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We report on the magnetic coupling behavior in all-ferromagnetic La0.7Sr0.3MnO3/SrRuO3 (LSMO/SRO) heterostructures deposited on the SrTiO3 (111) substrate, where the interface leads to an enhanced exchange field as compared to the one deposited on SrTiO3 (001). Importantly, high-resolution scanning transmission electron microscopy reveals distinct interface structures of the (111) heterostructures depending on the growth sequence. The heterostructure with an SRO bottom layer shows an atomically flat and abrupt interface, while the one with an LSMO bottom layer shows a deformed interface due to the surface roughening of LSMO deposited directly on SrTiO3 (111). As a result, the heterostructure with an abrupt interface exhibits a robust antiferromagnetic coupling between LSMO and SRO, while the one with a rough interface shows negligible magnetic coupling. Our results demonstrate the key role of an abrupt interface in determining the magnetic properties of oxide heterostructures. [ABSTRACT FROM AUTHOR]

Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. Current TIR image-based 3D reconstruction methods primarily focus on static conditions, which only capture the spatial distribution of thermal radiation but lack the ability to represent its temporal dynamics. The absence of dedicated datasets and effective methods for dynamic 3D representation are two key challenges that hinder progress in this field. To address these challenges, we propose a novel dynamic thermal 3D reconstruction method, named ThermalGS, based on 3D Gaussian Splatting (3DGS). ThermalGS employs a data-driven approach to directly learn both scene structure and dynamic thermal representation, using RGB and TIR images as input. The position, orientation, and scale of Gaussian primitives are guided by the RGB mesh. We introduce feature encoding and embedding networks to integrate semantic and temporal information into the Gaussian primitives, allowing them to capture dynamic thermal radiation characteristics. Moreover, we construct the Thermal Scene Day-and-Night (TSDN) dataset, which includes multi-view, high-resolution aerial RGB reference images and TIR images captured at five different times throughout the day and night, providing a benchmark for dynamic thermal 3D reconstruction tasks. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the TSDN dataset, with an average absolute temperature error of 1 °C and the ability to predict surface temperature variations over time. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real‐time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image‐guided adaptive RT systems, the use of deep learning has been considered.This study proposed a modified generative adversarial network (GAN) for predicting cine‐MR images in real time.Sagittal cine magnetic resonance (cine‐MR) images of 15 patients with liver cancer who received RT were collected. The image series length of each patient was 300, and each series was divided into training, validation, and test sets. The datasets were further divided using a sliding window size of 10 and a stride of 1. A pix2pix GAN with the generator replaced by convolutional long short‐term memory (ConvLSTM) was proposed herein. A five‐frame cine‐MR image series was inputted into the network, which predicted the next five frames. The proposed network was compared with three advanced networks: ConvLSTM, Eidetic 3D LSTM (E3D–LSTM), and SwinLSTM. Personalized models were trained for each patient. The peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), Pearson correlation coefficient (Pearson corr), and respiratory motion accuracy of the predicted images were used to evaluate the methods.The proposed network demonstrated optimal performance in the four networks across various indicators. The proposed method provided better SSIM values than ConvLSTM at time steps 1, 2, 3, and 4, and outperformed E3DLSTM at all time steps. In terms of the VIF, the proposed method outperformed E3D–LSTM at all time steps and SwinLSTM at time steps 2, 3, 4, and 5. The proposed method was not significantly different from other methods in terms of Pearson correlation values except that it outperformed E3DLSTM at time step 1. In terms of the Pearson corr, the proposed method consistently achieves better values, especially in the high‐frequency components. Low average landmark tracking errors were provided by the proposed method at time steps 4 and 5 (2.42 ± 0.91 and 2.44 ± 0.96 mm, respectively).The GAN–ConvLSTM network can generate high‐acutance real‐time cine‐MR images and predict respiratory motion with better accuracy. [ABSTRACT FROM AUTHOR]

Background: The multi‐leaf collimator (MLC) is an advanced device utilized for beam shaping and intensity modulation in radiotherapy. With the framework of the contemporary single‐layer MLC featuring a rounded leaf tip, the leaf tip transmission and leakage exert a considerable influence on radiotherapy. Purpose: To scale down the leakage and transmission from the leaf gap when the opposite leaves are closed. Methods: This study proposes a new design of MLC, named dynamic leaf machine (DLM), specifically engineered to diminish the transmission and leakage from the MLC leaf tip. The DLM incorporates an innovative leaf configuration that involves a combination of fixed and movable layers in a single MLC leaf named fixed and movable (FM) layers leaf, which is advantageous in dealing with transmission and leakage from the leaf tip by employing a staggered arrangement of the fixed and movable layer when the opposite leaves are closed. Subsequently, the DLM is assembled on the Elekta VERSA HD accelerator to assess its performance, focusing on aspects of mechanical characters, transmission, leakage, and penumbra. Results: In comparison to conventional MLC leaves, the FM leaf design in the DLM has achieved a remarkable reduction in the leakage between opposed closed leaves, decreasing it from 48.41% to 0.41%. Additionally, the transmission between the adjacent leaves has been measured at 0.59%, and the penumbra in 16 mm × 32 mm field is 2.82 mm, aligning with the performance of conventional MLC. Conclusions: The DLM with FM leaf performs a significant reduction in transmission from the opposite leaf tip in comparison to conventional MLC while maintaining minimal penumbra, effectively mitigating the transmission and leakage problem between opposing leaves, thereby enhancing the effect of radiotherapy. [ABSTRACT FROM AUTHOR]