Application and research progress of MRI deep learning image reconstruction technology in clinical diagnosis of musculoskeletal system diseases

doi:10.16150/j.1671-2870.2024.02.003

Abstract

Abstract:

Deep learning based reconstruction (DLR) technology is currently one of the most cutting-edge technological advancements in the field of MRI image reconstruction. Compared to conventional MRI image reconstruction techniques, DLR technology redefines a new boundary between signal-to-noise ratio, spatial resolution, and scanning time on MRI. Its outstanding technical advantage is the effective removal of image noise and artifacts, significantly reducing scanning time, and also has potential advantages in improving the detection rate and accuracy qualitative diagnosis of lesions. With the continuous optimization of algorithms and the improvement of model generalization, DLR has been widely used in MRI examinations for multiple parts, such as the nervous system, musculoskeletal system, and heart. Its applicable scanning sequences and clinical application scenarios are also constantly expanding. DLR technology, while maintaining the original spatial resolution, reduces the number of signal acquisition times and increases the parallel acquisition acceleration factor to shorten the imaging time by more than 50%, achieving rapid imaging of the musculoskeletal system, and obtaining significantly better image quality than traditional reconstructed images. Currently, DLR is widely used in MRI exa-minations of musculoskeletal systems, such as the knee, shoulder, wrist, and spine, and has demonstrated its outstanding performance in shortening imaging time, improving image signal-to-noise ratio and resolution.

Key words: Magnetic resonance imaging, Deep learning-based reconstruction, Musculoskeletal system

CLC Number:

R681

ZHA Yunfei, WU Xiaxia. Application and research progress of MRI deep learning image reconstruction technology in clinical diagnosis of musculoskeletal system diseases[J]. Journal of Diagnostics Concepts & Practice, 2024, 23(02): 114-118.

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