IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Department of Applied Mechanics

World University root node

Fulltext

Effect of Lesion Boundary Conditions on Axial Strain Elastograms: A Parametric Study

Assessing image quality in effective Poisson's ratio elastography and poroelastography: I

Poroelastography is a novel elastographic technique for imaging the time variation of the mechanical behaviour of poroelastic materials. Poroelastograms are generated as a series of time-sequenced effective Poisson's ratio (EPR) elastograms, obtained from the imaged material under sustained compression. In the companion report (Righetti et al. 2007 Phys. Med. Biol. 52 1303), we investigated image quality of EPR elastography starting from a theoretical analysis of the performance limitations of axial strain elastography and lateral strain elastography. In this report, we extend this analysis to poroelastography. The theoretical analysis reported in these two companion papers allows understanding the performance limitations of these novel techniques and identifying the fundamental parameters that control their signal-to-noise ratio, contrast-to-noise ratio and resolution. The results of these studies also indicate that EPR elastograms and poroelastograms of reasonable image quality can be generated in practical applications that may be of clinical interest provided that advanced elastographic techniques in combination with other commonly employed imaging methods to increase signal-to-noise and contrast-to-noise ratios are used. © 2007 IOP Publishing Ltd.

Department of Metallurgical and Materials Engineering

Physics in Medicine and Biology

Assessing image quality in effective Poisson's ratio elastography and poroelastography: II

In axial-shear strain elastography, the local axial-shear strain resulting from the application of quasi-static axial compression to an inhomogeneous material is imaged. In this paper, we investigated the image quality of the axial-shear strain estimates in terms of the signal-to-noise ratio (SNR asse) and contrast-to-noise ratio (CNRasse) using simulations and experiments. Specifically, we investigated the influence of the system parameters (beamwidth, transducer element pitch and bandwidth), signal processing parameters (correlation window length and axial window shift) and mechanical parameters (Young's modulus contrast, applied axial strain) on the SNRasse and CNRasse. The results of the study show that the CNRasse (SNRasse) is maximum for axial-shear strain values in the range of 0.005-0.03. For the inclusion/background modulus contrast range considered in this study (&lt;10), the CNRasse (SNR asse) is maximum for applied axial compressive strain values in the range of 0.005%-0.03%. This suggests that the RF data acquired during axial elastography can be used to obtain axial-shear strain elastograms, since this range is typically used in axial elastography as well. The CNRasse (SNRasse) remains almost constant with an increase in the beamwidth while it increases as the pitch increases. As expected, the axial shift had only a weak influence on the CNRasse (SNRasse) of the axial-shear strain estimates. We observed that the differential estimates of the axial-shear strain involve a trade-off between the CNRasse (SNR asse) and the spatial resolution only with respect to pitch and not with respect to signal processing parameters. Simulation studies were performed to confirm such an observation. The results demonstrate a trade-off between CNRasse and the resolution with respect to pitch. © 2007 IOP Publishing Ltd.

Signal-to-noise ratio, contrast-to-noise ratio and their trade-offs with resolution in axial-shear strain elastography

Ultrasound elastography produces strain images of compliant tissues under quasi-static compression. In axial-shear strain elastography, the local axial-shear strain resulting from application of quasi-static axial compression to an inhomogeneous material is imaged. The overall hypothesis of this work is that the pattern of axial-shear strain distribution around the inclusion/background interface is completely determined by the bonding at the interface after normalization for inclusion size and applied strain levels, and that it is feasible to extract certain features from the axial-shear strain elastograms to quantify this pattern. The mechanical model used in this study consisted of a single stiff circular inclusion embedded in a homogeneous softer background. First, we performed a parametric study using finite-element analysis (FEA) (no ultrasound involved) to identify possible features that quantify the pattern of axial-shear strain distribution around an inclusion/background interface. Next, the ability to extract these features from axial-shear strain elastograms, estimated from simulated pre- and post-compression noisy RF data, was investigated. Further, the feasibility of extracting these features from in vivo breast data of benign and malignant tumors was also investigated. It is shown using the FEA study that the pattern of axial-shear strain distribution is determined by the degree of bonding at the inclusion/background interface. The results suggest the feasibility of using normalized features that capture the region of positive and negative axial-shear strain area to quantify the pattern of the axial-shear strain distribution. The simulation results showed that it was feasible to extract the features, as identified in the FEA study, from axial-shear strain elastograms. However, an effort must be made to obtain axial-shear strain elastograms with the highest signal-to-noise ratio (SNR asse) possible, without compromising the resolution. The in vivo results demonstrated the feasibility of producing and extracting features from the axial-shear strain elastograms from breast data. Furthermore, the in vivo axial-shear strain elastograms suggest an additional feature not identified in the simulations that may potentially be used for distinguishing benign from malignant tumors - the proximity of the axial-shear strain regions to the inclusion/background interface identified in the sonogram. © 2007 IOP Publishing Ltd.

Visualization of bonding at an inclusion boundary using axial-shear strain elastography: A feasibility study

Resolution of axial shear strain elastography

In this paper, we develop a theoretical expression for the signal-to-noise ratio (SNR) of shear strain elastograms. The previously-developed ideas for the axial strain filter (ASF) and lateral strain filter (LSF) are extended to define the concept of the shear strain filter (SSF). Some of our theoretical results are verified using simulations and phantom experiments. The results indicate that the signal-to-noise ratio of shear-strain elastograms (SNRssc) improves with increasing shear strain and with improvements in system parameters such as the sonographic signal-to-noise ratio (SNR) beamwidth, center frequency and fractional bandwidth. The results also indicate that the amount of axial strain present along with the shear strain is an important parameter that determines the upper bound on SNRssc. The SNRssc will be higher in the absence of additional deformation due to axial strain. Copyright 2005/2006 by Dynamedia, Inc. All rights of reproduction in any form reserved.

Ultrasonic Imaging

Noise performance and signal-to-noise ratio of shear strain elastograms

The automated detection of electrographic seizures in the neonatal EEG is a difficult, unsolved problem because of the variety of seizure patterns and the large number of seizure-like artifacts and non-seizure rhythmic EEG events. In this paper we present an architecture and methodology for such a detection system designed around a combination of signal processing, pattern recognition, heuristic rules, and neural networks. We believe that this hybrid approach offers the best chance for reliable automated detection of neonatal seizures.