Kathryn R Nightingale

Nightingale

Theo Pilkington Distinguished Professor of Biomedical Engineering

The goals of our laboratory are to investigate and improve ultrasonic imaging methods for clinically-relevant problems. We do this through theoretical, experimental, and simulation methods. The main focus of our recent work is the development of novel, acoustic radiation force impulse (ARFI)-based elasticity imaging methods to generate images of the mechanical properties of tissue, involving interdisciplinary research in ultrasonics and tissue biomechanics. We have access to the engineering interfaces of several commercial ultrasound systems which allows us to design, rapidly prototype, and experimentally demonstrate custom sequences to explore novel beamforming and imaging concepts. We employ FEM modeling methods to simulate the behavior of tissues during mechanical excitation, and we have integrated these tools with ultrasonic imaging modeling tools to simulate the ARFI imaging process. We maintain strong collaborations with the Duke University Medical Center where we work to translate our technologies to clinical practice. The ARFI imaging technologies we have developed have served as the basis for commercial imaging technologies that are now being used in clinics throughout the world.  We are also studying the risks and benefits of increasing acoustic output energy for specific clinical imaging scenarios, with the goal of improving ultrasonic image quality in the difficult-to-image patient.

Appointments and Affiliations

  • Theo Pilkington Distinguished Professor of Biomedical Engineering
  • Professor in the Department of Biomedical Engineering
  • Member of the Duke Cancer Institute
  • Bass Fellow

Contact Information

Education

  • Ph.D. Duke University, 1997
  • B.S. Duke University, 1989

Research Interests

Ultrasonic and elasticity imaging, specifically nonlinear propagation, acoustic streaming and radiation force; the intentional generation of these phenomena for the purpose of tissue characterization; finite element modeling of normal and diseased tissue when exposed to ultrasound, and performing both phantom and clinical experiments investigating these phenomena. Other areas of interest include prostate imaging, abdominal imaging, image-guided therapies, and the bioeffects of ultrasound.

Awards, Honors, and Distinctions

  • Lois and John L. Imhoff Distinguished Teaching Award. Pratt School of Engineering. 2018
  • Fellow. American Institute for Medical and Biological Engineering. 2016
  • Capers and Marion McDonald Teaching and Research Award. Pratt School of Engineering. 2015
  • Klein Family Distinguished Teaching Award. Pratt School of Engineering. 2007

Courses Taught

  • BME 354L: Introduction to Medical Instrumentation
  • BME 493: Projects in Biomedical Engineering (GE)
  • BME 494: Projects in Biomedical Engineering (GE)
  • BME 790L: Advanced Topics with the Lab for Graduate Students in Biomedical Engineering
  • BME 791: Graduate Independent Study

In the News

Representative Publications

  • Morris, DC; Chan, DY; Lye, TH; Chen, H; Palmeri, ML; Polascik, TJ; Foo, W-C; Huang, J; Mamou, J; Nightingale, KR, Multiparametric Ultrasound for Targeting Prostate Cancer: Combining ARFI, SWEI, QUS and B-Mode., Ultrasound Med Biol (2020) [10.1016/j.ultrasmedbio.2020.08.022] [abs].
  • Caenen, A; Knight, AE; Rouze, NC; Bottenus, NB; Segers, P; Nightingale, KR, Analysis of multiple shear wave modes in a nonlinear soft solid: Experiments and finite element simulations with a tilted acoustic radiation force., Journal of the Mechanical Behavior of Biomedical Materials, vol 107 (2020) [10.1016/j.jmbbm.2020.103754] [abs].
  • Rouze, NC; Palmeri, ML; Nightingale, KR, Tractable calculation of the Green's tensor for shear wave propagation in an incompressible, transversely isotropic material., Physics in Medicine and Biology, vol 65 no. 1 (2020) [10.1088/1361-6560/ab5c2d] [abs].
  • Morris, DC; Chan, DY; Chen, H; Palmeri, ML; Polascik, TJ; Foo, WC; Huang, J; Mamou, J; Nightingale, KR, Multiparametric Ultrasound for the Targeting of Prostate Cancer using ARFI, SWEI, B-mode, and QUS, Ieee International Ultrasonics Symposium, Ius, vol 2019-October (2019), pp. 880-883 [10.1109/ULTSYM.2019.8926035] [abs].
  • Jin, FQ; Postiglione, M; Knight, AE; Cardones, AR; Nightingale, KR; Palmeri, ML, Comparison of Deep Learning and Classical Image Processing for Skin Segmentation, Ieee International Ultrasonics Symposium, Ius, vol 2019-October (2019), pp. 1152-1155 [10.1109/ULTSYM.2019.8926233] [abs].