Browsing by Author "Nezhad, Valiyeh Ansarian"

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Open Access
Generalizable deep mri reconstruction with cross-site data synthesis
(IEEE, 2024-06-23) Nezhad, Valiyeh Ansarian; Elmas, Gökberk; Arslan, Fuat; Kabas, Bilal; Çukur, Tolga
Deep learning techniques have enabled leaps in MRI reconstruction from undersampled acquisitions. While they yields high performance when tested on data from sites that the training data originates, they suffer from performance losses when tested on separate sites. In this work, we proposed a novel learning technique to improve generalization in deep MRI reconstruction. The proposed method employs cross-site data synthesis to benefit from multi-site data without introducing patient privacy risks. First, MRI priors are captured via generative adversarial models trained at each site independently. These priors are shared across sites, and then used to synthesize data from multiple sites. Afterwards, MRI reconstruction models are trained using these synthetic data. Experiments indicate that the proposed method attains higher generalization against single-site models, and higher site-specific performance against site-average models.
Open Access
Multi-contrast mr image synthesis with a brownian diffusion model
(IEEE, 2024-12-05) Kabaş, Bilal; Arslan, Fuat; Nezhad, Valiyeh Ansarian; Çukur, Tolga
Magnetic Resonance Imaging (MRI) plays a significant role in medical diagnostics. However, prolonged scan times may hinder its widespread applicability in clinical settings. To mitigate this challenge, certain contrasts within multi-contrast MRI protocols can be excluded, and these target contrasts can then be synthesized from the acquired set of source contrasts retrospectively. Recently introduced generative adversarial and diffusion based MRI synthesis models yield enhanced performance against classical methods, yet there can still benefit from technical improvements. In this study, we propose a Brownian diffusion-based multi-contrast MR image synthesis model. Existing diffusion models synthesize images starting from a Gaussian noise sample, so guidance from the source contrast images are weakened. Conditional denoising diffusion models employs a weak conditioning during reverse process within the denoising network that may result in suboptimal sample generation due to poor convergence to target distribution. Capitalizing Brownian diffusion, the proposed model instead incorporates stronger guidance toward the target contrast distribution via a refined diffusion process. Experimental results suggest that our method attains higher performance in noise reduction and capture of tissue structural details over existing methods.