张玉瑶对医学图像成像、处理和分析领域有广泛的研究兴趣。她目前的主要工作是开发基于深度学习的高效计算机断层成像技术、磁共振影像去噪、超分辨率和图像重建方法。这些先进的算法进一步应用于人脑磁共振3D重建、多模态3D/4D婴儿和成人脑图谱构建、神经退行性脑疾病分析和3D人体组织参数化建模等医学图像分析问题。通过与其他研究机构和医院的广泛合作，她的实验室将医学影像技术与临床应用背景相结合，实现了基于磁共振图像的胎儿和婴儿脑结构发育轨迹构建，脑部微小神经核团的精准定位和退化模型构建。相关研究成果被应用于脑发育疾病（如胎儿脑室扩张）和脑退行性疾病（如帕金森症和阿尔茨海默病）的早期干预、临床诊断和手术治疗，以及Covid-19肺炎病程发展的影像学分析等前沿临床问题的研究。

在过去的五年里，张玉瑶在领域内的顶级学术期刊和会议，如Neuroimage、IEEE TMI、IEEE JBHI、Human Brain Mapping、Brain、MRM、NMR in Biomedicine、JMRI、Journal of Neurosurgery、IJCAI、CVPR、MICCAI、ISMRM、ISBI、EMBC和SIGGRAPH等上发表了60余篇论文。她是国际磁共振协会ISMRM的会员，并担任Neuroimage、IEEE TMI、IEEE TIP、IEEE TBME、Human Brain Mapping、Neurocomputing等10余个期刊的常规审稿人。她还担任了AAAI 2022会议的资深程序委员和Journal of Frontier in Neuroscience的客座编辑。她获得了上海市青年东方学者的称号，并主持了一项国家自然科学基金的面上项目。

Unsupervised Coordinate Projection Network for Sparse-View Computed Tomography

Sparse-view Computed Tomography (SVCT) has great potential for decreasing patient radiation exposure dose during scanning. We propose SCOPE, a self-supervised coordinate projection network, for artifact-free CT image reconstruction from sparse-view sinograms. By leveraging an implicit neural representation network and a novel re-projection strategy, we improve the solution space and stability of the inverse problem. The CT image is represented as an implicit function of spatial coordinates, and a dense-view sinogram is generated. Filtered Back Projection is then applied for final reconstruction. Our SCOPE model, integrated with hash encoding, achieves state-of-the-art results in sparse-view CT image reconstruction, surpassing recent INR-based and supervised DL methods.

[1] Qing Wu, Ruimin Feng, Hongjiang Wei, Jingyi Yu, Yuyao Zhang, Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography, IEEE TCI, 2023

[2] Qing Wu, Xin Li, Hongjiang Wei, Jingyi Yu, Yuyao Zhang§: “Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field,” ISBI, 2023 Apr.

[3] Ruimin Feng, Qing Wu, Yuyao Zhang, Hongjiang Wei§: “IMJENSE: Scan-specific IMplicit representation for Joint coil sENSitivity and image Estimation in parallel MRI,” IEEE TMI, Revised 2023, Apr.

Robust self-supervised 3D isotropic fetal brain MRI reconstruction.

We propose a robust self-supervised volume reconstruction technique for fetal MR images, addressing slice misalignment and motion artifacts. Our approach involves two learning modules: one for high-fidelity 3D volume reconstruction and another for 2D slice misalignment correction. The volume reconstruction module utilizes a comprehensive forward model and an under-parameterized deep decoder to eliminate artifacts caused by misalignment and motion. Additionally, the misalignment correction module employs iterative slice-to-volume registration. Our self-supervised DL methodology achieves state-of-the-art performance in 3D fetal brain reconstruction without relying on ground truth references.

[4] Jiangjie Wu, Zhenghao Li, Lihui Wag, Hongjiang Wei, Yuyao Zhang§: ASSURED: A Self-Supervised Deep Decoder Network for Fetus Brain MRI Reconstruction, ISBI, 2023 Apr.

[5] Jiangjie Wu, Zhenghao Li, Lihui Wang, Hongjiang Wei, Yuyao Zhang§: ASSURED: A Self-Supervised Deep Decoder Network for Fetus Brain MRI Reconstruction, Neuroimage, Submitted: 2022 May. (Top Journal, IF 7.4)

[6] Jiangjie Wu, Zhenghao Li, Qing Wu, Yutong Wang, Ling Jiang, Zhaoxia Qian, Hongjiang Wei, Yuyao Zhang§: Longitudinal Chinese Population Structural Fetal Brain Atlases Construction toward precise fetal brain segmentation, EMBC, 2021.

An Arbitrary Scale Super-Resolution Approach for 3D MR Images

We introduce ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D high-resolution (HR) MR images. The ArSSR model employs a shared implicit neural voxel function to represent both the low-resolution (LR) and HR images, allowing for arbitrary up-sampling rates. By training the model on paired HR and LR examples, the implicit voxel function is approximated using deep neural networks. The ArSSR model comprises an encoder network for feature extraction and a decoder network to approximate the implicit voxel function. Experimental results demonstrate that the ArSSR model achieves state-of-the-art performance in 3D HR MR image reconstruction, with the ability to handle arbitrary up-sampling scales using a single trained model.

[7] Qing Wu, Yuwei Li, Lan Xu, Ruimin Feng, Hongjiang Wei, Qing Yang, Boliang Yu, Xiaozhao Liu, Yingyi Yu§, Yuyao Zhang§: IREM: High-Resolution Magnetic Resonance (M.R.) Image Reconstruction via Implicit Neural Representation, MICCAI, 2021.

[8] Qing Wu, Yuwei Li, Yawen Sun, Yan Zhou, Hongjiang Wei, Jingyi Yu, Yuyao Zhang§: An Arbitrary Scale Super-Resolution Approach for 3-Dimensional Magnetic Resonance Image using Implicit Neural Representation, IEEE JBHI. vol. 27, no. 2, pp. 1004-1015, Feb. 2023, doi: 10.1109/JBHI.2022.3223106. (Top Journal, IF 7.021)

[9] Chaolin Rao*, Qing Wu*, Pingqiang Zhou, Jingyi Yu, Yuyao Zhang§, Xin Lou§: An Energy-efficient Accelerator for Medical ImageReconstruction from Implicit Neural Representation, IEEE Transaction on Circuits and Systems I, Regular Papers, Early access: DOI: 10.1109/TCSI.2022.3231863. (Top Journal, IF 3.833)

[10] Haonan Zhang*, Yuhan Zhang*, Qing Wu, JIangjie Wu, Zhiming Zhen, Feng Shi, Jianmin Yuan, Chen Liu, Yuyao Zhang§: “Self-supervised arbitrary scale super-resolution framework for anisotropic MRI,” ISBI, 2023

[11] Jun Li*, Xiaojun Guan*, Qing Wu, Chenyu He, Weiming Zhang, Chunlei Liu, Hongjiang Wei, Xiaojun Xu§, Yuyao Zhang§： Direct Localization and Delineation of Human Pedunculopontine Nucleus based on a Self-supervised Magnetic Resonance Image Super-resolution Method, Human Brain Mapping. Accepted: 2023 Mar. (Top Journal, IF 5.04)

Temporal consistent longitudinal brain atlas construction using Implicit Neural Representation

We propose a deep-learning framework to improve longitudinal brain atlases. By treating the issue as a 4D image denoising task, our framework generates a continuous and noise-free atlas using implicit neural representation. This approach addresses temporal inconsistency caused by averaging discrete time points independently and differences in onto-genetic trends. Evaluation on two types of brain atlases demonstrates enhanced temporal consistency and accurate representation of brain structures. Additionally, our method enables the creation of higher-resolution 4D atlases.

[12] Jiangjie Wu, Taotao Sun, Boliang Yu, Zhenghao Li, Qing Wu, Yutong Wang, Zhaoxia Qian, Yuyao Zhang, Ling Jiang, Hongjiang Wei, Age-specific structural fetal brain atlases construction and cortical development quantification for chinese population, NeuroImage, 2021

[13] Lixuan Chen, Jiangjie Wu, Qing Wu, Hongjiang Wei, Yuyao Zhang, Continuous longitudinal fetus brain atlas construction via implicit neural representation, MICCAI workshop PIPPI 2022, 2022

[14] Lixuan Chen, Jiangjie Wu, Qing Wu, Guoyan Lao, Hongjiang Wei, Yuyao Zhang, COLLATOR: Consistent Spatial-Temporal Longitudinal Atlas Construction via Implicit Neural Representation, IEEE TMI, Submitted

Zero-shot Learning for Image Denoising

We proposes a self-supervised image denoising method called Noise2SR (N2SR) to address the limitations of existing methods in real scene noise removal. N2SR trains a simple and effective denoising model using paired noisy images of different dimensions. This training strategy enables efficient self-supervision and restoration of more image details from a single noisy observation. Experimental results demonstrate that N2SR outperforms other self-supervised deep learning denoising methods in simulated and microscopy noise removal. N2SR holds promise for enhancing the quality of various scientific imaging applications. 

[16] Xuanyu Tian, Qing Wu, Hongjiang Wei, Yuyao Zhang§: Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image, MICCAI, 2022 Oct.

[17] Changhao Jiang1*, Xuanyu Tian 1*, Yanbin Li, Jiangjie Wu, Xin Mu, Lei Zhang, Yuyao Zhang§: “Self-Supervised High-dimentional Megnatic Resonance Image Denoising using Super-Resolved Single Noisy Image,” ISBI, 2023 Apr.

[C1] Jiangjie Wu, Zhenghao Li, Lihui Wang, Hongjiang Wei, Yuyao Zhang§: ASSURED: A Self-Supervised Deep Decoder Network for Fetus Brain MRI Reconstruction, ISBI, 2023 Apr.

[C2] Qing Wu, Xin Li, Hongjiang Wei, Jingyi Yu, Yuyao Zhang§: “Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field,” ISBI, 2023 Apr.

[C3] Changhao Jiang1*, Xuanyu Tian 1*, Yanbin Li, Jiangjie Wu, Xin Mu, Lei Zhang, YuyaoZhang§: “Self-Supervised High-dimentional Megnatic Resonance Image Denoising using Super-Resolved Single Noisy Image,” ISBI, 2023 Apr.

[C4] Haonan Zhang*, Yuhan Zhang*, Qing Wu, JIangjie Wu, Zhiming Zhen, Feng Shi, Jianmin Yuan, Chen Liu, Yuyao Zhang§: “Self-supervised arbitrary scale super-resolution framework for anisotropic MRI,” ISBI, 2023 Apr.

[C5] Ruimin Feng, Qing Wu, Yuyao Zhang, Hongjiang Wei§: “A Scan-Specific Unsupervised method for Parallel MRI Reconstruction,” ISBI, 2023 Apr.

[J1] Qing Wu, Yuwei Li, Yawen Sun, Yan Zhou, Hongjiang Wei, Jingyi Yu, Yuyao Zhang§: An Arbitrary Scale Super-Resolution Approach for 3-Dimensional Magnetic Resonance Image using Implicit Neural Representation, IEEE JBHI. vol. 27, no. 2, pp. 1004-1015, Feb. 2023, doi: 10.1109/JBHI.2022.3223106. (Top Journal, IF 7.021)

[J2] Chenyu He, Xiaojun Guan, Weimin Zhang, Jun Li, Chunlei Liu, Hongjiang Wei, Xiaojun Xu, Yuyao Zhang§: Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification, Brain structure and function. 2022 Aug 29. doi: 10.1007/s00429-022-02547-1. Epub ahead of print. PMID: 36038737. (JCR Q1, IF 3.18)

[J3] Jinwei He, Ying Su, Zesong Qiu, Jun Chen, Zhe Luo§, Yuyao Zhang§: Steroids therapy in patients with severe COVID-19: association with decreasing of pneumonia fibrotic tissue volume, Frontier in Medicine, 2022 ;9:907727. DOI: 10.3389/fmed.2022.907727. PMID: 35911397; PMCID: PMC9329540 (JCR Q1, IF 5.058)

[J4] Chaolin Rao*, Qing Wu*, Pingqiang Zhou, Jingyi Yu, Yuyao Zhang§, Xin Lou§: An Energy-efficient Accelerator for Medical ImageReconstruction from Implicit Neural Representation, IEEE Transaction on Circuits and Systems I, Regular Papers, Early access: DOI: 10.1109/TCSI.2022.3231863. (Top Journal, IF 3.833)

[J5] Ying Su*, Zesong Qiu*, Jun Chen, Min-Jie Ju, Guo-Guang Ma, Jin-Wei He, Shen-Ji Yu, Kai Liu, Guo-Wei Tu, Yuyao Zhang§, Zhe Luo§: Steroid therapy is associated with decreases in the percentage of compromised lung volume in patients with severe COVID-19, Respiratory Research, 2022 April 29;23(1):105. (Top Journal, IF 5.631)

[J6] Yuting Shi, Steven Cao, Xu Li, Ruimin Feng, Jie Zhuang, Yuyao Zhang, Chunlei Liu, Hongjiang Wei§: Regularized Asymmetric Susceptibility Tensor Imaging in the Human Brain in vivo, is available under the IEEE JBHI. Early access: DOI: 10.1109/JBHI.2022.3182969 2022 June. (Top Journal, IF 7.021)

[J7] Yuting Shi, Ruimin Feng, Zhenghao Li, Jie Zhuang, Yuyao Zhang, Chunlei Liu, Hongjiang Wei§: Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: a multi-orientation gradient-echo MRI dataset, Neuroimage. 2022 Nov 1;261:119522. doi: 10.1016/j.neuroimage.2022.119522. Epub 2022 Jul 26. PMID: 35905811. (Top Journal, IF 7.4)

[J8] Yawen Sun, Ying Hu, Yage Qiu, Yuyao Zhang, Changhao Jiang, Peiwen Lu, Qun Xu, Yuting Shi, Hongjiang Wei, Yan Zhou§: Characterization of normal-appearing white matter over 1-2 years in small vessel disease using MR-based quantitative susceptibility mapping and free-water mapping, Frontiers Aging Neuroscience, 2022 Sep 30;14:998051. doi: 10.3389/fnagi.2022.998051. PMID: 36247993; PMCID: PMC9562046. (JCR Q1, IF 5.75)

[J9] Ruimin Feng, Zhenghao Li, Jie Zhuang, Yuyao Zhang, Hongjiang Wei§: An improved asymmetric susceptibility tensor imaging model with frequency offset correction, Magnetic Resonance in Medicine. 2023 Feb;89(2):828-844. doi: 10.1002/mrm.29494. Epub 2022 Oct 27. PMID: 36300852. (Top Journal, IF 4.6)

[J10] Ming Zhang, Chenglei Liu, Huimin Lin, Hanqi Wang, Le Qin, Zhiyong Zhang, Chunlei Liu, Yong Lu, Fuhua Yan, Yuyao Zhang, Hongjiang Wei§: “Age-Related Changes in the Spatial Variation of Magnetic Susceptibility of Human Articular Cartilage,” J Magn Reson Imaging. 2022 Nov 2. doi: 10.1002/jmri.28513. Epub ahead of print. PMID: 36322382. (JCR Q1, IF 5.119)

[J11] Fang Wang, Ming Zhang, Yan Li; Yufei Li, Hengfen Gong, Jun Li, Yuyao Zhang, Chencheng Zhang, Fuhua Yan, Bomin Sun, Naying He, Hongjiang Wei§: Brain Iron Deposition with Progression of Late-life Depression Measured by MRI-based Quantitative Susceptibility Mapping,Quantitative Imaging in Medicine and Surgery, 2022 12(7): 3873-3888. (IF 3.873)

[J12] Ming Zhang, Zhihui Li, Hanqi Wang, Tongtong Chen, Yong Lu, Fuhua Yan, Yuyao Zhang, Hongjiang Wei§: Simultaneous quantitative susceptibility mapping of articular cartilage and cortical bone of human knee joint using ultrashort echo time sequences, Frontiers in Endocrinology, 2022 February 22;13:844351. (JCR Q1, IF 5.555)

[J13] Yunhao Wu, Chao Zhang, Yufei LI, Jie Feng, Ming Zhang, Hongxia Li, Tao Wang, Yingying Zhang, Zhijia Jin, Chencheng Zhang, Yuyao Zhang, Dianyou Li, Yiwen WU, Hongjiang Wei, Bomin Sun§: Imaging insights of isolated idiopathic dystonia: voxel-based morphometry and activation likelihood estimation studies, Frontiers Neurology, 2022 April 26;13:823882. (JCR Q1, IF 4.003)

[C6] Zesong Qiu, Qixuan Zhang, Yuwei Li, Yinghao Zhang, Longwen Zhang, Qing Wu,, Yuyao Zhang§, Jingyi Yu§: SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator, Siggraph Asia, 2022 Dec. Oral Presentation

[C7] Lixuan Chen, Jiangjie Wu, Hongjiang Wei, Yuyao Zhang§: Continuous longitudinal fetus brain atlas construction via implicit neural representation, MICCAI workshop PIPPI, 2022 Oct. Oral Presentation

[C8] Xuanyu Tian, Qing Wu, Hongjiang Wei, Yuyao Zhang§: Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image, MICCAI, 2022 Oct.

[C9] Yuwei Li, Longwen Zhang, Zengsong Qiu, Yingwenqi Jiang, Nianyi Li, Yuexin Ma, Yuyao Zhang, Lan Xu, Jingyi Yu§: NIMBLE: A Non-rigid Hand Model with Bones and Muscles, Siggraph, 2022.

[C10] Xin Tang, Jiadong Zhang, Yongsheng Pan, Yuyao Zhang, Feng Shi§: “CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants,” MICCAI workshop DALI, 2022 Oct.

[C11] Lang Mei, Mianxin Liu, Lingbin Bian, Yuyao Zhang, Feng Shi, Han Zhang, Dinggang Shen§: “Modular Graph Encoding and Hierarchical Readout for Functional Brain Network based eMCI Diagnosis,” MICCAI workshop ISGIE, 2022 Oct.

[C12] Yonghang Guan, Jun Zhang, Kuan Tian, Sen Yang, Pei Dong, Jinxi Xiang, Wei Yang, Junzhou Huang, Yuyao Zhang, Xiao Han§: Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification, CVPR, 2022 Jun. Oral Presentation

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