Version 1
: Received: 27 September 2024 / Approved: 27 September 2024 / Online: 27 September 2024 (16:42:20 CEST)
How to cite:
Jiang, A. Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Techniques in Traditional Statistics and Neural Networks. Preprints2024, 2024092252. https://doi.org/10.20944/preprints202409.2252.v1
Jiang, A. Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Techniques in Traditional Statistics and Neural Networks. Preprints 2024, 2024092252. https://doi.org/10.20944/preprints202409.2252.v1
Jiang, A. Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Techniques in Traditional Statistics and Neural Networks. Preprints2024, 2024092252. https://doi.org/10.20944/preprints202409.2252.v1
APA Style
Jiang, A. (2024). Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Techniques in Traditional Statistics and Neural Networks. Preprints. https://doi.org/10.20944/preprints202409.2252.v1
Chicago/Turabian Style
Jiang, A. 2024 "Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Techniques in Traditional Statistics and Neural Networks" Preprints. https://doi.org/10.20944/preprints202409.2252.v1
Abstract
Phase errors in magnetic resonance (MR) techniques, including Nuclear Magnetic Resonance (NMR) spectroscopy and Magnetic Resonance Imaging (MRI), pose significant challenges to data accuracy and interpretation. As MR technologies advance, the demand for more sophisticated phase correction methods continues to grow, enhancing diagnostic precision and analytical outcomes. This review explores the evolution of phase correction models, beginning with simple global phase shifts, progressing through traditional linear statistical models, and culminating in modern machine learning techniques—specifically, neural networks. We also examine a range of optimization functions and optimizers, including both MR data-specific and common statistical approaches, applied in phase error correction. Despite notable progress, challenges remain in developing fully automatic phase error correction methods, particularly given the absence of ground truth in real-world MR data. This review highlights key methods, discusses their limitations, and proposes future pathways, including ensemble learning, that could guide further innovation in phase error correction.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
