51 citations · 388 across the 44 of their papers we have counts for
5 papers · 1 filter
Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi +2
Deep generative models have emerged as state-of-the-art for solving inverse problems, but applying them to inverse problems for PDEs, like electrical impedance tomography (EIT) rem…
DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images
Paolo Angella, Luca Balbi, Fabrizio Ferrando +4
Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Exis…
TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
Tatiana A. Bubba, Matteo Santacesaria, Andrea Sebastiani
Deep learning has emerged as a powerful tool for solving inverse problems in imaging, including computed tomography (CT). However, most approaches require paired training data with…
Assessing the use of Diffusion models for motion artifact correction in brain MRI
Paolo Angella, Vito Paolo Pastore, Matteo Santacesaria
Magnetic Resonance Imaging generally requires long exposure times, while being sensitive to patient motion, resulting in artifacts in the acquired images, which may hinder their di…
Classification of stroke using Neural Networks in Electrical Impedance Tomography
Juan Pablo Agnelli, Aynur Çöl, Matti Lassas +3
Electrical Impedance Tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting vol…