arXiv subjects

Yohan Jun

Publications and source records attributed to Yohan Jun.

17 recordsLinked to original sources

arXiv2026

Vendor-Agnostic Joint Relaxometry and Myelin Water Fraction Mapping with B1 and Motion Correction

Obtaining consistent quantitative maps of myelin content and relaxation times across different sites and vendors is essential for advancing our understanding of brain development. Herein, we present a harmonized, vendor-agnostic magnetic resonance acquisition method designed for joint T1, T2, and myelin water fraction mapping, along with a method for rapid B1+ and B1- field estimation. We used our dictionary-based fitting and multi-compartment modeling for joint mapping of T1, T2 and myelin water fraction. Self-navigation-based retrospective motion correction was integrated with subspace reconstruction to track and correct rigid head motion during scanning, operating without the need for external hardware. Simulations, phantom and in vivo experiments confirmed the sensitivity and accuracy of the method, particularly for short T2 values corresponding to myelin, and demonstrated consistent performance across multiple scanner types. Coupled with the harmonized calibration scan, the proposed package offers a practical tool for multi-site, multi-vendor neuroimaging studies in both adult and pediatric populations.

physics.med-ph↗

arXiv2026

MWF-MIMOSA for efficient simultaneous relaxometry and myelin water fraction mapping

Quantitative magnetic resonance imaging (qMRI) provides improved sensitivity and specificity to tissue composition and pathological alterations compared with conventional contrast-weighted imaging. Among various qMRI biomarkers, myelin water imaging is of particular interest because myelin plays a central role in brain function and its alteration is closely associated with many neurological diseases. However, conventional myelin water fraction (MWF) mapping techniques are often limited by long scan times, low spatial resolution, reduced signal-to-noise ratio (SNR), and high specific absorption rate (SAR). Here, we propose MWF-MIMOSA for efficient simultaneous T1, T2, T2* mapping, magnetic susceptibility source separation, and MWF estimation. To achieve this, multi-contrast and multi-slice zero-shot self-supervised learning (MZS-SSL) was used to jointly reconstruct whole-brain complex-valued images. To improve computational efficiency of the parameter estimation step, a multilayer perceptron (MLP) was trained within the GACELLE GPU-accelerated parameter estimation framework to circumvent the computationally intensive Bloch simulation process, resulting in a >100-fold computational speed-up in MWF estimation. Numerical simulations were performed to evaluate the accuracy and precision of MWF-MIMOSA, and in-vivo results further demonstrated its robustness. Comparison with existing myelin water imaging methods showed that MWF-MIMOSA is highly correlated with established approaches, while providing complementary quantitative parameter maps at higher spatial resolution and with shorter scan times. Notably, simultaneous multi-parametric mapping was achieved in 5 min at 1 mm isotropic resolution, and in 10 min at 0.7 mm isotropic resolution. These results demonstrate the potential of MWF-MIMOSA for fast, high-resolution simultaneous relaxometry and myelin water imaging.

physics.med-ph↗

arXiv2026

FINDER: Zero-Shot Field-Integrated Network for Distortion-free EPI Reconstruction in Diffusion MRI

Echo-planar imaging (EPI) remains the cornerstone of diffusion MRI, but it is prone to severe geometric distortions due to its rapid sampling scheme that renders the sequence highly sensitive to $B_{0}$ field inhomogeneities. While deep learning has helped improve MRI reconstruction, integrating robust geometric distortion correction into a self-supervised framework remains an unmet need. To address this, we present FINDER (Field-Integrated Network for Distortion-free EPI Reconstruction), a novel zero-shot, scan-specific framework that reformulates reconstruction as a joint optimization of the underlying image and the $B_{0}$ field map. Specifically, we employ a physics-guided unrolled network that integrates dual-domain denoisers and virtual coil extensions to enforce robust data consistency. This is coupled with an Implicit Neural Representation (INR) conditioned on spatial coordinates and latent image features to model the off-resonance field as a continuous, differentiable function. Employing an alternating minimization strategy, FINDER synergistically updates the reconstruction network and the field map, effectively disentangling susceptibility-induced geometric distortions from anatomical structures. Experimental results demonstrate that FINDER achieves superior geometric fidelity and image quality compared to state-of-the-art baselines, offering a robust solution for high-quality diffusion imaging.

eess.IV↗

arXiv2025

Omni-QALAS: Optimized Multiparametric Imaging for Simultaneous T1, T2 and Myelin Water Mapping

Purpose: To improve the accuracy of multiparametric estimation, including myelin water fraction (MWF) quantification, and reduce scan time in 3D-QALAS by optimizing sequence parameters, using a self-supervised multilayer perceptron network. Methods: We jointly optimize flip angles, T2 preparation durations, and sequence gaps for T1 recovery using a self-supervised MLP trained to minimize a Cramer-Rao bound-based loss function, with explicit constraints on total scan time. The optimization targets white matter, gray matter, and myelin water tissues, and its performance was validated through simulation, phantom, and in vivo experiments. Results: Building on our previously proposed MWF-QALAS method for simultaneous MWF, T1, and T2 mapping, the optimized sequence reduces the number of readouts from six to five and achieves a scan time nearly one minute shorter, while also yielding higher T1 and T2 accuracy and improved MWF maps. This sequence enables simultaneous multiparametric quantification, including MWF, at 1 mm isotropic resolution within 3 minutes and 30 seconds. Conclusion: This study demonstrated that optimizing sequence parameters using a self-supervised MLP network improved T1, T2 and MWF estimation accuracy, while reducing scan time.

q-bio.QM↗

arXiv2025

Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner

Biophysical diffusion MRI models like Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compartment diffusivities, neurite fraction, and exchange time. However, NEXI's multi-shell, multi-diffusion-time requirements cause prohibitively long acquisitions. Leveraging the Connectome 2.0 ultra-high gradient scanner, we developed a time-efficient protocol using an Explainable AI (XAI) framework. Combining XGBoost, SHAP, and Recursive Feature Elimination trained on synthetic signals, XAI identified an optimal 8-feature subset, cutting scan time from 27 to 14 minutes. Validated in vivo in seven healthy participants, the XAI protocol was benchmarked against the full 15-feature acquisition, a Cram'er-Rao Lower Bound (CRLB) theoretical optimum, and two heuristics ("Mid-Range" and "Corner"). It robustly reproduced parameter estimates and maintained test-retest reproducibility. Remarkably, the XAI selection converged to the CRLB optimum. This validates XAI's optimality while highlighting its main advantage: achieving gold-standard optimization without complex analytical Jacobians, making it easily adaptable to numerical models or complex noise where CRLB is intractable. Furthermore, XAI showed superior in vivo robustness over heuristics: "Mid-Range" sampling yielded biased exchange time estimates from insufficient temporal diversity, while "Corner" sampling gave unstable intra-neurite diffusivity estimates (5-fold higher CV) due to noise sensitivity. Ultimately, this robust 14-minute protocol accelerates exchange-sensitive microstructural mapping, establishing a model-agnostic optimization framework adaptable to future ultra-high gradient systems and existing clinical scanners.

physics.med-ph↗

arXiv2025

MIMOSA: Multi-parametric Imaging using Multiple-echoes with Optimized Simultaneous Acquisition for highly-efficient quantitative MRI

Purpose: To develop a new sequence, MIMOSA, for highly-efficient T1, T2, T2*, proton density (PD), and source separation quantitative susceptibility mapping (QSM). Methods: MIMOSA was developed based on 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) by combining 3D turbo Fast Low Angle Shot (FLASH) and multi-echo gradient echo acquisition modules with a spiral-like Cartesian trajectory to facilitate highly-efficient acquisition. Simulations were performed to optimize the sequence. Multi-contrast/-slice zero-shot self-supervised learning algorithm was employed for reconstruction. The accuracy of quantitative mapping was assessed by comparing MIMOSA with 3D-QALAS and reference techniques in both ISMRM/NIST phantom and in-vivo experiments. MIMOSA's acceleration capability was assessed at R = 3.3, 6.5, and 11.8 in in-vivo experiments, with repeatability assessed through scan-rescan studies. Beyond the 3T experiments, mesoscale quantitative mapping was performed at 750 um isotropic resolution at 7T. Results: Simulations demonstrated that MIMOSA achieved improved parameter estimation accuracy compared to 3D-QALAS. Phantom experiments indicated that MIMOSA exhibited better agreement with the reference techniques than 3D-QALAS. In-vivo experiments demonstrated that an acceleration factor of up to R = 11.8-fold can be achieved while preserving parameter estimation accuracy, with intra-class correlation coefficients of 0.998 (T1), 0.973 (T2), 0.947 (T2*), 0.992 (QSM), 0.987 (paramagnetic susceptibility), and 0.977 (diamagnetic susceptibility) in scan-rescan studies. Whole-brain T1, T2, T2*, PD, source separation QSM were obtained with 1 mm isotropic resolution in 3 min at 3T and 750 um isotropic resolution in 13 min at 7T. Conclusion: MIMOSA demonstrated potential for highly-efficient multi-parametric mapping.

physics.med-ph↗

arXiv2025

Multishot Dual Polarity GRAPPA: Robust Nyquist Ghost Correction for multishot EPI

Purpose: This work aims to develop a robust Nyquist ghost correction method for multishot echo-planar imaging (EPI). The method helps correct challenging Nyquist ghosts, particularly on scanners with high-performance gradients or ultra-high fields. Methods: A method for multishot EPI ghost correction, called multishot dual-polarity GRAPPA (msDPG), is developed by extending the DPG concept to multishot readouts. msDPG employs tailored DPG kernels to address high-order phase differences between two EPI readout polarities, which cannot be fully addressed using linear phase correction (LPC). Advanced regularizers can be readily employed with the proposed msDPG for physiologic inter-shot phase variation correction during reconstruction. Additionally, a calibration refinement method is proposed to improve the quality of the DPG calibration data and enhance reconstruction performance. Results: Phantom and in vivo experiments on scanners with high-performance gradients and ultra-high fields demonstrated that msDPG achieved superior ghost correction performance than LPC, reducing the ghost-to-signal ratio (GSR) by over 50%. Compared to conventional DPG, msDPG provided images with lower noise amplification, particularly for acquisitions with large in-plane acceleration. Consequently, high-fidelity, submillimeter diffusion images were obtained using msDPG with regularized reconstruction. Conclusion: The proposed msDPG provides a robust Nyquist ghost correction method for multishot EPI, enabling submillimeter imaging with improved fidelity.

physics.med-ph↗

arXiv2025

A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications

MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple structural sequences that provide complementary information for differential diagnosis, while research protocols often incorporate advanced functional, diffusion, spectroscopic, and relaxometry sequences to capture multidimensional insights into tissue structure and composition. However, these capabilities come at the cost of prolonged scan times, which reduce patient throughput, increase susceptibility to motion artifacts, and may require trade-offs in image quality or diagnostic scope. Over the last two decades, advances in image reconstruction algorithms--alongside improvements in hardware and pulse sequence design--have made it possible to accelerate acquisitions while preserving diagnostic quality. Central to this progress is the ability to incorporate prior information to regularize the solutions to the reconstruction problem. In this tutorial, we overview the basics of MRI reconstruction and highlight state-of-the-art approaches, beginning with classical methods that rely on explicit hand-crafted priors, and then turning to deep learning methods that leverage a combination of learned and crafted priors to further push the performance envelope. We also explore the translational aspects and eventual clinical implications of these methods. We conclude by discussing future directions to address remaining challenges in MRI reconstruction. The tutorial is accompanied by a Python toolbox (https://github.com/tutorial-MRI-recon/tutorial) to demonstrate select methods discussed in the article.

eess.IV↗

arXiv2024

PRIME: Phase Reversed Interleaved Multi-Echo acquisition enables highly accelerated distortion-free diffusion MRI

Purpose: To develop and evaluate a new pulse sequence for highly accelerated distortion-free diffusion MRI (dMRI) by inserting additional echoes without prolonging TR, when generalized slice dithered enhanced resolution (gSlider) radiofrequency encoding is used for volumetric acquisition. Methods: A phase-reversed interleaved multi-echo acquisition (PRIME) was developed for rapid, high-resolution, and distortion-free dMRI, which includes several echoes where the first echo is for target diffusion-weighted imaging (DWI) acquisition with high-resolution and additional echoes are acquired with either lower resolution for 1) high-fidelity field map estimation, 2) phase navigation for shot-to-shot phase correction, 3) motion navigation across diffusion directions, or with high resolution to enable 4) high fidelity diffusion relaxometry acquisitions. The sequence was evaluated on in vivo data acquired from healthy volunteers on clinical and Connectome 2.0 scanners. Results: In vivo experiments demonstrated that 1) high in-plane acceleration (Rin-plane of 5-fold with 2D partial Fourier) was achieved using the high-fidelity field maps estimated from the second echo, which was made at a lower resolution/acceleration to increase its SNR while matching the effective echo spacing of the first readout, 2) high-resolution diffusion relaxometry parameters were estimated from triple-echo PRIME data using a white matter model of multi-TE spherical mean technique (MTE-SMT), and 3) high-fidelity mesoscale DWI at 490 um isotropic resolution was obtained in vivo by capitalizing on the high-performance gradients of the Connectome 2.0 scanner. Conclusion: The proposed PRIME sequence enabled highly accelerated, high-resolution, and distortion-free dMRI using additional echoes without prolonging scan time when gSlider encoding is utilized.

physics.med-ph↗

arXiv2024

NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction

Typical quantitative MRI (qMRI) methods estimate parameter maps in a two-step pipeline that first reconstructs images from undersampled k-space data and then performs model fitting, which is prone to biases and error propagation. We propose NLCG-Net, a model-based nonlinear conjugate gradient (NLCG) framework for joint T2/T1 estimation that incorporates a U-Net regularizer trained in a scan-specific, zero-shot fashion. The method directly estimates qMRI maps from undersampled k-space using mono-exponential signal modeling with scan-specific neural network regularization, enabling high-fidelity T1 and T2 mapping. Experimental results on T2 and T1 mapping demonstrate that NLCG-Net improves estimation quality over subspace reconstruction at high acceleration factors.

eess.IV↗

arXiv2023

Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction

Diffusion MRI is commonly performed using echo-planar imaging (EPI) due to its rapid acquisition time. However, the resolution of diffusion-weighted images is often limited by magnetic field inhomogeneity-related artifacts and blurring induced by T2- and T2*-relaxation effects. To address these limitations, multi-shot EPI (msEPI) combined with parallel imaging techniques is frequently employed. Nevertheless, reconstructing msEPI can be challenging due to phase variation between multiple shots. In this study, we introduce a novel msEPI reconstruction approach called zero-MIRID (zero-shot self-supervised learning of Multi-shot Image Reconstruction for Improved Diffusion MRI). This method jointly reconstructs msEPI data by incorporating deep learning-based image regularization techniques. The network incorporates CNN denoisers in both k- and image-spaces, while leveraging virtual coils to enhance image reconstruction conditioning. By employing a self-supervised learning technique and dividing sampled data into three groups, the proposed approach achieves superior results compared to the state-of-the-art parallel imaging method, as demonstrated in an in-vivo experiment.

eess.IV↗

arXiv2023

Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS

Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and 2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling. Methods: A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to 9-fold. Results: Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to 9-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time. Conclusion: The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.

eess.IV↗

arXiv2023

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for slice-direction continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.

cs.CV↗

arXiv2023

SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS

Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. Methods: A SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an ISMRM/NIST phantom. The SSL-QALAS and the dictionary matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. Results: Phantom experiments showed that both the dictionary matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the ISMRM/NIST phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1, T2, PD, and IE maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.

eess.IV↗

arXiv2022

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose a self-training based unsupervised domain adaptation framework for 3D medical image segmentation named COSMOS and validate it with automatic segmentation of Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance Images (MRI). Our target-aware contrast conversion network translates source domain annotated T1 MRI to pseudo T2 MRI to enable segmentation training on target domain, while preserving important anatomical features of interest in the converted images. Iterative self-training is followed to incorporate unlabeled data to training and incrementally improve the quality of pseudo-labels, thereby leading to improved performance of segmentation. COSMOS won the 1\textsuperscript{st} place in the Cross-Modality Domain Adaptation (crossMoDA) challenge held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). It achieves mean Dice score and Average Symmetric Surface Distance of 0.871(0.063) and 0.437(0.270) for VS, and 0.842(0.020) and 0.152(0.030) for cochlea.

eess.IV↗

arXiv2021

Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition. However, it is extremely expensive to acquire annotation on every data in medical fields, especially on magnetic resonance images (MRI) that comprise many different contrasts. Unsupervised methods can alleviate this problem; however, the performance drop is inevitable compared to fully supervised methods. In this work, we propose a self-training based unsupervised-learning framework that performs automatic segmentation of Vestibular Schwannoma (VS) and cochlea on high-resolution T2 scans. Our method consists of 4 main stages: 1) VS-preserving contrast conversion from contrast-enhanced T1 scan to high-resolution T2 scan, 2) training segmentation on generated T2 scans with annotations on T1 scans, and 3) Inferring pseudo-labels on non-annotated real T2 scans, and 4) boosting the generalizability of VS and cochlea segmentation by training with combined data (i.e., real T2 scans with pseudo-labels and generated T2 scans with true annotations). Our method showed mean Dice score and Average Symmetric Surface Distance (ASSD) of 0.8570 (0.0705) and 0.4970 (0.3391) for VS, 0.8446 (0.0211) and 0.1513 (0.0314) for Cochlea on CrossMoDA2021 challenge validation phase leaderboard, outperforming most other approaches.

eess.IV↗

arXiv2020

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.

eess.IV↗