arXiv subjects

Gigi Galiana

Publications and source records attributed to Gigi Galiana.

3 recordsLinked to original sources

arXiv2026

Subject-Specific Low-Field MRI Synthesis via a Neural Operator

Low-field (LF) magnetic resonance imaging (MRI) improves accessibility and reduces costs but generally has lower signal-to-noise ratios and degraded contrast compared to high field (HF) MRI, limiting its clinical utility. Simulating LF MRI from HF MRI enables virtual evaluation of novel imaging devices and development of LF algorithms. Existing low field simulators rely on noise injection and smoothing, which fail to capture the contrast degradation seen in LF acquisitions. To this end, we introduce an end-to-end LF-MRI synthesis framework that learns HF to LF image degradation directly from a small number of paired HF-LF MRIs. Specifically, we introduce a novel HF to LF coordinate-image decoupled neural operator (H2LO) to model the underlying degradation process, and tailor it to capture high-frequency noise textures and image structure. Experimental results in T1w and T2w MRI demonstrate that H2LO produces more faithful simulated low-field images than existing parameterized noise synthesis models and popular image-to-image translation models. Furthermore, it improves performance in downstream image enhancement tasks, showcasing its potential to enhance LF MRI diagnostic capabilities.

eess.IV↗

arXiv2021

3D FRONSAC with PSF reconstruction

Purpose: This study extends the Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) method to include 3D acquisitions and reconstructions. It also presents a transform domain reconstruction that makes 3D reconstructions practical and provides new insights into how parallel imaging performance is enhanced by FRONSAC encoding. Methods: This work developed the first examples of FRONSAC incorporated into a 3D acquisition. 3D FRONSAC was tested on human subjects with both simple gradient echo and MPRAGE Cartesian acquisitions. The quality of the 3D FRONSAC images was evaluated using structural similarity index measure (SSIM), and normalized root mean square error (NRMSE). Results: FRONSAC encoding did not significantly modify the contrast obtained in either sequence, but it substantially improves the image quality of undersampled reconstruction. FRONSAC images have reduced undersampling ghosts and consistently improved SSIM and NRMSE. Conclusions: Acquisition and reconstruction of 3D FRONSAC images are feasible, and the additional FRONSAC encoding improves image quality in highly undersampled images.

physics.med-ph↗

arXiv2015

Accelerate Single-shot Data Acquisitions Using Compressed Sensing and FRONSAC Imaging

Nonlinear spatial encoding magnetic (SEM) fields have been studied to complement multichannel RF encoding and accelerate MRI scans. Published schemes include PatLoc, O-Space, Null Space, 4D-RIO, and others, but the large variety of possible approaches to exploiting nonlinear SEMs remains mostly unexplored. Before, we have presented a new approach, Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) imaging, where the nonlinear fields provide a small rotating perturbation to standard linear trajectories. While FRONSAC encoding greatly improves image quality, at the highest accelerations or weakest FRONSAC fields, some undersampling artifacts remain. However, the under-sampling artifacts that occur with FRONSAC encoding are relatively incoherent and well suited to the compressed sensing (CS) reconstruction. CS provides a sparsity-promoting convex strategy to reconstruct images from highly undersampled datasets. The work presented here combines the benefits of FRONSAC and CS. Simulations illustrate that this combination can further improve image reconstruction with FRONSAC gradients of low amplitudes and frequencies.

physics.med-ph↗