Fast-RF-Shimming: Accelerate RF Shimming in 7T MRI using Deep Learning

• URL: http://arxiv.org/abs/2501.12157v1
• Date: Tue, 21 Jan 2025 14:09:58 GMT
• Title: Fast-RF-Shimming: Accelerate RF Shimming in 7T MRI using Deep Learning
• Authors: Zhengyi Lu, Hao Liang, Ming Lu, Xiao Wang, Xinqiang Yan, Yuankai Huo,
• Abstract summary: Higher fields introduce challenges such as radiofrequency transmit (RF) field inhomogeneities, which result in uneven flip angles and image intensity artifacts. Traditional RF shimming methods, including Magnitude Least Squares (MLS) optimization, mitigate RF field inhomogeneity but are time-intensive and often require the presence of the patient. Recent machine learning methods, such as RF Shim Prediction by Iteratively Projected Ridge Regression, offer alternative approaches but face challenges such as extensive training requirements. This paper introduces a holistic learning-based framework called Fast RF Shimming, which achieves a 5000-fold speed
• Score: 16.39978444212565
• License:
• Abstract: Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a high signal-to-noise ratio (SNR), enabling exceptional spatial resolution for clinical diagnostics and research. However, higher fields introduce challenges such as transmit radiofrequency (RF) field inhomogeneities, which result in uneven flip angles and image intensity artifacts. These artifacts degrade image quality and limit clinical adoption. Traditional RF shimming methods, including Magnitude Least Squares (MLS) optimization, mitigate RF field inhomogeneity but are time-intensive and often require the presence of the patient. Recent machine learning methods, such as RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, offer alternative approaches but face challenges such as extensive training requirements, limited complexity, and practical data constraints. This paper introduces a holistic learning-based framework called Fast RF Shimming, which achieves a 5000-fold speedup compared to MLS methods. First, random-initialized Adaptive Moment Estimation (Adam) derives reference shimming weights from multichannel RF fields. Next, a Residual Network (ResNet) maps RF fields to shimming outputs while incorporating a confidence parameter into the loss function. Finally, a Non-uniformity Field Detector (NFD) identifies extreme non-uniform outcomes. Comparative evaluations demonstrate significant improvements in both speed and predictive accuracy. The proposed pipeline also supports potential extensions, such as the integration of anatomical priors or multi-echo data, to enhance the robustness of RF field correction. This approach offers a faster and more efficient solution to RF shimming challenges in UHF MRI.

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