Develop magnetic resonance (MR) physiological quantitative imaging technologies to empower precision diagnosis and treatment.

Professor Chen Huijun’s research focuses on leveraging physiological quantitative imaging technologies and clinical translation studies to effectively improve the diagnosis and treatment of major diseases such as cardio-cerebrovascular disorders, ultimately reducing their morbidity, mortality, and disability rates. Specifically, his current research achievements center on the innovation and clinical translation of quantitative imaging technologies for inflammation and neovascularization in carotid atherosclerotic plaques based on dynamic contrast-enhanced MRI (DCE-MRI). Additionally, through core technological innovations in DCE-MRI, he has achieved preliminary results in the field of quantitative liver function imaging.

Professor Chen’s main academic innovations and contributions lie in the research and development of high-precision MR quantitative imaging technologies and their clinical translation, representing a typical interdisciplinary medical-engineering research direction. Focusing on atherosclerosis—a major disease threatening Chinese people’s health—he has developed a complete set of MR-based quantitative imaging protocols for atherosclerotic plaques. These protocols not only enable high-precision quantitative imaging of key pathological processes (inflammation and neovascularization) through technological innovation but also have been initially validated for clinical and pathological significance. He has also developed new single-sequence technologies for T1 and T2 quantitative imaging and processing, enabling faster and more accurate acquisition of plaque morphology and composition. His research is expected to effectively improve the diagnosis and treatment of cardio-cerebrovascular diseases, a major threat to national health, and ultimately reduce their persistently high morbidity, mortality, and disability rates.

Key Research Projects & Technological Innovations

High-Precision Quantitative Imaging of Inflammation and Neovascularization in Carotid Atherosclerotic Plaques and Its Clinical Translation

Inflammation and neovascularization are core pathological processes of atherosclerosis. Addressing the limitations of traditional quantitative imaging methods (e.g., DCE-MRI)—such as large quantitative errors, poor reproducibility, and inability to assess early-stage plaques—Professor Chen has made a series of core technological breakthroughs:

Proposed the HOBBI (Half-Fourier Black-Blood and Bright-Blood Alternating Imaging) technique for carotid vessel wall DCE-MRI, reducing quantitative errors and variability to less than 1/6 and 1/3 of those with traditional methods (MRM 2015;73(5):1754-63).

Developed LaBBI (Large-Area Black-Blood and Bright-Blood Alternating Imaging) through blood suppression and acquisition trajectory innovations, achieving 3D large-range high-resolution vessel wall imaging with a 15-fold increase in imaging slices (MRM 2018;79(3):1334-44).

These two technologies have initially solved the problem of blood flow signal contamination, enabling quantitative imaging of inflammation and neovascularization in early-stage plaques and key plaque regions. Professor Chen’s translational medicine research has also yielded significant outcomes:

Discovered the correlation between DCE-MRI quantitative parameters and clinical cardio-cerebrovascular symptoms (Atherosclerosis 2017;263:420-6), demonstrating the potential clinical diagnostic value of his developed technologies.

Revealed the pathological link between intraplaque hemorrhage (a high-risk component of atherosclerotic plaques) and inflammation/neovascularization (Stroke 2013;44(4):1031-6).

These translational studies have initially validated the significance of the technologies in pathological and clinical research.

Development of a Next-Generation 3D Large-Range High-Resolution Multi-Contrast Quantitative Imaging Solution for Atherosclerotic Plaques

While traditional multi-contrast MR imaging provides valuable information on plaque morphology and composition, it suffers from limitations including long scan times (15-26 minutes), cumbersome and time-consuming post-processing, poor reproducibility due to scan condition dependence, and lack of quantitative perfusion assessment for target organs (e.g., the brain). Through technological innovation, Professor Chen has proposed a next-generation 3D large-range high-resolution multi-contrast high-precision quantitative plaque imaging solution, including:

Developed the GOAL-SNAP sequence for plaque T1 quantitative imaging, combining inversion recovery and 3D golden-angle radial acquisition (Radiology 2018;287(1):276-84). Further developed the SIMPLE technique for simultaneous T1 and T2 quantification in a single sequence (MRM 2018;80(6):2598-608). Compared to traditional methods requiring 4 sequences for T1 quantification and T1&T2 quantification, these innovations eliminate registration issues and reduce scan time by 77% and 47% respectively. Importantly, they provide multi-contrast images (bright-blood, black-blood, T1, T1&T2, PD) to meet the needs of plaque morphology and composition analysis. Compared to traditional 2D multi-sequence multi-contrast protocols, the single-sequence approach shortens scan time by at least 47%, eliminates registration errors, expands imaging range by at least 2.5 times, and achieves higher resolution.

Based on the new 3D large-range high-resolution single-sequence multi-contrast imaging protocol, developed AI-based vessel wall and component segmentation methods leveraging its registration-free advantage and large feature space (ISMRM 2019;p4743; MRI 2019;60:93-100). Preliminary results confirm the feasibility of this solution for quantitative plaque morphology and composition analysis. He also developed specialized analysis software for 3D large-range vessel wall images (Software Copyright: 2015SR199101).

Targeting cerebral blood perfusion affected by carotid atherosclerosis, developed DeepMARS—a deep learning reconstruction method for spin-labeled MR fingerprinting perfusion imaging (MRM 2020;84(2):1024-34). This method accelerates reconstruction time by over 2,400 times (24 minutes vs. 0.58 seconds) while improving perfusion quantification accuracy and reproducibility.

1.Y Li, Y Wang, H Qi, Z Hu, Z Chen, R Yang, H Qiao, J Sun, T Wang, X Zhao, H Guo, H Chen*. Deep learning-enhanced T1 mapping with spatial-temporal and physical constraint. Magnetic Resonance in Medicine 2021;86(3):1647-1661.

2.Q Zhang, P Su, X Chen, Y Liao, S Chen, R Guo, Q Qi, X Li, X Zhang, H Hu, H Lu, H Chen*. Deep learning-based MR fingerprinting ASL ReconStruction (DeepMARS). Magnetic Resonance in Medicine 2020;84(2):1024-1034.

3.H Qi, J Sun, S Chen, Z Zhou, X Pan, Y Wang, X Zhao, R Li, C Yuan, H Chen*. Carotid intraplaque hemorrhage imaging with quantitative vessel wall T1 mapping: technical development and initial experience. Radiology 2018;287(1):276-284.

4.H Qi, F Huang, Z Zhou, P Koken, N Balu, B Zhang, C Yuan, H Chen*. Large coverage black-bright blood interleaved imaging sequence (LaBBI) for 3D dynamic contrast-enhanced MRI of vessel wall. Magnetic Resonance in Medicine 2018;79(3):1334-1344.

5.J Ning, Y Sun, S Xie, B Zhang, F Huang, P Koken, J Smink, C Yuan, H Chen*. Simultaneous acquisition sequence for improved hepatic pharmacokinetics quantification accuracy (SAHA) for dynamic contrast-enhanced MRI of liver. Magnetic Resonance in Medicine 2018;79(5):2629-2641

6.J Wang, H Liu, J Sun, H Xue, S Yu, C Liang, X Han, Z Guan, L Xie, L Wei, C Yuan, X Zhao, H Chen. Varying correlation between 18F-fluorodeoxyglucose positron emission tomography and dynamic contrast-enhanced MRI in carotid atherosclerosis: implications for plaque inflammation. Stroke 2014;45(6):1842-1845.

7.J Sun, Y Song, H Chen*, WS Kerwin, DS Hippe, L Dong, Min Chen, Cheng Zhou, Thomas S. Hatsukami, Chun Yuan. Adventitial perfusion and intraplaque hemorrhage: a dynamic contrast-enhanced MRI study in the carotid artery. Stroke 2013;44(4):1031-1036.

Academic Honors and Awards

2018 Beijing Science and Technology Award (Third Class)

Courses Taught

Principles of Medical Instruments

English Practice

Human Anatomy and Physiology

Information and Life

Medical Imaging (1): Physical Foundations

Medical Imaging (2): Image Reconstruction

Frontiers and Practice of Medical Imaging Technology

Other Social Affiliations

1. Member, Cerebral Blood Flow and Metabolism Branch, Chinese Stroke Association

2. Member, Quantitative Imaging Biomarkers Alliance (QIBA) PDF-MRI Technical Committee, Radiological Society of North America (RSNA)

Technical Patents and Software Copyrights

1. Huijun Chen; Chunyao Wang; Chen Zhang; Haikun Qi; Qiang Zhang; Yajie Wang. Non-contact neck-based respiratory and pulse signal detection method and apparatus, and imaging device. PCT/CN2018/111707, USA, April 28, 2020.

2. Huijun Chen; Haikun Qi; Qiang Zhang. Magnetic resonance imaging correction method, apparatus and device based on 3D topography measurement. 201711041327.3, China, October 30, 2017.

3. Jinnan Wang; Huijun Chen; Peter Boernert; Chun Yuan. Interleaved Black and Bright Blood Imaging for Interleaved Dynamic Contrast Enhanced Magnetic Resonance Imaging. PCT/IB2013/060831, USA, November 12, 2015.

4. Huijun Chen; Jinnan Wang; Chun Yuan. Optical based subject motion detection in imaging systems. PCT/IB2014/066569, USA, June 25, 2015.

5. Tsinghua University. 3D Large-Range Multi-Contrast Vascular Imaging Analysis and Processing Software V1.1. Software Copyright No.: 2015SR199101. Original Acquisition, All Rights Reserved, August 1, 2014.

6. Huijun Chen; Zhiwei Cui; Jue Zhang; Jing Fang. A method and apparatus for rapid 3D topography measurement. ZL200910079562.9, China, March 30, 2011.