Restoration of ultrasonic images using non-linear system identification and deconvolution

This paper studies a new ultrasound image restoration method based on a non-linear forward model. A Hammerstein polynomial-based non-linear image formation model is considered to identify the system impulse response in baseband and around the second harmonic. The identification process is followed by a joint deconvolution technique minimizing an appropriate cost function. This cost function is constructed from two data fidelity terms exploiting the linear and non-linear model components, penalized by an additive-norm regularization enforcing sparsity of the solution. An alternating optimization approach is considered to minimize the penalized cost function, allowing the tissue reflectivity function to be estimated. Results on synthetic ultrasound images are finally presented to evaluate the algorithm performance

Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used

Iterative blind deconvolution and its application in characterization of eddy current NDE signals

Eddy current techniques are widely used to detect and characterize the defects in steam generator tubes in nuclear power plants. Although defect characterization is crucial for the successful inspection of defects, it is often difficult due to due to the finite size of the probes used for inspection. A feasible solution is to model the defect data as the convolution of the defect surface profile and the probe response. Therefore deconvolution algorithms can be used to remove the effect of probe on the signal. This thesis presents a method using iterative blind deconvolution algorithm based on the Richardson-Lucy algorithm to address the defect characterization problem. Another iterative blind deconvolution method based on Wiener filtering is used to compare the performance. A preprocessing algorithm is introduced to remove the noise and thus enhance the performance. Two new convergence criterions are proposed to solve the convergence problem. Different types of initial estimate of the PSF are used and their impacts on the performance are compared. The results of applying this method to the synthetic data, the calibration data and the field data are presented

医用超音波における散乱体分布の高解像かつ高感度な画像化に関する研究

Ultrasound imaging as an effective method is widely used in medical diagnosis andNDT (non-destructive testing). In particular, ultrasound imaging plays an important role in medical diagnosis due to its safety, noninvasive, inexpensiveness and real-time compared with other medical imaging techniques. However, in general the ultrasound imaging has more speckles and is low definition than the MRI (magnetic resonance imaging) and X-ray CT (computerized tomography). Therefore, it is important to improve the ultrasound imaging quality. In this study, there are three newproposals. The first is the development of a high sensitivity transducer that utilizes piezoelectric charge directly for FET (field effect transistor) channel control. The second is a proposal of a method for estimating the distribution of small scatterers in living tissue using the empirical Bayes method. The third is a super-resolution imagingmethod of scatterers with strong reflection such as organ boundaries and blood vessel walls. The specific description of each chapter is as follows: Chapter 1: The fundamental characteristics and the main applications of ultrasound are discussed, then the advantages and drawbacks of medical ultrasound are high-lighted. Based on the drawbacks, motivations and objectives of this study are stated. Chapter 2: To overcome disadvantages of medical ultrasound, we advanced our studyin two directions: designing new transducer improves the acquisition modality itself, onthe other hand new signal processing improve the acquired echo data. Therefore, the conventional techniques related to the two directions are reviewed. Chapter 3: For high performance piezoelectric, a structure that enables direct coupling of a PZT (lead zirconate titanate) element to the gate of a MOSFET (metal-oxide semiconductor field-effect transistor) to provide a device called the PZT-FET that acts as an ultrasound receiver was proposed. The experimental analysis of the PZT-FET, in terms of its reception sensitivity, dynamic range and -6 dB reception bandwidth have been investigated. The proposed PZT-FET receiver offers high sensitivity, wide dynamic range performance when compared to the typical ultrasound transducer. Chapter 4: In medical ultrasound imaging, speckle patterns caused by reflection interference from small scatterers in living tissue are often suppressed by various methodologies. However, accurate imaging of small scatterers is important in diagnosis; therefore, we investigated influence of speckle pattern on ultrasound imaging by the empirical Bayesian learning. Since small scatterers are spatially correlated and thereby constitute a microstructure, we assume that scatterers are distributed according to the AR (auto regressive) model with unknown parameters. Under this assumption, the AR parameters are estimated by maximizing the marginal likelihood function, and the scatterers distribution is estimated as a MAP (maximum a posteriori) estimator. The performance of our method is evaluated by simulations and experiments. Through the results, we confirmed that the band limited echo has sufficient information of the AR parameters and the power spectrum of the echoes from the scatterers is properly extrapolated. Chapter 5: The medical ultrasound imaging of strong reflectance scatterers based on the MUSIC algorithm is the main subject of Chapter 5. Previously, we have proposed a super-resolution ultrasound imaging based on multiple TRs (transmissions/receptions) with different carrier frequencies called SCM (super resolution FM-chirp correlation method). In order to reduce the number of required TRs for the SCM, the method has been extended to the SA (synthetic aperture) version called SA-SCM. However, since super-resolution processing is performed for each line data obtained by the RBF (reception beam forming) in the SA-SCM, image discontinuities tend to occur in the lateral direction. Therefore, a new method called SCM-weighted SA is proposed, in this version the SCM is performed on each transducer element, and then the SCM result is used as the weight for RBF. The SCM-weighted SA can generate multiple B-mode images each of which corresponds to each carrier frequency, and the appropriate low frequency images among them have no grating lobes. For a further improvement, instead of simple averaging, the SCM applied to the result of the SCM-weighted SA for all frequencies again, which is called SCM-weighted SA-SCM. We evaluated the effectiveness of all the methods by simulations and experiments. From the results, it can be confirmed that the extension of the SCM framework can help ultrasound imaging reduce grating lobes, perform super-resolution and better SNR(signal-to-noise ratio). Chapter 6: A discussion of the overall content of the thesis as well as suggestions for further development together with the remaining problems are summarized.首都大学東京, 2019-03-25, 博士（工学）首都大学東

Development of a Post-Processing Algorithm for Accurate Human Skull Profile Extraction via Ultrasonic Phased Arrays

Ultrasound Imaging has been favored by clinicians for its safety, affordability, accessibility, and speed compared to other imaging modalities. However, the trade-offs to these benefits are a relatively lower image quality and interpretability, which can be addressed by, for example, post-processing methods. One particularly difficult imaging case is associated with the presence of a barrier, such as a human skull, with significantly different acoustical properties than the brain tissue as the target medium. Some methods were proposed in the literature to account for this structure if the skull\u27s geometry is known. Measuring the skull\u27s geometry is therefore an important task that requires attention. In this work, a new edge detection method for accurate human skull profile extraction via post-processing of ultrasonic A-Scans is introduced. This method, referred to as the Selective Echo Extraction algorithm, SEE, processes each A-Scan separately and determines the outermost and innermost boundaries of the skull by means of adaptive filtering. The method can also be used to determine the average attenuation coefficient of the skull. When applied to simulated B-Mode images of the skull profile, promising results were obtained. The profiles obtained from the proposed process in simulations were found to be within 0.15 λ ± 0.11 λ or 0.09 ± 0.07 mm from the actual profiles. Experiments were also performed to test SEE on skull mimicking phantoms with major acoustical properties similar to those of the actual human skull. With experimental data, the profiles obtained with the proposed process were within 0.32 λ ± 0.25 λ or 0.19 ± 0.15 mm from the actual profile

Fundamental and Harmonic Ultrasound Image Joint Restoration

L'imagerie ultrasonore conserve sa place parmi les principales modalités d'imagerie en raison de ses capacités à révéler l'anatomie et à inspecter le mouvement des organes et le flux sanguin en temps réel, d'un manière non invasive et non ionisante, avec un faible coût, une facilité d'utilisation et une grande vitesse de reconstruction des images. Néanmoins, l'imagerie ultrasonore présente des limites intrinsèques en termes de résolution spatiale. L'amélioration de la résolution spatiale des images ultrasonores est un défi actuel et de nombreux travaux ont longtemps porté sur l'optimisation du dispositif d'acquisition. L'imagerie ultrasonore à haute résolution atteint cet objectif grâce à l'utilisation de sondes spécialisées, mais se confronte aujourd'hui à des limites physiques et technologiques. L'imagerie harmonique est la solution intuitive des spécialistes pour augmenter la résolution lors de l'acquisition. Cependant, elle souffre d'une atténuation en profondeur. Une solution alternative pour améliorer la résolution est de développer des techniques de post-traitement comme la restauration d'images ultrasonores. L'objectif de cette thèse est d'étudier la non-linéarité des échos ultrasonores dans le processus de restauration et de présenter l'intérêt d'incorporer des images US harmoniques dans ce processus. Par conséquent, nous présentons une nouvelle méthode de restauration d'images US qui utilise les composantes fondamentales et harmoniques de l'image observée. La plupart des méthodes existantes sont basées sur un modèle linéaire de formation d'image. Sous l'approximation de Born du premier ordre, l'image RF est supposée être une convolution 2D entre la fonction de réflectivité et la réponse impulsionelle du système. Par conséquent, un problème inverse résultant est formé et résolu en utilisant un algorithme de type ADMM. Plus précisément, nous proposons de récupérer la fonction de reflectivité inconnue en minimisant une fonction composée de deux termes de fidélité des données correspondant aux composantes linéaires (fondamentale) et non linéaires (première harmonique) de l'image observée, et d'un terme de régularisation basé sur la parcimonie afin de stabiliser la solution. Pour tenir compte de l'atténuation en profondeur des images harmoniques, un terme d'atténuation dans le modèle direct de l'image harmonique est proposé sur la base d'une analyse spectrale effectuée sur les signaux RF observés. La méthode proposée a d'abord été appliquée en deux étapes, en estimant d'abord la réponse impulsionelle, suivi par la fonction de réflectivité. Dans un deuxième temps, une solution pour estimer simultanément le réponse impulsionelle et la fonction de réflectivité est proposée, et une autre solution pour prendre en compte la variabilité spatiale du la réponse impulsionelle est présentée. L'intérêt de la méthode proposée est démontré par des résultats synthétiques et in vivo et comparé aux méthodes de restauration conventionnelles