A new method for generating poroelastograms in noisy environments

Ultrasound estimation of strain time constant and vascular permeability in tumors using a CEEMDAN and linear regression-based method

Ultrasound poroelastography focuses on the estimation of the spatio-temporal mechanical behavior of tissues using data often corrupted with non-stationary noise. The cumulative strain calculated from prolonged temporal acquisition of RF data can face the problem of aggregate noise. This noise can significantly affect the accuracy of curve fitting techniques necessary to estimate the clinically significant strain Time Constant (TC) and related parameters. We present a new technique, which decomposes the non-linear temporal behavior of the differential strain to extract the monotonic decaying trend by using the time-domain and data-driven Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. A linear regression scheme is then used to obtain the slope of the transformed non-linear trend, which carries information about the strain TC. Assessment of Vascular Permeability (VP), a transport parameter indicative of tumor growth, requires accurate strain TC estimations. Finite Element (FE), ultrasound simulations and in vivo experiments are used to investigate the performance of the proposed technique. Based on the simulation analysis, the average Percentage Relative Error (PRE) values of our method are 4.15% (for TC estimation) and 5.00% (for VP estimation) at 20 dB SNR level for different Percentage of Good Frames (PGF) (i.e., 20%, 50%, 75%, and 100%). These PRE values are substantially lower than those obtained using other conventional elastographic techniques. Our proposed method could become a new data-adaptive tool for analyzing the non-linear time-dependent response of complex tissues such as cancers.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Non-invasive imaging of Young's modulus and Poisson's ratio in cancers in vivo

Alterations of Young's modulus (YM) and Poisson's ratio (PR) in biological tissues are often early indicators of the onset of pathological conditions. Knowledge of these parameters has been proven to be of great clinical significance for the diagnosis, prognosis and treatment of cancers. Currently, however, there are no non-invasive modalities that can be used to image and quantify these parameters in vivo without assuming incompressibility of the tissue, an assumption that is rarely justified in human tissues. In this paper, we developed a new method to simultaneously reconstruct YM and PR of a tumor and of its surrounding tissues based on the assumptions of axisymmetry and ellipsoidal-shape inclusion. This new, non-invasive method allows the generation of high spatial resolution YM and PR maps from axial and lateral strain data obtained via ultrasound elastography. The method was validated using finite element (FE) simulations and controlled experiments performed on phantoms with known mechanical properties. The clinical feasibility of the developed method was demonstrated in an orthotopic mouse model of breast cancer. Our results demonstrate that the proposed technique can estimate the YM and PR of spherical inclusions with accuracy higher than 99% and with accuracy higher than 90% in inclusions of different geometries and under various clinically relevant boundary conditions.

A Spline Interpolation-based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography

Ultrasound poroelastography is a cost-effective and noninvasive imaging technique, which can be used to reconstruct mechanical parameters of tissues such as Young's modulus, Poisson's ratio, interstitial permeability, and vascular permeability. To estimate interstitial permeability and vascular permeability using poroelastography, accurate estimation of the strain time constant (TC) is required. This can be a challenging task due to the nonlinearity of the exponential strain curve and noise affecting the experimental data. Due to motion artifacts caused by the sonographer, animal/patient, and/or the environment, noise affecting some strain frames can be significantly higher than the strain signal. If these frames are used for the computation of the strain TC, the resulting TC estimate can be highly inaccurate, which, in turn, can cause high errors in the reconstructed mechanical parameters. In this paper, we introduce a cubic spline-based interpolation method, which allows to use only good quality strain frames (i.e., frames with sufficiently high signal-to-noise ratio [SNR]) to estimate the strain TC. Using finite element simulations, we demonstrate that the proposed interpolation method can improve the estimation accuracy of the strain TC by 46% with respect to the case where no interpolation and filtering are used and by 37% with respect to the case where the strain frames are Kalman filtered before TC estimation (at an SNR of 30 dB). We also prove the technical feasibility of the proposed technique using in vivo experimental data. While detecting the bad frames in both simulations and experiments, we assumed the lower limit SNR to be below 10 dB. Based on our results, the proposed technique may be of great help in applications relying on the accurate assessment of the temporal behavior of strain data.

Non-Invasive Imaging of Normalized Solid Stress in Cancers in Vivo

The solid stress (SSg) that develops inside a cancer is an important marker of cancer’s growth, invasion and metastasis. Currently, there are no non-invasive methods to image SSg inside tumors. In this paper, we develop a new, non-invasive and cost-effective imaging method to assess SSg inside tumors that uses ultrasound poroelastography. Center to the proposed method is a novel analytical model, which demonstrates that SSg and the compression-induced stress (SSc) that generates inside the cancer in a poroelastography experiment have the same spatial distribution. To show the clinical feasibility of the proposed technique, we imaged and analyzed the normalized SSg inside treated and untreated human breast cancers in a small animal model. Given the clinical significance of assessing SSg in cancers and the advantages of the proposed ultrasonic methods, our technique could have a great impact on cancer diagnosis, prognosis and treatment methods.

Optimization of a Pixel-to-Pixel Curve-Fitting Method for Poroelastography Imaging

Ultrasound poroelastography is an imaging modality used to characterize the temporal behavior of soft tissue that can be modeled as a solid permeated by interconnected pores filled with liquid (poroelastic medium). It could be useful in the stage classification of lymphedema. Generally, time-constant models are applied to strain images, and precision of the fitting process, computational cost and versatility in response to changes in tissues properties are crucial aspects of clinical applications. In the work described here, we performed creep experiments on poroelastic phantoms and used rheologic models to visualize the changes in viscoelastic response associated with fluid mobility. We used the Levenberg-Marquardt algorithm as a fitting tool and performed parametric studies to improve its performance. On the basis of these studies, we proposed an optimization schema for the pixel-to-pixel curve-fitting process. We determined that the bimodal Kelvin-Voigt model describes efficiently the temporal evolution of the strain images in heterogeneous phantoms.

Application of real-time sonoelastography in musculoskeletal diseases related to physical medicine and rehabilitation

Real-time sonoelastography is a recently developed ultrasound-based technique that evaluates tissue elasticity in real time, and it is based on the principle that the compression of tissue produces a strain (displacement) that is lower in hard tissue and higher in soft tissue. Real-time sonoelastography provides information on tissue elasticity, in addition to the shape or vascularity, which is obtained via B-mode ultrasound. Similar to B-mode ultrasound, freehand manipulation with the transducer and real-time visualization are now available for real-time sonoelastography in actual clinical practice. Tissue elasticity not only varies among different tissues but also seems to reflect disease-induced alternations in tissue properties. Real-time sonoelastography was recently applied to the normal and pathologic tissues in muscle and tendon disorders, and it showed promising results and new potentialities. Therefore, it is expected to be a useful modality for providing novel diagnostic information in musculoskeletal diseases because tissue elasticity is closely related to its pathology. It can also be used as a research tool to provide insight into the biomechanics and pathophysiology of tissue abnormality.

Real-time quasi-static ultrasound elastography

Ultrasound elastography is a technique used for clinical imaging of tissue stiffness with a conventional ultrasound machine. It was first proposed two decades ago, but active research continues in this area to the present day. Numerous clinical applications have been investigated, mostly related to cancer imaging, and though these have yet to prove conclusive, the technique has seen increasing commercial and clinical interest. This paper presents a review of the most widely adopted, non-quantitative, techniques focusing on technical innovations rather than clinical applications. The review is not intended to be exhaustive, concentrating instead on placing the various techniques in context according to the authors' perspective of the field.

Performance analysis of a new real-time elastographic time constant estimator

New elastographic techniques such as poroelastography and viscoelasticity imaging aim at imaging the temporal mechanical behavior of tissues. These techniques usually involve the use of curve fitting methods being applied to noisy data to estimate new elastographic parameters. As of today, however, current elastographic implementations of poroelastography and viscoelasticity imaging methods are in general too slow and not optimized for clinical applications. Furthermore, image quality performance of these new elastographic techniques is still largely unknown due to a paucity of data and the lack of systematic studies that analyze their performance limitations. In this paper, we propose a new elastographic time constant (TC) estimator, which is based on the use of the least square error (LSE) curve-fitting method and the Levenberg-Marquardt (LM) optimization rule as applied to noisy elastographic data obtained from a material in a creep-type experiment. The algorithm is executed on a massively parallel general purpose graphics processing unit (GPGPU) to achieve real-time performance. The estimator's performance is analyzed using simulations. Experimental results obtained from poroelastic phantoms are presented as a proof of principle of the new estimator's technical applicability on real experimental data. The results of this study demonstrate that the newly proposed elastographic estimator can produce highly accurate and sensitive elastographic TC estimates in real-time and at high signal-to-noise ratios.

The general properties including accuracy and resolution of linear filtering methods for strain estimation

The vast majority of strain imaging systems applies linear filtering to estimate strain from displacement data. Methods such as piecewise-linear least squares regression and staggered strain estimation have come to be widely known and applied, but the properties of these estimators have rarely (or never) been compared quantitatively. Given their tractable properties, careful analysis of linear filters allows us to make numerous observations that are simple, yet valuable. We consider accuracy and resolving power, which raises the question of whether any particular filter offers the best possible accuracy at a given resolution. Our surprising results provide insight at two levels: They highlight general considerations affecting the type of filter that is appropriate for practical applications, and indicate promising avenues for further research.

The spatio-temporal strain response of oedematous and nonoedematous tissue to sustained compression in vivo

Poroelastic theory predicts that compression-induced fluid flow through a medium reveals itself via the spatio-temporal behaviour of the strain field. Such strain behaviour has already been observed in simple poroelastic phantoms using generalised elastographic techniques (Berry et al. 2006a, 2006b). The aim of this current study was to investigate the extent to which these techniques could be applied in vivo to image and interpret the compression-induced time-dependent local strain response in soft tissue. Tissue on both arms of six patients presenting with unilateral lymphoedema was subjected to a sustained compression for up to 500 s, and the induced strain was imaged as a function of time. The strain was found to exhibit time-dependent spatially varying behaviour, which we interpret to be consistent with that of a heterogeneous poroelastic material. This occurred in both arms of all patients, although it was more easily seen in the ipsilateral (affected) arm than in the contralateral (apparently unaffected) arm in five out of the six patients. Further work would appear to be worthwhile to determine if poroelasticity imaging could be used in future both to diagnose lymphoedema and to explore the patho-physiology of the condition.

Imaging and estimation of tissue elasticity by ultrasound

Ultrasound (US) elasticity imaging is an extension of the ancient art of palpation and of earlier US methods for viewing tissue stiffness such as echopalpation. Elasticity images consist of either an image of strain in response to force or an image of estimated elastic modulus. There are 3 main types of US elasticity imaging: elastography that tracks tissue movement during compression to obtain an estimate of strain, sonoelastography that uses color Doppler to generate an image of tissue movement in response to external vibrations, and tracking of shear wave propagation through tissue to obtain the elastic modulus. Other modalities may be used for elasticity imaging, the most powerful being magnetic resonance elastography. With 4 commercial US scanners already offering elastography and more to follow, US-based methods may be the most widely used for the near future. Elasticity imaging is possible for nearly every tissue. Breast mass elastography has potential for enhancing the specificity of US and mammography for cancer detection. Lesions in the thyroid, prostate gland, pancreas, and lymph nodes have been successfully imaged using elastography. Evaluation of diffuse disease including cirrhosis and transplant rejection is also possible using both imaging and nonimaging methods. Vascular imaging including myocardium, blood vessel wall, plaque, and venous thrombi has also shown great potential. Elasticity imaging may also be important in assessing the progress of ablation therapy. Recent work in assessing porous materials using elastography suggests that the technique may be useful in monitoring the severity of lymphedema.

The feasibility of using poroelastographic techniques for distinguishing between normal and lymphedematous tissues in vivo

Lymphedema is a common condition involving an abnormal accumulation of lymphatic fluid in the interstitial space that causes swelling, most often in the arm(s) and leg(s). Lymphedema is a significant lifelong concern that can be congenital or develop following cancer treatment or cancer metastasis. Common methods of evaluation of lymphedema are mostly qualitative making it difficult to reliably assess the severity of the disease, a key factor in choosing the appropriate treatment. In this paper, we investigate the feasibility of using novel elastographic techniques to differentiate between lymphedematous and normal tissues. This study represents the first step of a larger study aimed at investigating the combined use of elastographic and sonographic techniques for the detection and staging of lymphedema. In this preliminary study, poroelastographic images were generated from the leg (8) and arm (4) subcutis of five normal volunteers and seven volunteers having lymphedema, and the results were compared using statistical analyses. The preliminary results reported in this paper suggest that it may be feasible to perform poroelastography in different lymphedematous tissues in vivo and that poroelastography techniques may be of help in differentiating between normal and lymphedematous tissues.

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

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.