 5.8] and simulated (CNR = 11.5) pXFCT image. Due to fluorescence x-ray attenuation in the higher concentration vials, the 4% and 5% Au contrast were underestimated by 10% and 15%, respectively, in both the experimental and simulated pXFCT images. CONCLUSIONS: Proton-induced x-ray fluorescence CT imaging of 3%-5% gold solutions in a small animal sized water phantom has been demonstrated for the first time by means of experiments and MC simulations.

Independent calculation of monitor units for VMAT and SPORT.

PURPOSE: Dose and monitor units (MUs) represent two important facets of a radiation therapy treatment. In current practice, verification of a treatment plan is commonly done in dose domain, in which a phantom measurement or forward dose calculation is performed to examine the dosimetric accuracy and the MU settings of a given treatment plan. While it is desirable to verify directly the MU settings, a computational framework for obtaining the MU values from a known dose distribution has yet to be developed. This work presents a strategy to calculate independently the MUs from a given dose distribution of volumetric modulated arc therapy (VMAT) and station parameter optimized radiation therapy (SPORT). METHODS: The dose at a point can be expressed as a sum of contributions from all the station points (or control points). This relationship forms the basis of the proposed MU verification technique. To proceed, the authors first obtain the matrix elements which characterize the dosimetric contribution of the involved station points by computing the doses at a series of voxels, typically on the prescription surface of the VMAT/SPORT treatment plan, with unit MU setting for all the station points. An in-house Monte Carlo (MC) software is used for the dose matrix calculation. The MUs of the station points are then derived by minimizing the least-squares difference between doses computed by the treatment planning system (TPS) and that of the MC for the selected set of voxels on the prescription surface. The technique is applied to 16 clinical cases with a variety of energies, disease sites, and TPS dose calculation algorithms. RESULTS: For all plans except the lung cases with large tissue density inhomogeneity, the independently computed MUs agree with that of TPS to within 2.7% for all the station points. In the dose domain, no significant difference between the MC and Eclipse Anisotropic Analytical Algorithm (AAA) dose distribution is found in terms of isodose contours, dose profiles, gamma index, and dose volume histogram (DVH) for these cases. For the lung cases, the MC-calculated MUs differ significantly from that of the treatment plan computed using AAA. However, the discrepancies are reduced to within 3% when the TPS dose calculation algorithm is switched to a transport equation-based technique (Acuros™). Comparison in the dose domain between the MC and Eclipse AAA/Acuros calculation yields conclusion consistent with the MU calculation. CONCLUSIONS: A computational framework relating the MU and dose domains has been established. The framework does not only enable them to verify the MU values of the involved station points of a VMAT plan directly in the MU domain but also provide a much needed mechanism to adaptively modify the MU values of the station points in accordance to a specific change in the dose domain.