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This https://arxiv.org/abs/2306.00530 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction
In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. However, image quality is a crucial factor that influences the accuracy of clinical diagnosis; hence, high-quality image reconstruction from undersampled measurements has been a key area of research. Recently, deep learning (DL) methods have emerged as the state-of-the-art for MRI reconstruction, typically involving deep ne…

X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models
Emmanuelle Bourigault, Abdullah Hamdi, Amir Jamaludin
https://arxiv.org/abs/2404.19604 https://arxiv.org/pdf/2404.19604
arXiv:2404.19604v1 Announce Type: new
Abstract: In this work, we present X-Diffusion, a cross-sectional diffusion model tailored for Magnetic Resonance Imaging (MRI) data. X-Diffusion is capable of generating the entire MRI volume from just a single MRI slice or optionally from few multiple slices, setting new benchmarks in the precision of synthesized MRIs from extremely sparse observations. The uniqueness lies in the novel view-conditional training and inference of X-Diffusion on MRI volumes, allowing for generalized MRI learning. Our evaluations span both brain tumour MRIs from the BRATS dataset and full-body MRIs from the UK Biobank dataset. Utilizing the paired pre-registered Dual-energy X-ray Absorptiometry (DXA) and MRI modalities in the UK Biobank dataset, X-Diffusion is able to generate detailed 3D MRI volume from a single full-body DXA. Remarkably, the resultant MRIs not only stand out in precision on unseen examples (surpassing state-of-the-art results by large margins) but also flawlessly retain essential features of the original MRI, including tumour profiles, spine curvature, brain volume, and beyond. Furthermore, the trained X-Diffusion model on the MRI datasets attains a generalization capacity out-of-domain (e.g. generating knee MRIs even though it is trained on brains). The code is available on the project website https://emmanuelleb985.github.io/XDiffusion/ .

The Lorentz force at work: multi-phase magnetohydrodynamics throughout a flare lifespan
Wenzhi Ruan, Rony Keppens, Limei Yan, Patrick Antolin
https://arxiv.org/abs/2403.19204

The Lorentz force at work: multi-phase magnetohydrodynamics throughout a flare lifespan
The hour-long, gradual phase of solar flares is well-observed across the electromagnetic spectrum, demonstrating many multi-phase aspects, where cold condensations form within the heated post-flare system, but a complete three-dimensional (3D) model is lacking. Using a state-of-the-art 3D magnetohydrodynamic simulation, we identify the key role played by the Lorentz force through the entire flare lifespan, and show that slow variations in the post-flare magnetic field achieve the bulk of the en…

Stellar Surface Magnetic Fields Impact Limb Darkening
Nadiia M. Kostogryz, Alexander I. Shapiro, Veronika Witzke, Robert H. Cameron, Laurent Gizon, Natalie A. Krivova, Hans-G. Ludwig, Pierre F. L. Maxted, Sara Seager, Sami K. Solanki, Jeff Valenti
https://arxiv.org/abs/2403.00118

Stellar Surface Magnetic Fields Impact Limb Darkening
Stars appear darker at their limbs than at their disk centers because at the limb we are viewing the higher and cooler layers of stellar photospheres. Limb darkening derived from state-of-the-art stellar atmosphere models systematically fails to reproduce recent transiting exoplanet light curves from the Kepler, TESS, and JWST telescopes -- stellar brightness obtained from measurements drops less steeply towards the limb than predicted by models. All previous models assumed atmosphere devoid of…

MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging
Chaojie Zhang, Shengjia Chen, Ozkan Cigdem, Haresh Rengaraj Rajamohan, Kyunghyun Cho, Richard Kijowski, Cem M. Deniz
https://arxiv.org/abs/2405.02784

MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates the ImageNet pre-training and captures three-dimensional (3D) spatial correlation from the MR images. The performance of the proposed model was compared to existing state-of-the-art deep learning models for knee injury diagnosis using MRI. Knee MR scans of four different tissue contrasts from the Osteoarthritis Initiati…