Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

Real-time quantitative sonoelastography in an ultrasound research system

Quantitative Sono-Elastographie ist eine neue Technologie für die Ultraschall Bildgebung, die Radiologen maligne Tumoren ohne Risiko der strahlungsinduzierten Krebs (d.h. Mammographie) zu erfassen können. Aufgrund gefunden Rechenkomplexität in der aktuellen Algorithmen, Implementierung von Echtzeit-Anwendungen, die Prüfungsverfahren profitieren wurde jedoch noch nicht berichtet. Zusätzlich, aktuelle Schätzer für die Darstellung eine Elastizität Bilder vorhanden Artefakte der hohen Schätzung Varianz, die die Techniker in die Gegenwart steifer Massen irreführen könnten und zwar, falsch-positive Diagnose zu erzeugen. In dieser Arbeit wird eine GPU-basierte Elastographie-System entwickelt und an einem Forschungsultraschallgeräten implementiert. Quantitative Elastizität in Echtzeit bei 2 FPS mit einer Verbesserung Rechenzeitfaktor aus 26 wird gezeigt. Validierung der Systemgenauigkeit Anzeige wurde, auf Gelatinebasis Gewebe Phantome durchgeführt., waren niedrige Vorspannung der Elastizitätswerte berichtet wurde (4,7 %) bei geringe Anregungsfrequenzen nachahmt. Ausserdem wird eine neue Elastizität Schätzer auf quantitative Sono-Elastographie basiert eingeführt. Ein lineares Problem wurde entlang der seitlichen Abmessung modelliert und eine Regularisierung Methode wurde implementieren. Elastizität Bilder mit niedriger Vorspannung wurde darstellen (1,48 %) sowie seine Leistung in einer Brust kalibrierte Phantom mit verbesserter CNR (47,3 dB) im Vergleich mit anderen Schätzer ausgewertet sowie die Verringerung Seiten Artefakte bereits erwähnt in der Literatur (PD: 22,7 dB, 1DH 28,7 dB) gefunden. Diese zwei Beitrag profitieren, die Umsetzung und Entwicklung weiterer Elastographie Techniken, die eine verbesserte Qualität der Elastizität Bilder liefern könnten und somit eine verbesserte Genauigkeit der Diagnose.Quantitative sonoelastography is an alternative technology for ultrasound imaging that helps radiologist to diagnose malignant tumors with no risk of radiation-induced cancer (i.e. mammography). However, due to the high computational complexity found in the current algorithms, implementation of real-time systems that could benefit examination procedures has not been yet reported. Additionally, elasticity maps depicted from current estimators feature artifacts of high estimation variance that could mislead the technician into the presence of stiffer masses, generating false positive diagnosis. In this thesis, a GPU-based elastography system was designed and implemented on a research ultrasound equipment, displaying quantitative elasticity in real-time at 2 FPS with an improvement computational time factor of 26. Validation of the system accuracy was conducted on gelatin-based tissue mimicking phantoms, where low bias of elasticity values were reported (4.7%) at low excitation frequencies. Additionally, a new elasticity estimator based on quantitative sonoelastography was developed. A linear problem was modeled from the acquired sonolastography data along the lateral dimension and a regularization method was implemented. The resulting elasticity images presented low bias (1.48%), enhanced CNR and reduced lateral artifacts when evaluating the algorithm’s performance in a breast calibrated phantom and comparing it with other estimators found in the literature. These two contribution benefit the implementation and development of further elastography techniques that could provide enhanced quality of elasticity images and thus, improved accuracy of diagnosis.Tesi

Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis

Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods. A growing number of TDE techniques require an approximate but robust and fast method to initialize solving for TDE. Herein, we present a fast method for calculating an approximate TDE between two radio frequency (RF) frames of ultrasound. Although this approximate TDE can be useful for several algorithms, we focus on GLobal Ultrasound Elastography (GLUE), which currently relies on Dynamic Programming (DP) to provide this approximate TDE. We exploit Principal Component Analysis (PCA) to find the general modes of deformation in quasi-static elastography, and therefore call our method PCA-GLUE. PCA-GLUE is a data-driven approach that learns a set of TDE principal components from a training database in real experiments. In the test phase, TDE is approximated as a weighted sum of these principal components. Our algorithm robustly estimates the weights from sparse feature matches, then passes the resulting displacement field to GLUE as initial estimates to perform a more accurate displacement estimation. PCA-GLUE is more than ten times faster than DP in estimation of the initial displacement field and yields similar results.Comment: Accepted to be Published in 2019, 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, German

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL (L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai.Comment: Link to the Supplemental Video: https://drive.google.com/file/d/1uOmt-T4i9MwR98jUoMsu-eOhQ2mgjrBd/view?usp=sharin

Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography

Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In contrast to axial strain, lateral strain, which is highly required in Poisson's ratio imaging and elasticity reconstruction, has a poor quality. The main causes include low sampling frequency, limited motion, and lack of phase information in the lateral direction. Recently, physically inspired constraint in unsupervised regularized elastography (PICTURE) has been proposed. This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains. Despite the substantial improvement, the regularization was only applied during the training; hence it did not guarantee during the test that the lateral strain is within the feasible range. Furthermore, only the feasible range was employed, other constraints such as incompressibility were not investigated. In this paper, we address these two issues and propose kPICTURE in which two iterative algorithms were infused into the network architecture in the form of known operators to ensure the lateral strain is within the feasible range and impose incompressibility during the test phase.Comment: Accepted in MICCAI 202