arXiv subjects

Yimeng Lin

Publications and source records attributed to Yimeng Lin.

5 recordsLinked to original sources

arXiv2026

Leveraging high-performance head-only gradients to enable ~2-fold higher multiband acceleration in SMS-EPI and SMS-EPTI

High-performance head-only gradient systems can substantially reduce echo spacing (ESP) in echo-planar imaging (EPI), but the resulting encoding speed is typically used to shorten readouts and reduce distortion. Here, we instead leverage the increased echo budget to enable denser k-space sampling and higher simultaneous-multislice (SMS) acceleration in SMS-EPI and SMS-EPTI. Fast k-space traversal with denser ky-kz sampling is achieved using peripheral nerve stimulation (PNS)-optimized gradient waveforms, while high-quality reconstruction is obtained using field-correcting GRAPPA (FCG). The resulting ESPs on MAGNUS 3T and Impulse 7T are approximately half those of high-performance whole-body systems, enabling approximately 2-fold higher multiband (MB) acceleration. This facilitates high-quality 2.5-mm isotropic whole-brain resting-state fMRI at 7T with MB28 and a repetition time (TR) of 123 ms.

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arXiv2026

High-Temporal-Resolution Motion Correction in Magnetic Resonance Fingerprinting Using a Quantitative Scout and Compact Spiral Navigators

Motion correction in magnetic resonance fingerprinting (MRF) helps preserve the accuracy of quantitative maps; however, existing approaches provide motion updates only every 7-8 seconds. We propose a navigation framework that integrates compact k-space navigators throughout the MRF acquisition, enabling sub-second motion estimation at minimal sequence overhead. A 3D spiral-projection MRF sequence was augmented with three orthogonal spiral navigators inserted every 0.5 seconds, enabling motion estimation by comparing navigator signals with quantitative scout (Q-Scout) data, i.e., motion-free low-resolution k-space with matching contrast evolution. The Q-Scout is obtained via a rapid calibration during the dummy preparation period, incurring no additional scan time. Motion estimation is formulated as dictionary matching in a discriminant subspace with optimization refinement. The method was evaluated in simulation and in vivo for 1 mm isotropic brain 3D MRF at 3 T. Across 35 motion-corrupted acquisitions with motion-free references available, the proposed motion correction reduced MRF reconstruction normalized root-mean-square error (NRMSE) by 7.1% and increased the structural similarity index measure (SSIM) by 0.085. Motion estimates aligned with 8 second temporal-rate image-based navigation (mean absolute difference of 0.15 mm and 0.23 degrees), while the proposed method provided higher temporal resolution and improved motion correction. The proposed framework enables robust motion navigation in MRF at 0.5 second temporal resolution with minimal sequence overhead. By using contrast-consistent modeling and efficient inference, it improves the reliability of quantitative MRI under rapid, unpredictable motion.

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arXiv2026

Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives

Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from geometric distortions caused by B0 field inhomogeneities. Existing correction methods first reconstruct distorted images using parallel imaging, then estimate the B0 field and correct the distortion in the image domain. In this sequential process, reconstruction artifacts at high acceleration factors and low SNR at high diffusion b-values degrade B0 estimation and limit the overall correction quality. We propose a physics-informed framework that jointly estimates the B0 field and distortion-free image directly from k-space data, without depending on an intermediate parallel-imaging reconstruction for the correction. The image and the B0 field are each represented as a superposition of Gaussian primitives embedded within an MRI physics forward model. The explicit, continuous parameterization captures both smooth regions and tissue boundaries and supports rotated-view EPI acquisitions without interpolation. The diffusion-weighted image is modeled as real and non-negative, with the image phase absorbed into a per-shot phase factor. Rotated views distribute distortions across multiple phase-encoding orientations, improving point spread function isotropy and providing stronger constraints for B0 estimation. On in vivo brain diffusion EPI, the proposed method attains the closest brain-boundary agreement with a distortion-free structural reference, with the largest improvement over sequential methods at high b-value and high acceleration. Extensive visual comparisons further show improved detail fidelity and noise suppression.

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arXiv2026

Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose "Mobile-GRAPPA", a k-space "cleaning" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to generate clean data. In experiments on highly motion-corrupted 1-mm whole-brain GRE (Tacq = 10 min; 1,620 motion/dB0 trackings) and EPTI (Tacq = 2 min; 544 trackings), Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI). These results show that Mobile-GRAPPA incorporates detailed motion and dB0 tracking into SENSE with minimal computational overhead, enabling fast, high-quality reconstructions of challenging data.

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arXiv2025

Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging

Echo Planar Imaging (EPI) is widely used for its rapid acquisition but suffers from severe geometric distortions due to B0 inhomogeneities, particularly along the phase encoding direction. Existing methods follow a two-step process: reconstructing blip-up/down EPI images, then estimating B0, which can introduce error accumulation and reduce correction accuracy. This is especially problematic in high B0 regions, where distortions align along the same axis, making them harder to disentangle. In this work, we propose a novel approach that integrates Implicit Neural Representations (INRs) with a physics-informed correction model to jointly estimate B0 inhomogeneities and reconstruct distortion-free images from rotated-view EPI acquisitions. INRs offer a flexible, continuous representation that inherently captures complex spatial variations without requiring predefined grid-based field maps. By leveraging this property, our method dynamically adapts to subject-specific B0 variations and improves robustness across different imaging conditions. Experimental results on 180 slices of brain images from three subjects demonstrate that our approach outperforms traditional methods in terms of reconstruction quality and field estimation accuracy.

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