Abstract #2765

Phase Inconsistency Problem of VarNet Reconstruction with External Calibration Data

Kexin Yang^1,2,3, Weichen Zhou¹, Lifeng Mei^1,2, Yilong Liu³, and Mengye Lyu^1,2,3

¹College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, ²College of Applied Sciences, Shenzhen University, Shenzhen, China, ³Guangdong-Hongkong-Macau Institute of CNS Regeneration, Key Laboratory of CNS Regeneration (Ministry of Education), Jinan University, Guangzhou, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: E2E VarNet is a powerful deep learning reconstruction method for MRI, but struggles when using external calibration data due to phase inconsistencies, particularly in applications like SMS and EPI.

Goal(s): To address the phase inconsistency problem in E2E VarNet, improving reconstruction quality when using external calibration data.

Approach: Various solutions were explored, including fine-tuning E2E VarNet with phase-inconsistent calibration data, applying ESPIRiT for coil sensitivity mapping, and using GRAPPA to generate phase-consistent calibration data.

Results: Fine-tuning with phase-inconsistent calibration and ESPIRiT showed minimal improvement. GRAPPA-based calibration provided the best results, reducing noise and improving reconstruction accuracy.

Impact: The proposed GRAPPA-based workaround solution to mitigate phase inconsistency improves the ability of E2E VarNet to utilize external calibration data, enhancing reconstruction quality and expanding its potential applications in research.

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