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

• URL: http://arxiv.org/abs/2501.12157v2
• Date: Mon, 06 Oct 2025 03:43:52 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: We introduce Fast-RF-Shimming, which achieves a 5000x speed-up compared to the traditional MLS method.<n>Our technique shows a promising solution for addressing persistent inhomogeneity challenges.
• Score: 25.158522994692273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), enabling exceptionally high spatial resolution that benefits both clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field ($B_{1}^{+}$) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate $B_{1}^{+}$ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. Although these approaches show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. In this paper, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000x speed-up compared to the traditional MLS method. In the initial phase, we employ random-initialized Adaptive Moment Estimation (Adam) to derive the desired reference shimming weights from multi-channel $B_{1}^{+}$ fields. Next, we train a Residual Network (ResNet) to map $B_{1}^{+}$ fields directly to the ultimate RF shimming outputs, incorporating the confidence parameter into its loss function. Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. Comparative evaluations with standard MLS optimization underscore notable gains in both processing speed and predictive accuracy, which indicates that our technique shows a promising solution for addressing persistent inhomogeneity challenges.

Related papers

• Denoising and Baseline Correction of Low-Scan FTIR Spectra: A Benchmark of Deep Learning Models Against Traditional Signal Processing [0.0]
  We propose a physics-informed cascade Unet that separates denoising and baseline correction tasks.<n>This architecture forces the network to separate random noise from chemical signals using an embedded SNIP layer.<n>We benchmarked this approach against a standard single Unet and a traditional Savitzky-Golay/SNIP workflow.
  arXiv  Detail & Related papers  (2026-01-28T15:19:02Z)
• SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification [47.20483076887704]
  Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference.<n>We propose a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD)<n>We show that SKANet achieves an overall accuracy of 96.99%, exhibiting superior robustness for compound jamming classification.
  arXiv  Detail & Related papers  (2026-01-19T07:42:45Z)
• Low-Field Magnetic Resonance Image Quality Enhancement using a Conditional Flow Matching Model [0.3683202928838613]
  conditional flow matching (CFM) learns a continuous flow between a noise distribution and target data distributions.<n>Our framework is designed to reconstruct high-field-like MR images from their corresponding low-field inputs.<n>Experiments demonstrate that CFM achieves state-of-the-art performance, but also generalizes robustly to both in-distribution and out-of-distribution data.
  arXiv  Detail & Related papers  (2025-10-14T11:41:27Z)
• NeRF-based CBCT Reconstruction needs Normalization and Initialization [53.58395475423445]
  NeRF-based methods suffer from a local-global training mismatch between their two key components: the hash encoder and the neural network.<n>We introduce a Normalized Hash, which enhances feature consistency and mitigates the mismatch.<n>The neural network exhibits improved stability during early training, enabling faster convergence and enhanced reconstruction performance.
  arXiv  Detail & Related papers  (2025-06-24T16:01:45Z)
• A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data [1.7582682214679273]
  We propose fcHMRF-LIS, a powerful, stable, and scalable spatial FDR control method for voxel-wise multiple testing.<n>fcHMRF-LIS achieves accurate FDR control, lower FNR, reduced variability in FDP and FNP, and a higher number of true positives compared to existing methods.
  arXiv  Detail & Related papers  (2025-05-27T03:59:02Z)
• RINN: One Sample Radio Frequency Imaging based on Physics Informed Neural Network [9.812746486699323]
  Radio frequency (RF) imaging technology is expected to bring new possibilities for embodied intelligence and multimodal sensing. In this paper, we combine the ideas of PINN to design the RINN network, using physical constraints instead of true value comparison constraints. Our numerical evaluation results show that RINN's imaging results based on phaseless data are good, with indicators such as RRMSE (0.11) performing similarly well.
  arXiv  Detail & Related papers  (2025-04-19T15:19:12Z)
• CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space Models for Remote Physiological Measurement [24.511384674989223]
  Heart rate (HR) estimation via remote photoplethys (rPl) offers a non-invasive solution for health monitoring. Traditional single-modality approaches (RGB or Radio Frequency (RF)) face challenges in balancing robustness and accuracy due to lighting variations, motion artifacts, and skin tone bias. We propose CardiacMamba, a multimodal RGB-RF fusion framework that leverages the complementary strengths of both modalities.
  arXiv  Detail & Related papers  (2025-02-19T11:00:34Z)
• Few-shot NeRF by Adaptive Rendering Loss Regularization [78.50710219013301]
  Novel view synthesis with sparse inputs poses great challenges to Neural Radiance Field (NeRF) Recent works demonstrate that the frequency regularization of Positional rendering can achieve promising results for few-shot NeRF. We propose Adaptive Rendering loss regularization for few-shot NeRF, dubbed AR-NeRF.
  arXiv  Detail & Related papers  (2024-10-23T13:05:26Z)
• Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning [9.596613410358206]
  Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B1+) inhomogeneities, leading to uneven flip angles and image intensity anomalies. This study addresses B1+ field homogeneity through a novel deep learning-based strategy.
  arXiv  Detail & Related papers  (2024-08-21T04:10:56Z)
• Spatial Annealing for Efficient Few-shot Neural Rendering [73.49548565633123]
  We introduce an accurate and efficient few-shot neural rendering method named textbfSpatial textbfAnnealing regularized textbfNeRF (textbfSANeRF)<n>By adding merely one line of code, SANeRF delivers superior rendering quality and much faster reconstruction speed compared to current few-shot neural rendering methods.
  arXiv  Detail & Related papers  (2024-06-12T02:48:52Z)
• SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance [136.15885067858298]
  This study presents a novel feature-matching-based sparse geometry regularization module, enhanced by a spatially consistent geometry filtering mechanism and a frequency-guided geometric regularization strategy.<n>Our experiments demonstrate that SGCNeRF achieves superior geometry-consistent outcomes and also surpasses FreeNeRF, with improvements of 0.7 dB in PSNR on LLFF and DTU.
  arXiv  Detail & Related papers  (2024-04-01T08:37:57Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar [8.041399176135178]
  We propose to transform a large-scale RFNN into a compact RFNN while almost preserving its accuracy. We construct the Robust TT-RFNN (RTT-RFNN) by incorporating a robustness solver on TT-RFNN to enhance its robustness. Empirical evaluations conducted on MNIST and CIFAR-10 datasets show the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-12-16T06:21:42Z)
• SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models [76.43625653814911]
  Diffusion models have gained popularity for accelerated MRI reconstruction due to their high sample quality. They can effectively serve as rich data priors while incorporating the forward model flexibly at inference time. We introduce SURE-based MRI Reconstruction with Diffusion models (SMRD) to enhance robustness during testing.
  arXiv  Detail & Related papers  (2023-10-03T05:05:35Z)
• RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts [7.652037892439504]
  Delay-and-sum beamforming leads to irreversible reduction of Radio-Frequency (RF) channel data. rich contextual information embedded within RF wavefronts offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. We propose to directly localize scatterers in RF channel data using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block.
  arXiv  Detail & Related papers  (2023-10-02T18:41:23Z)
• One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers [57.441926088870325]
  Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements. Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
  arXiv  Detail & Related papers  (2023-06-06T20:28:37Z)
• CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution [78.734927709231]
  Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model. We propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that can yield medical images at arbitrary scales and viewpoints in a continuous domain.
  arXiv  Detail & Related papers  (2023-03-28T18:36:19Z)
• Learning Federated Visual Prompt in Null Space for MRI Reconstruction [83.71117888610547]
  We propose a new algorithm, FedPR, to learn federated visual prompts in the null space of global prompt for MRI reconstruction. FedPR significantly outperforms state-of-the-art FL algorithms with 6% of communication costs when given the limited amount of local training data.
  arXiv  Detail & Related papers  (2023-03-28T17:46:16Z)
• One-shot Generative Distribution Matching for Augmented RF-based UAV Identification [0.0]
  This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based identification methods ineffective. One-shot generative methods for augmenting transformed RF signals offer a significant improvement in UAV identification.
  arXiv  Detail & Related papers  (2023-01-20T02:35:43Z)
• SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction [147.9379707578091]
  NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks. Despite NeRFmm producing precise scene synthesis and pose estimations, it still struggles to outperform the full-annotated baseline on challenging scenes. We propose Sinusoidal Neural Radiance Fields (SiNeRF) that leverage sinusoidal activations for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray batch efficiently.
  arXiv  Detail & Related papers  (2022-10-10T10:47:51Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.