Mike Insana

Research Interests

• Advance perfusion imaging methods
• Machine learning for medical elasticity imaging
• Medical imaging systems development particularly for applications breast cancer, vascular disease, and kidney disease. Ultrasonics, signal processing, signal detection, elasticity imaging, soft tissue biomechanics, polymer mechanics, complex system modeling.

Research Statement

Laboratory research focuses on the development of novel ultrasonic instrumentation and methods for imaging soft tissue microstructure, viscoelasticity and blood flow. The goal is to understand basic mechanisms of cancerous lesion formation, metastatic progression, responses to therapy, and sources of image contrast. Research includes the fundamentals of imaging system design and performance evaluation, signal processing, detection and estimation. The lab uses polymer hydrogels to develop models of visco- and poroelastic behavior of soft tissues for cancer imaging. Insana's lab also investigates spatio-temporal filtering for noise reduction and enhanced spatial resolution with applications in breast elasticity imaging and arterial-wall shear-stress estimation.

Graduate Research Opportunities

I now supervise 2 graduate students.

Primary Research Area

• Bioimaging at Multi-Scale

Research Areas

• Complex system modeling
• Medical imaging systems - particularly for applications in breast cancer and vascular and kidney diseases
• Polymer mechanics
• Signal detection
• Signal processing
• Soft tissue biomechanics
• Ultrasound

Books Authored or Co-Authored (Original Editions)

• Michael F. Insana, "Biomedical Measurement and Data Science," Cambridge University Press, UK, 2021.
• Jamshid Ghaboussi and Michael F. Insana, "Understanding Systems: A Grand Challenge for 21st Century Engineering," World Scientific Publishing, Singapore, 2017.

Books Edited or Co-Edited (Original Editions)

• M.F. Insana and S.A. Wickline (editors), Proceedings of the IEEE, Volume 96, No. 3, Special Issue: "Multimodality Biomolecular Imaging", March Issue 2008.

Selected Articles in Journals

• Babaei S, Dobrucki WL, Insana MF, “Power-Doppler ultrasonic imaging of peripheral perfusion in diabetic mice,” IEEE Trans. Biomed. Eng., (in press).
• Zhang H, Liang S, Matkovic LA, Momin S, Wang K, Yang X, Insana MF. "Deep Q-learning to globally optimize a k-D parameter search for medical imaging." Quant Imaging Med Surg;13(8):4879-4896, 2023.
• Babaei S, Dai B, Abbey CK, Ambreen Y, Dobrucki LW, Insana, MF, “Monitoring muscle perfusion in rodents during short-term ischemia using power-Doppler ultrasound,” Ultrasound Med. Biol., 49(6):1465-1475, 2023.
• Insana MF, Dai B, Babaei S, Abbey CK, “Combining spatial registration with clutter filtering for power-Doppler imaging in peripheral perfusion applications,” IEEE Trans Ultrason Ferroelec Freq Control, 69(12): 3243-3254, 2022.
• Wang Y, Ghaboussi J, Hoerig C, Insana MF, “A data-driven approach to characterizing nonlinear elastic behavior of soft materials,” J Mechan Behav Biomed Materials, 130: 105178, 2022.
• Kalyanam S, Toohey KS, Insana MF, “Modeling biphasic hydrogels under spherical indentation: application to soft tissues,” Mechanics of Materials, 161: 103987, 2021.
• Zhang H, Lu T, Zhang Q, Zhou Y, Zhu H, Harms J, Yang X, Wan M, Insana M.F., Solutions to ramp-hold dynamic oscillation indentation tests for assessing the viscoelasticity of hydrogel by Kelvin-Voigt fractional derivative modeling, Mech. Mater., 148, 103431, 2020.
• Bayat M, Nabavizadeh A, Nayak R, Webb JM, Gregory AV, Meixner DD, Fazzio RT. Insana MF, Alizad A, Fatemi M, Multi-parameter sub-Hertz analysis of viscoelasticity with quality metrics for differentiation of breast masses, Ultrasound Med Biol., 46(12):3393-3403, 2020.
• Hedhli J, Kim M, Knox HJ, Cole JA, Huynh T, Schuelke M, Dobrucki IT, Kalinowski L, Chan J, Sinusas AJ, Insana MF, Dobrucki LW, Imaging the landmarks of vascular recovery, Theranostics, 10(4):1733-1745, 2020.
• Hoerig C, Ghaboussi J, Insana MF, Data-driven elasticity imaging using Cartesian neural network constitutive models and the autoprogressive method, IEEE Trans Med Imaging 38(5):1150-1160, 2019.
• Kim M-W, Zhu Y, Hedhli J, Dobrucki LW, Insana MF, Multi-dimensional clutter filter optimization for ultrasonic perfusion imaging, IEEE Trans Ultrason Ferroelec Freq Control, 65(11): 2020-2029, 2018.
• Bayat M, Nabavizadeh A, Kumar V, Gregory A, Kinnick RR, Meixner D, Insana M, Alizad A, Fatemi M, Automated in vivo sub-Hertz analysis of viscoelasticity (SAVE) for evaluation of breast lesions, IEEE Trans Biomed Eng, 65(10): 2237-2247, 2018.
• Zhang H-M, Zhang Q, Ruan L, Duan J, Wan M, Insana MF, Modeling ramp-hold indentation measurements based on Kelvin-Voigt fractional derivative model, Meas Sci Technol, 29(3):035701, 2018.
• Zhu Y, Han A, O'Brien Jr. WD, Oelze ML, Insana MF, Analysis of scatterer size estimation using ultrasonic backscatter. J Acoust Soc Am, 142(6):3677-3690, 2017.
• Kim M-W, Abbey CK, Hedhli J, Dobrucki WL, Insana MF: Expanding acquisition and clutter filter dimensions for improved perfusion sensitivity, IEEE Trans Ultrason Ferroelec Freq Control, 64(10), 1429-1438, 2017
• Abbey CK, Zhu Y, Bahramian S, Insana MF: Linear system models for ultrasonic imaging: intensity signal statistics, IEEE Trans Ultrason Ferroelec Freq Control, 64(4): 669-678, 2017.
• Zhang H, Wang Y, Fatemi M, Insana MF: Assessing composition and structure of soft biphasic media from Kelvin-Voigt fractional derivative model parameters, Meas Sci Technol, 28 035703 (12pp), 2017.
• Nguyen NQ, Prager R, Insana MF: Improvements to ultrasonic beamformer design and implementation derived from the task-based analytical framework, J. Acoust Soc Am, 141 (6), 4427-4437, 2017.
• Zhang H, Wang Y, Insana MF: Ramp-hold relaxation solutions for the KVFD model applied to soft viscoelastic media, Meas Sci Technol, 27(2):025702 (11 pages), 2016.
• Nguyen NQ, Prager R, Insana MF: A task-based analytical framework for ultrasonic beamformer comparison, J Acoust Soc Am, 140(2): 1048-1059, 2016.
• Altahhan KN, Wang Y, Sobh N, Insana MF: Indentation measurements to validate dynamic elasticity imaging methods, Ultrasonic Imaging, 2015 Sep 16. pii: 0161734615605046.
• Qayyum M, Kwak JT, Insana MF: Stromal-epithelial responses to fractionated radiotherapy in a breast cancer microenvironment,” Cancer Cell International, 15:67, 2015. DOI:10.1186/s12935-015-0218-9.
• Wang Y, Insana MF: Viscoelastic properties of rodent mammary tumors using ultrasonic shear wave imaging, Ultrasonic Imaging 35(2):126-145, 2013
• Abbey CK, Nguyen NQ, Insana MF: Effects of frequency and bandwidth on diagnostic information transfer in ultrasonic B-mode imaging, IEEE Trans Ultrason Ferro Freq Control, 59(6):1115-1126, 2012
• Qayyum M, Insana MF: Stromal responses to fractionated radiotherapy, Internat J Rad Biol, 88(5):383-392, 2012 
• Coussot, C, Kalyanam, S, Yapp RD, Insana, MF, "Fractional derivative models for ultrasonic characterization of polymer and breast tissue viscoelasticity," IEEE Trans Ultrason Ferro Freq Contrl 56:715-26, 2009.
• Yapp RD, Insana MF, "pH-induced contrast in viscoelasticity imaging of biopolymers," Phys Med Biol 54:1089-1109, 2009.
• Qiu Y, Sridhar M, Tsou JK, Lindfors KK, Insana MF, "Ultrasonic viscoelasticity imaging of nonpalpable breast tumors: preliminary results," Academic Radiol, 15:1526-33, 2008.
• Insana MF, Wickline SA, "Multimodality biomolecular imaging," , Proc IEEE, vol 96, pp 378-81, 2008.
• Tsou JK, Gower RM, Ting HJ, Schaff UY, Insana MF, Passerini AG, Simon SI, "Spatial regulation of inflammation by human aortic endothelial cells in a linear gradient of shear stress," Microcirculation 15:311-23, 2008.
• Tsou JK, Simon SI, Barakat AI, Insana MF, "Role of ultrasonic shear rate estimation errors in assessing inflammatory response and vascular risk," Ultrasound Med Biol, 34: 963-972, 2008.

Articles in Conference Proceedings

• Newman WR, Ghaboussi J, Insana MF, “Improving image quality in a new method of data-driven elastography,” Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 12932-9 (6 pages), 2024.
• Zhou Y, Wang K, Zhang K, Wan M, Fatemi M, Insana MF, Zhang H, “Deep reinforcement learning of Kelvin-Voigt fractional derivative parameters for viscoelastic imaging,” Proc IEEE Ultrasonics Symposium, 4 pages, Venice, Italy, October 10-13, 2022.
• Dai B, Babaei S, Abbey CK, Insana MF, “Registration methods in power Doppler ultrasound for peripheral perfusion imaging,” SPIE Medical Imaging, MI-108. 15 pages. San Diego CA, February 20-24, 2022.
• Zhou Y, Wang K, Zhang K, Wan M, Fatemi M, Insana MF, Zhang H, “Deep reinforcement learning of KVFD viscoelastic imaging parameters,” Proc IEEE Ultrasonics Symposium, 4 pages, Xi’an, China (virtual) September 12-16, 2021.
• Insana MF, "Statistical ultrasonics: the influence of Robert F. Wagner," Proc SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, vol. 7263, pp. 72630E1-6, 2009.

Pending Articles

• Babaei S, Dobrucki WL, Phillips H, Insana MF. “Power-Doppler ultrasound for monitoring peripheral muscle perfusion in a porcine model of progressive hindlimb ischemia,” J. Med. Imaging, (in review).

Honors

• Life Fellow of the IEEE - 2020 (2020)
• Fellow of the Acoustical Society of America - 2009
• Fellow of the Institute of Physics - 1999