Publications
34 publications
2026
[1]Improved vascular segmentation via binary flow matching with straight-path regularization
in Proc. MICCAI, Sep. 2026 (Accepted)
[2]Coarse-to-fine meta-reweighting with dynamic retrieval for adult-to-pediatric domain adaptation in tumor segmentation
in Proc. MICCAI, Sep. 2026 (Accepted)
[3]Anatomy-guided deep learning for visceral fat segmentation in positron emission tomography–computed tomography
Engineering Applications of Artificial Intelligence, vol. 180, pp. 115286, Sep. 2026
[4]Cross-modality image registration via generating aligned image using a reference-augmented framework
IEEE Journal of Biomedical and Health Informatics, vol. 30, no. 7, pp. 6119–6132, Jul. 2026
[5]Regional-aware and sequence-informed multi-decoder network for robust brain glioma segmentation in multi-parametric MRI
Computers in Biology and Medicine, vol. 201, pp. 111387, Jan. 2026
[6]Deformation-informed unsupervised reference-augmented synthesis for CT alignment from misaligned CBCT–CT pairs in head-and-neck radiotherapy
Scientific Reports, 2026 (Accepted)
[7]Joint torque estimation from daily living motion for passive sarcopenia monitoring in older adults
Journal of NeuroEngineering and Rehabilitation, vol. 23, no. 1, Apr. 2026
[8]Context-aware 3D vessel segmentation with anatomical guidance
in Proc. IEEE ICASSP, pp. 6226–6230, 2026
2025
[9]Improving pelvic MR–CT image alignment with self-supervised reference-augmented pseudo-CT generation framework
in Proc. IEEE/CVF WACV, 2025 (Oral presentation)
[10]Learning robust brain tumor segmentation under label corruption and data scarcity
Engineering Applications of Artificial Intelligence, vol. 162, pp. 112322, Dec. 2025
[11]AI advancements in healthcare: Medical imaging and sensing technologies
Bioengineering, vol. 12, no. 10, pp. 1026, Sep. 2025
[12]Meta-learning guidance for robust medical image synthesis: Addressing real-world misalignment and corruptions
Computerized Medical Imaging and Graphics, vol. 121, pp. 102506, Apr. 2025
2024
[13]Comprehensive review of data-driven degradation diagnosis of lithium-ion batteries through electrochemical and multi-scale imaging analyses
Korean Journal of Chemical Engineering, vol. 43, no. 1, pp. 1–18, Jan. 2026
[14]MRI retrospective respiratory gating and cardiac sensing by CW Doppler radar: A feasibility study
IEEE Transactions on Biomedical Engineering, vol. 72, no. 1, pp. 112–122, Jan. 2025
[15]FLAIR MRI sequence synthesis using a squeeze-attention generative model for reliable brain tumor segmentation
Alexandria Engineering Journal, vol. 99, pp. 108–123, Jul. 2024
[16]JUST-Net: Jointly unrolled cross-domain optimization-based spatio-temporal reconstruction network for accelerated 3D myelin water imaging
Magnetic Resonance in Medicine, vol. 91, no. 6, pp. 2483–2497, Jun. 2024
2023
[17]Accelerating high b-value diffusion-weighted MRI using a convolutional recurrent neural network (CRNN-DWI)
Bioengineering, vol. 10, no. 7, pp. 864, Jul. 2023
[18]Multi-planar 2.5D U-Net for image quality enhancement of dental cone-beam CT
PLOS ONE, vol. 18, no. 5, pp. e0285608, May 2023
Previous
[20]Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction
Medical Physics, vol. 49, no. 9, pp. 5929–5942, Sep. 2022
[21]Reconfigurable heterogeneous integration enabled by stackable chips with embedded artificial intelligence
Nature Electronics, vol. 5, no. 6, pp. 386–393, Jun. 2022
[22]Improving high-frequency image features of deep-learning reconstruction via k-space refinement with a null-space kernel
Magnetic Resonance in Medicine, vol. 88, no. 3, pp. 1263–1272, Sep. 2022
[23]Improving phase-based conductivity reconstruction by means of deep-learning-based denoising of phase data for 3T MRI
Magnetic Resonance in Medicine, vol. 86, no. 4, pp. 2084–2094, Oct. 2021
[24]K-space refinement in deep-learning MR reconstruction via regularizing scan-specific SPIRiT-based self-consistency
ICCV Workshop on Learning for Computational Imaging, 2021
[25]Multi-task accelerated MR reconstruction schemes for jointly training multiple contrasts
NeurIPS Workshop on Deep Learning and Inverse Problems, 2021
[26]Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks
Medical Physics, vol. 48, no. 6, pp. 2939–2950, Jun. 2021
[27]Artificial neural network for multi-echo gradient-echo-based myelin water fraction estimation
Magnetic Resonance in Medicine, vol. 85, no. 1, pp. 380–389, Jan. 2021
[28]Stenosis detection from time-of-flight magnetic resonance angiography via deep-learning 3D squeeze-and-excitation residual networks
IEEE Access, vol. 8, pp. 43325–43335, 2020
[29]Estimating age-related changes in in vivo cerebral magnetic resonance angiography using a convolutional neural network
Neurobiology of Aging, vol. 87, pp. 125–131, Mar. 2020
[30]Validation of deep-learning-based artifact correction on synthetic FLAIR images in a different scanning environment
Journal of Clinical Medicine, vol. 9, no. 2, pp. 364, Jan. 2020
[31]Synthesizing T1-weighted MPRAGE images from multi-echo GRE images via deep neural networks
Magnetic Resonance Imaging, vol. 64, pp. 13–20, Dec. 2019
[32]Data-driven synthetic MRI FLAIR artifact correction via a deep neural network
Journal of Magnetic Resonance Imaging, vol. 50, no. 5, pp. 1413–1423, Nov. 2019
[33]Multi-echo GRE-based conductivity imaging using a Kalman phase estimation method
Magnetic Resonance in Medicine, vol. 81, no. 1, pp. 702–710, Jan. 2019
[34]Regulation of root patterns in mammalian teeth
Scientific Reports, vol. 7, no. 1, Oct. 2017
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