Theme: Quantitative Ultrasound


Background

The Quantitative Ultrasound research line at MUSIC covers a wide range of aspects from improving image quality to tissue characterization using various quantitative parameters as well as artificial intelligence (AI) like deep learning techniques to asses features from echograms. For example, we perform research on quantitative ultrasound techniques for automated head circumference measurement, breech position detection, improving breast tumor classification, staging of hepatic steatosis, and detection and differentiation of musculoskeletal diseases. For that, we derive quantitative parameters representing geometrical features or quantitative echographic features (brightness, speckle size, attenuation of the echo signal etc.,). Additionally, we develop image formation methods to improve the image quality like beamforming methods.


Research/Developments*

  • Image improvement
    1. Plane wave imaging beamforming and reconstruction methods
      Plane-wave imaging is a novel ultrasound technique featured high framerate up to 10000 fps. It is used in many applications in which high framerate is required or desired, like flow-imaging, elastography and 3D ultrasound imaging. In this technique, the entire transducer is employed to transmit unfocused wave and receive backscattered signals simultaneously. This contrasts with conventional imaging in which small focused ultrasound beams are transmitted and received to construct an image line-by-line. To improve the image quality in plane-wave imaging, we are investigating coherent compounding (combining steered acquisitions) and developing advanced filtering techniques for Fourier-based image reconstruction (beamforming) like angular weighting and PSF-based deconvolution. Furthermore, we have interest in comparing and optimizing the image quality of conventional imaging, plane-wave imaging and beamforming and our developed filtering techniques in on-going phantom and in-vivo studies.


  • Image analysis
    1. AI for automated guidance and interpretation of ultrasound
      Ultrasound imaging required a well-trained sonographer to both acquire and interpret the images. By combining AI, a standardized acquisition protocol and hand-held ultrasound devices it becomes possible to vastly reduce training of sonographers. The AI systems can be used to aid the user during the acquisition of the ultrasound data and give real-time feedback to the user. When a standardized acquisition protocol is completed, the algorithms automatically interpret the ultrasound images and reports the results back to the user. This makes ultrasound imaging accessible to a wide range of users that were previously not able to perform ultrasound imaging. We are developing such algorithms for automated prenatal screening and COVID-19 classification.
    2. Tissue Characterization
      3D Muscle (MURAB)
      By using automated ultrasound on skeletal muscle, 3D imaging can be used for the evaluation of neuromuscular disorders. Imaging volumes are created by stacking 2D images in the elevational direction. The addition of the third dimension introduces new parameters that characterize muscle tissue such as muscle size and muscle architecture. Automated ultrasound is commercially available and can therefore offers a safe tool for many clinical applications. Combined with methods for quantitative muscle ultrasound this method is ready to study and evaluate patients.
      Computer Aided UltraSound (CAUS)
      Music developed a quantitative ultrasound (QUS) image analysis method called CAUS. CAUS determines various QUS parameters semi-automatically like the ”attenuation”, “mean echo level”, and “speckle size”, and express them relatively to commercial available reference phantom which mimics a healthy liver. The latter turns CAUS into a generic method which can be applied to a broad range of ultrasound modalities having numerous applications.
      QA4US
      MUSIC developed a Quality Assurance program 4 UltraSound equipment QA4US. Based on theoretical considerations, a minimum set of parameters to determine and follow-up the quality of an ultrasound machine and transducer combination.

Clinical applications

BREAST
LIVER
LUNG
OB-GYN
MUSCLE

Key Publications

  • G. Ferraioli, V. Kumar, A. Ozturk, K. Nam, C. de Korte and R. Barr. "US Attenuation for Liver Fat Quantification: An AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative.", 2022. Abstract/PDF DOI PMID

  • G. Hendriks, H. Hansen, C. De Korte and C. Chen. "Optimization of transmission and reconstruction parameters in angular displacement compounding using plane wave ultrasound.", 2020. Abstract/PDF DOI PMID

  • T. van den Heuvel, H. Petros, S. Santini, C. de Korte and B. van Ginneken. "Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries.", 2019. Abstract DOI PMID

  • C. Chen, G. Hendriks, R. van Sloun, H. Hansen and C. de Korte. "Improved Plane-Wave Ultrasound Beamforming by Incorporating Angular Weighting and Coherent Compounding in Fourier Domain.", 2018. Abstract/PDF DOI PMID

  • G. Hendriks, C. Chen, H. Hansen and C. de Korte. "Quasi-static elastography and ultrasound plane-wave imaging: The effect of beam-forming strategies on the accuracy of displacement estimations", 2018. PDF

  • T. van den Heuvel, D. de Bruijn, C. de Korte and B. Ginneken. "Automated measurement of fetal head circumference using 2D ultrasound images.", 2018. Abstract DOI PMID

  • A. van den Abeelen, G. Weijers, J. van Zelst, J. Thijssen, R. Mann and C. de Korte. "3D quantitative breast ultrasound analysis for differentiating fibroadenomas and carcinomas smaller than 1cm", 2017. Abstract/PDF DOI PMID

  • T. van den Heuvel, D. Graham, K. Smith, C. de Korte and J. Neasham. "Development of a Low-Cost Medical Ultrasound Scanner Using a Monostatic Synthetic Aperture.", 2017. Abstract DOI PMID

  • B. Holländer, G. Hendriks, R. Mann, H. Hansen and C. de Korte. "Plane-Wave Compounding in Automated Breast Volume Scanning: A Phantom-Based Study.", 2016. Abstract/PDF DOI PMID

  • G. Weijers, G. Wanten, J. Thijssen, M. van der Graaf and C. de Korte. "Quantitative Ultrasound for Staging of Hepatic Steatosis in Patients on Home Parenteral Nutrition Validated with Magnetic Resonance Spectroscopy: A Feasibility Study.", 2016. Abstract/PDF DOI PMID

  • G. Weijers, A. Starke, J. Thijssen, A. Haudum, P. Wohlsein, J. Rehage and C. de Korte. "Transcutaneous vs. intraoperative quantitative ultrasound for staging bovine hepatic steatosis", 2012. Abstract/PDF DOI PMID

  • G. Weijers, A. Starke, A. Haudum, J. Thijssen, J. Rehage and C. De Korte. "Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis", 2010. Abstract/PDF URL PMID

  • J. Thijssen, A. Starke, G. Weijers, A. Haudum, K. Herzog, P. Wohlsein, J. Rehage and C. De Korte. "Computer-aided B-mode ultrasound diagnosis of hepatic steatosis: a feasibility study", 2008. Abstract/PDF DOI PMID

  • J. Thijssen, G. Weijers and C. de Korte. "Objective performance testing and quality assurance of medical ultrasound equipment", 2007. Abstract/PDF DOI PMID




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