Unrolled Variational Networks and Super Resolution in Clinical MRI at 3 Tesla: An Evaluation of Noise Characteristics, Contrast, and Sharpness

Motivation: Deep Learning based reconstruction methods, specifically Unrolled Variational Networks (UVN), have been proposed to enhance MRI image quality at high reduction factors. However, physical image characteristics have not been fully evaluated. Goal(s): This study evaluated the noise, contrast characteristics, and sharpness in images reconstructed using UVN. Approach: Various phantoms were utilized for evaluation, assessing SNR, Noise Power Spectrum, Contrast Ratio, and Point Spread Function. Results: With an increase in the reduction factor, the SNR improved, but both low-frequency and high-frequency noise increased. Despite a slight decrease in the contrast ratio with the increase in reduction factor, DL application notably enhanced image sharpness. Impact: Our study investigates the application of Unrolled Variational Networks in MRI systems. The potential improvement in image quality could lead to more accurate diagnoses, changing the way clinicians approach MRI imaging and prompting further research in medical imaging.