TY - GEN
T1 - Real-Time Image Reconstruction with Deep Denoising for Low-Field MRI-Guided Interventions
AU - Liu, Yixuan
AU - Ding, Yu
AU - Liu, Yingmin
AU - Chen, Chong
AU - Chow, Kelvin
AU - Jin, Ning
AU - Krafft, Axel
AU - Maier, Florian
AU - Armstrong, Aimee
AU - Ahmad, Rizwan
AU - Simonetti, Orlando
AU - Xue, Yuan
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Congenital heart disease (CHD) affects thousands of infants annually, often necessitating repeated catheterizations guided by X-ray fluoroscopy, leading to cumulative radiation exposure. Low-field MRI guided intervention can reduce this risk; however, image noise is problematic. To reduce this risk, We present a real-time image reconstruction pipeline for low-field (0.55T) Magnetic Resonance Imaging (MRI)-guided interventions, using a 0.55T MRI scanner. Our method integrates the GRAPPA with a modified DnCNN denoising network in a deep learning framework, achieving high-quality, low-latency image reconstructions. The proposed pipeline was validated in preclinical in vivo experiments, demonstrating a 15% improvement in signal-to-noise ratio (SNR) and a 30% reduction in image artifacts compared to conventional techniques, while maintaining real-time performance.
AB - Congenital heart disease (CHD) affects thousands of infants annually, often necessitating repeated catheterizations guided by X-ray fluoroscopy, leading to cumulative radiation exposure. Low-field MRI guided intervention can reduce this risk; however, image noise is problematic. To reduce this risk, We present a real-time image reconstruction pipeline for low-field (0.55T) Magnetic Resonance Imaging (MRI)-guided interventions, using a 0.55T MRI scanner. Our method integrates the GRAPPA with a modified DnCNN denoising network in a deep learning framework, achieving high-quality, low-latency image reconstructions. The proposed pipeline was validated in preclinical in vivo experiments, demonstrating a 15% improvement in signal-to-noise ratio (SNR) and a 30% reduction in image artifacts compared to conventional techniques, while maintaining real-time performance.
UR - https://www.scopus.com/pages/publications/105005834917
UR - https://www.scopus.com/pages/publications/105005834917#tab=citedBy
U2 - 10.1109/ISBI60581.2025.10980690
DO - 10.1109/ISBI60581.2025.10980690
M3 - Conference contribution
AN - SCOPUS:105005834917
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings
PB - IEEE Computer Society
T2 - 22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025
Y2 - 14 April 2025 through 17 April 2025
ER -