Paired conditional generative adversarial network for highly accelerated liver 4D MRI.

Purpose.4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.Methods.Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3×, 6× and 10×) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer (RST), were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D +t) as temporal slices (2D +t). A total of 48 patients with 12 332 temporal slices were split into training (37 patients with 10 721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results.Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15, 0.16, and 120 s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3× Re-Con-GAN 80.98%; 3× CS 80.74%; 3× UNet 79.88%) of unprocessed under-sampled images (3× 69.61%).Conclusion.A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm - Deep Match. METHOD: Deep Match consists of four self-definable stages - training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10-15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). RESULTS: On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. CONCLUSION: Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Dosimetric predictors for genitourinary toxicity in MR-guided stereotactic body radiation therapy (SBRT): Substructure with fraction-wise analysis.

BACKGROUND: MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients. PURPOSE: (1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the trigone and urethra. METHODS: Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall, trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set. RESULTS: IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy), trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative trigone 15.9  and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance. CONCLUSION: Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.

Breaking Local Charge Symmetry of Iron Single Atoms for Efficient Electrocatalytic Nitrate Reduction to Ammonia.

The electrochemical conversion of nitrate pollutants into value-added ammonia is a feasible way to achieve artificial nitrogen cycle. However, the development of electrocatalytic nitrate-to-ammonia reduction reaction (NO3 - RR) has been hampered by high overpotential and low Faradaic efficiency. Here we develop an iron single-atom catalyst coordinated with nitrogen and phosphorus on hollow carbon polyhedron (denoted as Fe-N/P-C) as a NO3 - RR electrocatalyst. Owing to the tuning effect of phosphorus atoms on breaking local charge symmetry of the single-Fe-atom catalyst, it facilitates the adsorption of nitrate ions and enrichment of some key reaction intermediates during the NO3 - RR process. The Fe-N/P-C catalyst exhibits 90.3 % ammonia Faradaic efficiency with a yield rate of 17980 µg h-1 mgcat -1 , greatly outperforming the reported Fe-based catalysts. Furthermore, operando SR-FTIR spectroscopy measurements reveal the reaction pathway based on key intermediates observed under different applied potentials and reaction durations. Density functional theory calculations demonstrate that the optimized free energy of NO3 - RR intermediates is ascribed to the asymmetric atomic interface configuration, which achieves the optimal electron density distribution. This work demonstrates the critical role of atomic-level precision modulation by heteroatom doping for the NO3 - RR, providing an effective strategy for improving the catalytic performance of single atom catalysts in different electrochemical reactions.

Asymmetric dinitrogen-coordinated nickel single-atomic sites for efficient CO2 electroreduction.

Developing highly efficient, selective and low-overpotential electrocatalysts for carbon dioxide (CO2) reduction is crucial. This study reports an efficient Ni single-atom catalyst coordinated with pyrrolic nitrogen and pyridinic nitrogen for CO2 reduction to carbon monoxide (CO). In flow cell experiments, the catalyst achieves a CO partial current density of 20.1 mA cmgeo-2 at -0.15 V vs. reversible hydrogen electrode (VRHE). It exhibits a high turnover frequency of over 274,000 site-1 h-1 at -1.0 VRHE and maintains high Faradaic efficiency of CO (FECO) exceeding 90% within -0.15 to -0.9 VRHE. Operando synchrotron-based infrared and X-ray absorption spectra, and theoretical calculations reveal that mono CO-adsorbed Ni single sites formed during electrochemical processes contribute to the balance between key intermediates formation and CO desorption, providing insights into the catalyst's origin of catalytic activity. Overall, this work presents a Ni single-atom catalyst with good selectivity and activity for CO2 reduction while shedding light on its underlying mechanism.

Fixed Beamline Optimization for Intensity Modulated Carbon-Ion Therapy.

A major obstacle for the adoption of heavy ion therapy is the cost and technical difficulties to construct and maintain a rotational gantry. Many heavy ion treatment facilities instead choose to construct fixed beamlines as a compromise, which we propose to mitigate with optimized treatment couch angle. We formulate the integrated beam orientation and scanning spot optimization problem as a quadratic cost function with a group sparsity regularization term. The optimization problem is efficiently solved using fast iterative shrinkage-thresholding algorithm (FISTA). To test the method, we created the fixed beamline plans with couch rotation (FBCR) and without couch rotation (FB) for intensity modulated carbon-ion therapy (IMCT) and compared with the ideal scenario where both the couch and gantry have 360 degrees of freedom (GCR). FB, FBCR, and GCR IMCT plans were compared for ten pancreas cases. The FBCR plans show comparable PTV coverage and OAR doses for each pancreas case. In conclusion, the dosimetric limitation of fixed beams in heavy ion radiotherapy may be largely mitigated with integrated beam orientation optimization of the couch rotation.

Reversible Al Metal Anodes Enabled by Amorphization for Aqueous Aluminum Batteries.

Aqueous aluminum metal batteries (AMBs) are regarded as one of the most sustainable energy storage systems among post-lithium-ion candidates, which is attributable to their highest theoretical volumetric capacity, inherent safe operation, and low cost. Yet, the development of aqueous AMBs is plagued by the incapable aluminum plating in an aqueous solution and severe parasitic reactions, which results in the limited discharge voltage, thus making the development of aqueous AMBs unsuccessful so far. Here, we demonstrate that amorphization is an effective strategy to tackle these critical issues of a metallic Al anode by shifting the reduction potential for Al deposition. The amorphous aluminum (a-Al) interfacial layer is triggered by an in situ lithium-ion alloying/dealloying process on a metallic Al substrate with low strength. Unveiled by experimental and theoretical investigations, the amorphous structure greatly lowers the Al nucleation energy barrier, which forces the Al deposition competitive to the electron-stealing hydrogen evolution reaction (HER). Simultaneously, the inhibited HER mitigates the passivation, promoting interfacial ion transfer kinetics and enabling steady aluminum plating/stripping for 800 h in the symmetric cell. The resultant multiple full cells using Al@a-Al anodes deliver approximately a 0.6 V increase in the discharge voltage plateau compared to that of bare Al-based cells, which far outperform all reported aqueous AMBs. In both symmetric cells and full cells, the excellent electrochemical performances are achieved in a noncorrosive, low-cost, and fluorine-free Al2(SO4)3 electrolyte, which is ecofriendly and can be easily adapted for sustainable large-scale applications. This work brings an intriguing picture of the design of metallic anodes for reversible and high-voltage AMBs.

Ferromagnetism in CVT grown tungsten diselenide single crystals with nickel doping.

Two dimensional (2D) single crystal layered transition materials have had extensive consideration owing to their interesting magnetic properties, originating from their lattices and strong spin-orbit coupling, which make them of vital importance for spintronic applications. Herein, we present synthesis of a highly crystalline tungsten diselenide layered single crystal grown by chemical vapor transport technique and doped with nickel (Ni) to tailor its magnetic properties. The pristine WSe2 single crystal and Ni-doped crystal were characterized and analyzed for magnetic properties using both experimental and computational aspects. It was found that the magnetic behavior of the 2D layered WSe2 crystal changed from diamagnetic to ferromagnetic after Ni-doping at all tested temperatures. Moreover, first principle density functional theory (DFT) calculations further confirmed the origin of room temperature ferromagnetism of Ni-doped WSe2, where the d-orbitals of the doped Ni atom promoted the spin moment and thus largely contributed to the magnetism change in the 2D layered material.