Please visit Dr. Liu’s Google Scholar profile.
Most of the papers are freely accessible from Dr. Liu’s ResearchGate profile.
Journal Publications
After Joining Texas Tech
J27. M. Liu, H. Dong, A. Mazlout, Y. Wu, A. Kalyanasundaram, J.N. Oshinski, W.Sun, J.A. Elefteriades, B.G. Leshnower, and R. L. Gleason. The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy. Computers in Biology and Medicine. 2024; 108041. [Link]
Before Joining Texas Tech
J26. L. Liang, M. Liu, J. Elefteriades, and W. Sun. Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics. Computer Methods in Applied Mechanics and Engineering. 2023; 116347. [Link]
J25. D. H. Pak, M. Liu, T. Kim, L. Liang, A. Caballero, J. Onofrey, S.S. Ahn, Y. Xu, R. McKay, W. Sun, R. Gleason, and J. S. Duncan. Patient-specific Heart Geometry Modeling for Solid Biomechanics using Deep Learning. IEEE Transactions on Medical Imaging. 2023. [Link]
J24. H. Dong, M. Liu, J. Woodall, B.G. Leshnower, and R.L. Gleason Jr. Effect of Nonlinear Hyperelastic Property of Arterial Tissues on the Pulse Wave Velocity based on the Unified-Fiber-Distribution (UFD) Model. Annals of Biomedical Engineering. 2023. [Link]
J23. L. Liang, M. Liu, J. Elefteriades, and W. Sun. PyTorch-FEA: Autograd-enabled Finite Element Analysis Methods with Applications for Biomechanical Analysis of Human Aorta. Computer Methods and Programs in Biomedicine. 2023; 107616. [Link]
J22. M. Liu, L. Liang, H. Dong, W. Sun, and R.L. Gleason. Constructing growth evolution laws of arteries via reinforcement learning. Journal of the Mechanics and Physics of Solids. 2022; 168, 105044. [Link]
J21. H. Dong, M. Liu, X. Lou, B. G. Leshnower, W. Sun, B. Ziganshin, M. Zafar, J. A. Elefteriades. Ultimate Tensile Strength and Biaxial Stress–Strain Responses of Aortic Tissues—A Clinical-Engineering Correlation. Applications in Engineering Science. 2022; 10,100101. [Link]
J20. H. Dong, M. Liu, T. Qin, L. Liang, B. Ziganshin, H. Ellauzi, M. Zafar, S. Jang, J. Elefteriades, W. Sun, and R. L. Gleason. A novel computational growth framework for biological tissues: Application to growth of aortic root aneurysm repaired by the V-shape surgery. Journal of the Mechanical Behavior of Biomedical Materials. 2022; 127,105081. [Link]
J19. H. Dong, M. Liu, T. Qin, L. Liang, B. Ziganshin, H. Ellauzi, M. Zafar, S. Jang, J. Elefteriades, and W. Sun. Engineering analysis of aortic wall stress in the surgery of V-shape resection of the noncoronary sinus. Interactive CardioVascular and Thoracic Surgery. 2022. [Link]
J18. M. Liu, L. Liang, Y. Ismail, H. Dong, X. Lou, G. Iannucci, E.P. Chen, B.G. Leshnower, J.A. Elefteriades, and W. Sun. Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model. Computers in Biology and Medicine 2021; 137,104794. [Link]
J17. M. Liu, L. Liang, Q. Zou, Y. Ismail, X. Lou, G. Iannucci, E.P. Chen, B.G. Leshnower, J.A. Elefteriades, and W. Sun. A probabilistic and anisotropic failure metric for ascending thoracic aortic aneurysm risk assessment. Journal of the Mechanics and Physics of Solids 2021; 155,104539. [Link]
J16. M. Liu, L. Liang, and W. Sun. A generic physics-informed neural network-based constitutive model for soft biological tissues. Computer Methods in Applied Mechanics and Engineering 2020; 372, 113402. [Link]
J15. M. Liu, H. Dong, X. Lou, G. Iannucci, E.P. Chen, B.G. Leshnower, and W. Sun. A novel anisotropic failure criterion with dispersed fiber orientations for aortic tissues. Journal of Biomechanical Engineering 2020; 142 (11). [Link]
J14. H. Dong, M. Liu, C. Martin, and W. Sun. A residual stiffness-based model for the fatigue damage of biological soft tissues. Journal of the Mechanics and Physics of Solids 2020; 143,104074. [Link]
J13. M. Liu, L. Liang, F. Sulejmani, X. Lou, G. Iannucci, E. Chen, B. Leshnower, and W. Sun. Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans. Scientific Reports 2019; 9 (1), 1-13. [Link]
J12. M. Liu, L. Liang, and W. Sun. Letter to the editor regarding the paper titled “on the role of material properties in ascending thoracic aortic aneurysms”. Computers in Biology and Medicine 2019; 112: 103373. [Link]
J11. H. Liu, M. Zhang, M. Liu, C. Martin, Z. Cai, and W. Sun. Finite element simulation of three dimensional residual stress in the aortic wall using an anisotropic tissue growth model. Journal of the Mechanical Behavior of Biomedical Materials 2019; 92: 188-196. [Link]
J10. M. Liu, L. Liang, and W. Sun. Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach. Computer Methods in Applied Mechanics and Engineering 2019; 347: 201-217. [Link]
J9. M. Liu, L. Liang, C. Martin, and W. Sun. On the computation of in vivo transmural mean stress of patient-specific aortic wall. Biomechanics and Modeling in Mechanobiology 2018; 18 (2): 387-398. [Link]
J8. L. Liang, M. Liu, C. Martin, and W. Sun. A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta. International Journal for Numerical Methods in Biomedical Engineering 2018; 34 (8): e3103. [Link]
J7. L. Liang, M. Liu, C. Martin, and W. Sun. A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. Journal of The Royal Society Interface 2018; 15 (138): 20170844. [Link]
J6. M. Liu, L. Liang, and W. Sun. Estimation of in vivo mechanical properties of the aortic wall: A multi-resolution direct search approach. Journal of the Mechanical Behavior of Biomedical Materials. 2018; 77: 649-659. [Link]
J5. L. Liang, M. Liu, and W. Sun. A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images. Acta Biomaterialia 2017; 63: 227-235. [Link]
J4. M. Liu, L. Liang, and W. Sun. A new inverse method for estimation of in vivo mechanical properties of the aortic wall. Journal of the Mechanical Behavior of Biomedical Materials 2017; 72: 148-158. [Link]
J3. L. Liang #, M. Liu #, C. Martin, J.A. Elefteriades, and W. Sun. A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm. Biomechanics and Modeling in Mechanobiology 2017; 16 (5): 1519-1533. [Link]
J2. K. Kim, Z. Liang, M. Liu, and D.E. Fan. Biobased high-performance rotary micromotors for individually reconfigurable micromachine arrays and microfluidic applications. ACS Applied Materials & Interfaces 2017; 9 (7): 6144-6152. [Link]
J1. J. Zhao #, M. Liu #, L. Liang, W. Wang, and J. Xie. Airborne particulate matter classification and concentration detection based on 3D printed virtual impactor and quartz crystal microbalance sensor. Sensors and Actuators A: Physical 2016; 238: 379-88. [Link]
#: Co-first Authors
Submitted Manuscripts
S1. M. Liu, W. Sun, and L. Liang. Analysis of aortic rupture: a computational biomechanics perspective. (Book Chapter) to appear in the book: Biomechanics of the Aorta lead by T. Christian Gasser, Stéphane Avril, and John A. Elefteriades.
Full-Length Conference Publications
C3. D. H. Pak, M. Liu, T. Kim, L. Liang, R. McKay, W. Sun, and J.S. Duncan. Distortion Energy for Deep Learning-based Volumetric Finite Element Mesh Generation for Aortic Valves. The 24th International Conference on Medical Imaging Computing & Computer Assisted Intervention 2021 (MICCAI 2021). Strasbourg, France, September 27–October 1, 2021. (full-length) [Link]
C2. D. H. Pak, M. Liu, S.S. Ahn, A. Caballero, J.A. Onofrey, L. Liang, W. Sun, and J.S. Duncan. Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images. Information Processing in Medical Imaging 2021 (IPMI 2021). June 28th – June 30th, 2021. (full-length) [Link]
C1. J. Zhao, M. Liu, W. Wang, and J. Xie. Airborne particulate matter classification and concentration detection based on 3D printed virtual impactor and quartz crystal microbalance sensor. The 29th IEEE International Conference on. Micro Electro Mechanical Systems (MEMS 2016). Shanghai, China, Jan 24-28, 2016. (full-length) [Link]
Abstracts and Presentations
A25. L. Liang, M. Liu, and W. Sun. Integration Of Deep Neural Networks And Finite Element Method For Biomechanical Analysis Of The Aorta. Summer Biomechanics, Bioengineering, and Biotransport Conference 2023 (SB3C 2023). Vail. CO. June 4-8, 2023.
A24. H. Dong, J. Ferruzzi, M. Liu, L. Brewster, and R. Gleason. Effect of aging, sex, and gene (fibulin-5) on the arterial stiffness of mouse: 20 weeks adult mice with fibulin-5 knockout are older than 100 weeks wild-type mice. Summer Biomechanics, Bioengineering, and Biotransport Conference 2023 (SB3C 2023). Vail. CO. June 4-8, 2023.
A23. M. Liu, H. Dong, Y. Wu, A. Mazlout, J.N. Oshinski, W.Sun, J.A. Elefteriades, B.G. Leshnower, and R. L. Gleason. Statistical shape modeling reveals anatomic shape features associated with the outcome of optimal medical therapy in uncomplicated type B aortic dissection. American Heart Association Scientific Sessions 2022* Chicago, IL. November 5–7, 2022.
A22. H. Dong, M. Liu, Y. Zhou, A. Chhabra, J.N. Oshinski, W.Sun, J.A. Elefteriades, B.G. Leshnower, and R. L. Gleason. Mechanical properties of flap and adventitia in type B aortic dissection. American Heart Association Scientific Sessions 2022 Chicago, IL. November 5–7, 2022.
A21. M. Liu, L. Nayak, M. Yigeremu, S. Tekluand, and R. L. Gleason. Statistical shape analysis of 3D women body throughout gestation: toward a novel, low-cost, accurate framework for risk assessment of cephalopelvic disproportion. 5th African Conference on Computational Mechanics (AfriComp 5). Cape Town, South Africa, November 2-4, 2022.
A20. M. Liu, H. Dong, J.A. Elefteriades, B.G. Leshnower, and R. L. Gleason. Spatiotemporal statistical shape analysis of aortic growth in uncomplicated type B aortic dissection. 2022 BMES Annual Meeting. San Antonio, TX. October 12-15, 2022.
A19. H. Dong, M. Liu, W. Wan, and R. L. Gleason. Application of the unified-fiber-distribution (UFD) model to carotid artery of mice with genetic modification (fibulin-5 gene knockout). 2022 BMES Annual Meeting. San Antonio, TX. October 12-15, 2022.
A18. M. Liu, L. Liang, H. Dong, W. Sun, and R. L. Gleason. Determining finite growth laws of arteries with reinforcement learning. The 9th World Congress of Biomechanics 2022 (WCB 2022). Taipei, ROC (Taiwan). July 10-14, 2022.
A17. H. Dong, M. Liu, T. Qin, L. Liang, B. Ziganshin, H. Ellauzi, M. Zafar, S. Jang, J. Elefteriades, W. Sun, and R. L. Gleason. A Novel Computational Growth Framework for Biological Tissues: Application to Growth of Aortic Root Aneurysm Repaired by the V-shape Surgery. The 9th World Congress of Biomechanics 2022 (WCB 2022). Taipei, ROC (Taiwan). July 10-14, 2022.
A16. M. Liu, L. Liang, Q. Zou, Y. Ismail, X. Lou, G. Iannucci, E.P. Chen, B.G. Leshnower, J.A. Elefteriades, and W. Sun. A probabilistic and anisotropic failure metric for risk assessment of ascending thoracic aortic aneurysm. Summer Biomechanics, Bioengineering, and Biotransport Conference 2021 (SB3C 2021). June 14-18, 2021.
A15. H. Dong, M. Liu, T. Qin, L. Liang, B. Ziganshin, H. Ellauzi, M. Zafar, S. Jang, J. Elefteriades, and W. Sun. Engineering analysis of aortic wall stress and root dilation in the V-shape surgery for ascending aortic aneurysms. Summer Biomechanics, Bioengineering, and Biotransport Conference 2021 (SB3C 2021). June 14-18, 2021.
A14. M. Liu, L. Liang, L. Liang, Q. Zou, Y. Ismail, X. Lou, G. Iannucci, E.P. Chen, B.G. Leshnower, J.A. Elefteriades, and W. Sun. A novel biomechanical risk index for ascending thoracic aortic aneurysm. American Association for Thoracic Surgery (AATS) Annual Meeting 2021. April 30 – May 2, 2021.
A13. M. Liu, L. Liang, and W. Sun. A generic constitutive framework for soft biological tissues based on physics-informed neural network. 2020 BMES Virtual Annual Meeting. Jun 14-17, 2020.
A12. H. Dong, M. Liu, T. Qin, B. Ziganshin, H. Ellauzi, J.A. Elefteriades, and W. Sun. Patient-specific finite element analysis of ascending aortic aneurysms with operation of V-shaped resection of the noncoronary sinus. 2020 BMES Virtual Annual Meeting. Jun 14-17, 2020.
A11. H. Dong, M. Liu, C. Martin, and W. Sun.A residual stiffness-based damage model for biological collagenous tissues under long-term cyclic-loading. 2020 ASME Summer Biomechanics Bioengineering and Biotransport Conference (SB3C 2020). Virtual Conference, June 17-20, 2020.
A10. X. Lou, W. Sun, M. Liu, F. Sulejmani, Q. Zou, M.S. AL Jabi, E.P. Chen, B.G. Leshnower. Biomechanical and histological analysis supports increased stiffness and fibrosis in chronic versus acute aortic dissection flaps. American Heart Association’s 2019 Scientific Sessions. Philadelphia, PA. Nov 16-18, 2019. [Link]
A9. M. Liu, L. Liang, X. Lou, G. Iannucci, E. Chen, B. Leshnower, and W. Sun. On the identification of heterogeneous nonlinear material properties of the aortic wall from Clinical Gated CT Scans. International Conference on Biomechanics and Medical Engineering- Yuan-Cheng Fung 100th Birthday Celebration (ICBME 2019). San Diego, CA, Sep 20-23, 2019. [Link]
A8. M. Liu, L. Liang, F. Sulejmani, X. Lou, G. Iannucci, E. Chen, B. Leshnower, and W. Sun. An integrated machine learning-inverse finite element approach for identification of patient specific material properties of the aortic wall from clinical CT images. 2019 ASME Summer Biomechanics Bioengineering and Biotransport Conference (SB3C 2019). Seven Springs, PA, Jun 25-28, 2019.
A7. X. Lou, W. Sun, F. Sulejmani, M. Liu, E. Chen, B. Leshnower. Biomechanical analysis of acute versus chronic aortic dissection flaps. ACTS Translational Science 2019. Washington, DC, Mar 5-8, 2019. [Link]
A6. M. Liu, L. Liang, F. Sulejmani, G. Iannucci, E. Chen, B. Leshnower, and W. Sun. Estimation of in vivo material parameters of the aortic wall using multi-phase CT data. 2018 BMES Annual Meeting. Atlanta, GA, Oct 17-20, 2018.
A5. M. Liu, L. Liang, B. G. Leshnower, and W. Sun. Estimation of in vivo mechanical properties of the human thoracic aorta using neural networks. The 13th International Symposium on Biomechanics in Vascular Biology and Cardiovascular Disease. Atlanta, GA, Apr 12-13, 2018.
A4. M. Liu, L. Liang, and W. Sun. Estimation of in vivo mechanical properties of the aortic wall. The 5th International Conference on Computational and Mathematical Biomedical Engineering (CMBE 2017). Pittsburgh, PA, Apr 10-12, 2017.
A3. L. Liang, M. Liu, and W. Sun. Estimation of collagenous tissue elastic property from microscopy images using deep learning. The 5th International Conference on Computational and Mathematical Biomedical Engineering (CMBE 2017). Pittsburgh, PA, Apr 10-12, 2017.
A2. K. Kim, Z. Liang, M. Liu, and D.E. Fan. High-performance rotary micromachines based on biomaterials and their applications in microfluidics. 2016 MRS Fall Meeting & Exhibit. Boston, MA, Nov 27-Dec 2, 2016.
A1. K. Kim, M. Liu, and D.E. Fan. Bioinspired Rotary Micromachines for Microfluidic Applications. ASME 2016 5th Global Congress on NanoEngineering for Medicine and Biology (NEMB 2016). Houston, TX, Feb 21-24, 2016.
Patents
P3. W. Sun, J. Elefteriades, L. Liang, M. Liu, and C. Martin. Machine Learning-based Approach for Noninvasive Assessment of Ascending Aortic Aneurysm Pressure Rupture Risk Application. US Patent App. 62/385,357
P2. J. Xie, J. Zhao, R. Zhao, and M. Liu. CN Patent CN205,506,606 U. [Link]
P1. J. Xie, J. Zhao, R. Zhao, and M. Liu. CN Patent CN105,651,643 A. [Link]