Algorithms, Available technologies, Engineering, Healthcare, Information and communications technology (ICT), Life Sciences, Medical devices, Medical Hardware, Medical Software, Signal and Data Processing

METHOD AND SYSTEM FOR ANALYZING 2D TISSUE DEFORMATION WITH ULTRASOUND IMAGING

Robust and simple method/system of image processing for evaluating tissue deformability to quantify stiffness and identify rigid areas (tumors) in soft tissues using ultrasound imaging techniques

The need of advanced methods and technologies in medical ultrasound imaging

  • In the context of medical imaging, tissue deformation represents a precious aid for the diagnosis of diseases
  • Ultrasound elastography imaging segment accounted for the largest share of 99.9% of the elastography imaging market in 2018
  • This segment is projected to reach USD 4,642.5 million by 2024 from USD 2,481.9 million in 2018, at a CAGR of 11.0%
  • A difficulty is that ultrasonic images are of poor quality and very sensitive to the way the probe is manipulated, which makes it difficult to locate the correlation peak required by traditional quasistatic elastography methods in a robust way
  • 1D deformation measurements are usually obtained from pre- and post-compression RF signals for each individual
  • One commonly used methods is the normalized cross-correlation algorithm (NCC), which is based on locating the peak of the cross-correlation function

Robust method to calculate 2D index for tissue deformation

  • A new index for global 2D tissue deformation analysis without correlation
  • Foregoing correlation-based tissue tracking to estimate the tissue deformation
  • Using single scalar value to characterize the deformation in a robust fashion
  • Assessing the global deformation of tissues with relatively homogeneous stiffness (such as muscles) even if it is difficult to find a correspondence between pre- and post-compression frames
  • Technology developed by Prof. Catherine Laporte, Arnaud Brignol (Department of Electrical Engineering, École de technologie supérieure (ÉTS)) and Prof. Farida Cheriet (Department of Computer Engineering and Software Engineering, Polytechnique Montréal)

2D tissue deformation model presents multiple competitive advantages

  • Quantifying stiffness and identifying rigid areas (tumors) in soft tissues using ultrasound imaging techniques
  • Robustness in the case of undesirable movements during the acquisition and for structures of homogeneous appearance
  • Possibility of globally estimating the deformation of the tissue by selecting one static global area of interest (ROI), no need to locate some markers.
  • Possibility to assess the deformation between two frames even if they are highly decorrelated (as may be the case with a large deformation or in the presence of out-of-plane motion or high noise levels), as long as the structure of interest remains visible
  • In the case where the deformation index is correlated with the biomechanical properties of the tissues, the possibility of avoiding finite element approaches that require knowledge of the Young’s modulus and to use a generalized spring mass approach that would be based on constraints of incompressibility deduced from the deformation index

Business opportunity

  • Technology available for licensing
  • Co-development, partnering

CONTACT

If you are interested by this technology, please contact :
Pierre des Lierres, Director Business Development, Engineering
PdesLierres@aligo.ca, (514) 571-6556

UNIVERSITY

École de Technologie supérieure (ÉTS)

Main inventors

catherine-laporte

Catherine Laporte, Professor, Electrical Engineering

Catherine Laporte obtained the B. Ing. degree in computer engineering from École Polytechnique de Montréal in 2001.  She worked as a software designer at Matrox in 2001-2002 before pursuing a M. Eng. degree at McGill University in the field of computer vision, which she obtained in 2004.  She then obtained the Ph.D. degree from McGill University in 2010 for her research on sensorless freehand 3D ultrasound.  Since 2010, she is a professor at the department of electrical engineering at École de technolgie supérieure, where she teaches courses in the fields of computer science and biomedical engineering, and conducts research in the field of medical image analysis

arnaud

Arnaud Brignol, Researcher, Electrical Engineering

Arnaud Brignol obtained an engineering degree from ESIEE, France (2011) and a master’s degree from Université de Montréal (2015) in computer science. He is currently a PhD student in medical image analysis under the supervision of Prof. C. Laporte in the Department of Electrical Engineering at École de technologie supérieure. His research focuses on the 3D analysis of spinal deformities due to adolescent idiopathic scoliosis, where ultrasound imaging is being considered as a safe alternative to X-ray imaging for tracking the evolution of the disease. He finds his passion in applying concepts from nonlinear analysis methods and chaos theory to tackle the challenges encountered in the field of medical imaging and signal processing

farida

Farida Cheriet, Professor, Computer and Software Engineering, Polytechnique Montreal

Farida Cheriet received the B.Sc. degree in computer science from the University USTHB, Algiers, in 1984, the D.E.A. degree in the field of languages, algorithms and programming from the University of Paris VI, France, in 1986, and the Ph.D. degree in computer science from the University of Montreal, QC, Canada, in 1996. She held a postdoctoral position at the Biomedical Engineering Institute, Polytechnique Montreal, from 1997 to 1999. Since 1999, she has been appointed in the Department of Computer and Software Engineering, Polytechnique Montreal, where she is currently a full Professor. She is an IEEE Senior member and her research interests include three–dimensional (3-D) reconstruction of anatomical structures from medical images, 3-D Augmented Reality systems for minimally invasive surgery and Computer-Assisted Diagnosis Systems in orthopedics, cardiology and ophthalmology.