Kathryn R 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


  • 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 791: Graduate Independent Study

In the News

Representative Publications

  • Knight, AE; Trutna, CA; Rouze, NC; Hobson-Webb, LD; Caenen, A; Jin, FQ; Palmeri, ML; Nightingale, KR, Full Characterization of in vivo Muscle as an Elastic, Incompressible, Transversely Isotropic Material using Ultrasonic Rotational 3D Shear Wave Elasticity Imaging., Ieee Trans Med Imaging, vol PP (2021) [10.1109/TMI.2021.3106278] [abs].
  • Zhang, B; Pinton, GF; Nightingale, KR, On the Relationship between Spatial Coherence and In Situ Pressure for Abdominal Imaging., Ultrasound in Medicine & Biology, vol 47 no. 8 (2021), pp. 2310-2320 [10.1016/j.ultrasmedbio.2021.03.008] [abs].
  • Morris, DC; Chan, DY; Palmeri, ML; Polascik, TJ; Foo, W-C; Nightingale, KR, Prostate Cancer Detection Using 3-D Shear Wave Elasticity Imaging., Ultrasound Med Biol, vol 47 no. 7 (2021), pp. 1670-1680 [10.1016/j.ultrasmedbio.2021.02.006] [abs].
  • Chan, DY; Morris, DC; Polascik, TJ; Palmeri, ML; Nightingale, KR, Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging., Ieee Trans Ultrason Ferroelectr Freq Control, vol 68 no. 7 (2021), pp. 2472-2481 [10.1109/TUFFC.2021.3068377] [abs].
  • Jin, FQ; Knight, AE; Cardones, AR; Nightingale, KR; Palmeri, ML, Semi-automated weak annotation for deep neural network skin thickness measurement., Ultrason Imaging, vol 43 no. 4 (2021), pp. 167-174 [10.1177/01617346211014138] [abs].