Chiral dynamics of hadrons in nuclei

In this talk I report on selected topics of hadron modification in the nuclear medium using the chiral unitary approach to describe the dynamics of the problems. I shall mention how antikaons, $\eta$, and $\phi$ are modified in the medium and will report upon different experiments done or planned to measure the $\phi$ width in the medium.Comment: 10 pgs, 3 figs. Invited talk in the Workshop on in Medium Hadron Physics, Giessen, Nov 200

Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement).Comment: accepted for the STACOM2022 workshop @ MICCAI202

Ballistic and diffuse transport through a ferromagnetic domain wall

We study transport through ballistic and diffuse ferromagnetic domain walls in a two-band Stoner model with a rotating magnetization direction. For a ballistic domain wall, the change in the conductance due to the domain wall scattering is obtained from an adiabatic approximation valid when the length of the domain wall is much longer than the Fermi wavelength. In diffuse systems, the change in the resistivity is calculated using a diagrammatic technique to the lowest order in the domain wall scattering and taking into account spin-dependent scattering lifetimes and screening of the domain wall potential.Comment: 9 pages, 3 figures, to appear in Phys. Rev.

Semi-supervised learning of cardiac MRI using image registration

In this work, we propose a method to aid the 2-D segmentation of short-axis cardiac MRI. In particular, the deformation fields obtained during the registration are used to propagate the labels to all time frames, resulting in a weakly supervised segmentation approach that benefits from the features in unlabelled volumes along with the annotated data. Experimental results over the M\&Ms datasets show that the addition of the synthetically obtained labels to the original dataset yields promising results in the performance and improves the capability of the network to generalise to scanners from different vendors

Effects of pseudoscalar-baryon channels in the dynamically generated vector-baryon resonances

We study the interaction of vector mesons with the octet of stable baryons in the framework of the local hidden gauge formalism using a coupled channels unitary approach, including also the pseudoscalar-baryon channels which couple to the same quantum numbers. We examine the scattering amplitudes and their poles, which can be associated to known $J^P=1/2^-,3/2^-$ baryon resonances, and determine the role of the pseudoscalar-baryon channels, changing the width and eventually the mass of the resonances generated with only the basis of vector-baryon states

Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline

In this work, we approach the problem of cardiac Magnetic Resonance Imaging (MRI) image reconstruction from undersampled k-space. This is an inherently ill-posed problem leading to a variety of noise and aliasing artifacts if not appropriately addressed. We propose a two-step double-stream processing pipeline that first reconstructs a noisy sample from the undersampled k-space (frequency domain) using the inverse Fourier transform. Second, in the spatial domain we train a denoising GNA-UNET (enhanced by Group Normalization and Attention layers) on the noisy aliased and fully sampled image data using the Mean Square Error loss function. We achieve competitive results on the leaderboard and show that the algorithmic combination proposed is effective in high-quality MRI reconstruction from undersampled cardiac long-axis and short-axis complex k-space data