A Framework for Inline Parallel Multi-Sequence Reconstructions: A Head Motion Correction Application

Motivation: Offline MRI reconstruction limits clinical applicability due to lengthy processing-times and lack integration with other scanner-data. While Gadgetron enables customised inline reconstruction, it may introduce scanner-reconstruction delays with multi-sequence implementation when methods are time-consuming. Goal(s): To develop a generalized framework for inline reconstruction capable of parallel multi-sequence operation without interrupting subsequent scans or scanner-reconstructions. Approach: A Gadgetron-based, multi-GPU framework was developed and implemented with DISORDER motion correction reconstruction across three 3D neuroimaging sequences to validate feasibility and robustness. Results: The framework demonstrated feasibility and high robustness (success in 271/273 cases) with DISORDER reconstruction implementation, effectively reducing motion artifacts while maintaining high image quality. Impact: This generalized inline framework enables advanced but time-consuming, customised multi-sequence reconstructions within an MR examination without acquisition or scanner-reconstruction delays. It demonstrated high robustness and is extensible for future integration of customised methods across scanners via centralized servers.