Hugo Giambini, Ph.D.
My current research addresses the mechanical and biomechanical factors influencing hard and soft tissue integrity and performance, as well as non-invasive tissue assessment and modeling using medical imaging. My research interests lie in using biomechanics and imaging tools to improve predictive methods and better understand pathogenesis of musculoskeletal conditions. The long-term goal of my research is to develop clinical tools to enable earlier diagnosis, prescribe effective interventions, and assess outcomes for individuals with musculoskeletal disorders.
Two main projects in the lab relate to 1) the development of subject-specific vertebral fracture risk prediction tools using finite element modeling and medical imaging (CT and MRI); and 2) the implementation of MRI and shear wave elastography to evaluate rotator cuff muscle properties and aid during surgical planning and the rehabilitation process.
The laboratory is involved in basic science and translational research. There exist strong collaborations with clinicians and scientists in many areas including radiology, orthopedics, mechanical and biomedical engineering.
Giambini H, Dragomir-Daescu D, Huddleston PM, Camp JJ, An KN, Nassr A. The Effect of Quantitative Computed Tomography Acquisition Protocols on Bone Mineral Density Estimation. J Biomech Eng. 2015 Nov;137(11):114502. doi: 10.1115/1.4031572. PubMed PMID: 26355694; PubMed Central PMCID: PMC4844109.
Hatta T, Giambini H, Uehara K, Okamoto S, Chen S, Sperling JW, Itoi E, An KN. Quantitative assessment of rotator cuff muscle elasticity: Reliability and feasibility of shear wave elastography. J Biomech. 2015 Nov 5;48(14):3853-8. doi: 10.1016/j.jbiomech.2015.09.038. Epub 2015 Oct 9. PubMed PMID: 26472309; PubMed Central PMCID: PMC4655159.
Giambini H, Qin X, Dragomir-Daescu D, An KN, Nassr A. Specimen-specific vertebral fracture modeling: a feasibility study using the extended finite element method. Med Biol Eng Comput. 2016 Apr;54(4):583-93. doi: 10.1007/s11517-015-1348-x. Epub 2015 Aug 4. PubMed PMID: 26239163; PubMed Central PMCID: PMC4852468.
Giambini H, Fang Z, Zeng H, Camp JJ, Yaszemski MJ, Lu L. Noninvasive Failure Load Prediction of Vertebrae with Simulated Lytic Defects and Biomaterial Augmentation. Tissue Eng Part C Methods. 2016 Aug;22(8):717-24. doi: 10.1089/ten.TEC.2016.0078. Epub 2016 Jun 29. PubMed PMID: 27260559; PubMed Central PMCID: PMC4991609.
Giambini H, Dragomir-Daescu D, Nassr A, Yaszemski MJ, Zhao C. Quantitative Computed Tomography Protocols Affect Material Mapping and Quantitative Computed Tomography-Based Finite-Element Analysis Predicted Stiffness. J Biomech Eng. 2016 Sep 1;138(9). doi: 10.1115/1.4034172. PubMed PMID: 27428281; PubMed Central PMCID: PMC4967881.
Giambini H, Hatta T, Krzysztof GR, Widholm P, Karlsson A, Leinhard O, Adkins MC, Zhao C, An K. Intramuscular fat infiltration evaluated by magnetic resonance imaging predicts the extensibility of the supraspinatus muscle. Muscle and Nerve. 2017 Apr 25. doi: 10.1002/mus.25673.
Hatta T, Giambini H, Itoigawa Y, Hooke AW, Sperling JW, Steinmann SP, Itoi E, An KN. Quantifying extensibility of rotator cuff muscle with tendon rupture using shear wave elastography: A cadaveric study. J Biomech. 2017 Aug 16;61:131-136. doi: 10.1016/j.jbiomech.2017.07.009. Epub 2017 Jul 21.
Giambini H, Hatta T, Rezaei A, An KN. Extensibility of the supraspinatus muscle can be predicted by combining shear wave elastography and magnetic resonance imaging-measured quantitative metrics of stiffness and volumetric fat infiltration: A cadaveric study. Clin Biomech (Bristol, Avon). 2018 Aug;57:144-149. doi: 10.1016/j.clinbiomech.2018.07.001. Epub 2018 Jul 3.