Scaled reassigned spectrograms applied to linear transducer signals

This study evaluates the applicability of scaled reassigned spectrograms (ReSTS) on ultrasound radio frequency data obtained with a clinical linear array ultrasound transducer. The ReSTS's ability to resolve axially closely spaced objects in a phantom is compared to the classical cross-correlation method with respect to the ability to resolve closely spaced objects as individual reflectors using ultrasound pulses with different lengths. The results show that the axial resolution achieved with the ReSTS was superior to the cross-correlation method when the reflected pulses from two objects overlap. A novel B-mode imaging method, facilitating higher image resolution for distinct reflectors, is proposed

Acoustical structured illumination for super-resolution ultrasound imaging.

Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges

Constrained least squares filtering algorithm for ultrasound image deconvolution

A new medical ultrasound tissue model is considered in this paper, which incorporates random fluctuations of the tissue response and provides more realistic interpretation of the received pulse-echo ultrasound signal. Using this new model, we propose an algorithm for restoration of the degraded ultrasound image. The proposed deconvolution is a modification of the classical regularization technique which combines Wiener filter and the constrained least squares (LS) algorithm for restoration of the ultrasound image. The performance of the algorithm is evaluated based on both the simulated phantom images and real ultrasound radio frequency (RF) data. The results show that the algorithm can provide improved ultrasound imaging performance in terms of the resolution gain. The deconvolved images visually show better resolved tissue structures and reduce speckle, which are confirmed by a medical expert

Restauración de imágenes con desensibilización de estimaciones

El marco de esta tesis es la restauración digital de imágenes, esto es, el proceso por el cual se recupera una imagen original que ha sido degradada por las imperfecciones del sistema de adquisición: emborronamiento y ruido. Restaurar esta degradación es un problema mal condicionado pues la inversión directa por mínimos cuadrados amplifica el ruido en las altas frecuencias. Por ello, se utiliza la regularización matemática como medio para incluir información a priori de la imagen que consiga estabilizar la solución. Durante la primera parte de la memoria se hace un repaso de ciertos algoritmos del estado del arte, que se usarán posteriormente como métodos de comparación en los experimentos. Para resolver el problema de regularización, la restauración de imágenes tiene dos requisitos previos. En primer lugar, es necesario realizar hipótesis sobre el comportamiento de la imagen fuera de sus fronteras, debido a la propiedad no local de la convolución que modela la degradación. La ausencia de condiciones de frontera en la restauración da lugar al artefacto conocido como boundary ringing. En segundo lugar, los algoritmos de restauración dependen de un número importante de parámetros divididos en tres grupos: parámetros respecto al proceso de degradación, al ruido y a la imagen original. Todos ellos necesitan de una estimación a priori suficientemente precisa, pues pequeños errores respecto a sus valores reales producen importantes desviaciones en los resultados de restauración. El problema de frontera y la sensibilidad a estimaciones son los objetivos a resolver en esta tesis mediante dos algoritmos iterativos. El primero de los algoritmos afronta el problema de frontera partiendo de una imagen truncada en el campo de visión como observación real. Para resolver esta no linealidad, se utiliza una red neuronal que minimiza una función de coste definida principalmente por la regularización por variación total, pero sin incluir ningún tipo de información a priori sobre las fronteras ni requerir entrenamiento previo de la iv red. Como resultado, se obtiene una imagen restaurada sin efectos de ringing en el campo de visión y además las fronteras truncadas son reconstruidas hasta el tamaño original. El algoritmo se basa en la técnica de retro-propagación de energía, con lo que la red se convierte en un ciclo iterativo de dos procesos: forward y backward, que simulan una restauración y una degradación por cada iteración. Siguiendo el mismo concepto iterativo de restauración-degradación, se presenta un segundo algoritmo en el dominio de la frecuencia para reducir la dependencia respecto a las estimaciones de parámetros. Para ello, se diseña un nuevo filtro de restauración desensibilizado como resultado de aplicar un algoritmo iterativo sobre un filtro original. Estudiando las propiedades de sensibilidad de este filtro y estableciendo un criterio para el número de iteraciones, se llega a una expresión para el algoritmo de desensibilización particularizado a los filtros Wiener y Tikhonov. Los resultados de los experimentos demuestran el buen comportamiento del filtro respecto al error dependiente del ruido, con lo que la estimación que se hace más robusta es la correspondiente a los parámetros del ruido, si bien la desensibilización se extiende también al resto de estimaciones. Abstract The framework of this thesis is digital image restoration, that is to say, the process of recovering an original image which has been degraded due to the imperfections in the acquisition system: blurring and noise. Restoring this degradation is an ill-posed problem since the inverse solution using least-squares leads to excessive noise amplification. For that reason, mathematical regularization is used to include prior knowledge about the image which allows the stabilization of the solution in the face of noise. In the first part of the thesis, we provide a review of the state-of-the-art methods which will be used later in the experimental results. To deal with a regularization problem, image restoration imposes two main requirements. First, it is necessary to make assumptions about how the image behaves outside the field of view, as a result of the non-local property of the underlying convolution. The absence of boundary conditions in the restoration problem produces the so-called boundary ringing artifact. Secondly, the restoration methods depend on a wide set of parameters which can be largely grouped into three categories: parameters with respect to the degradation process, the noise and the original image. All parameters require an accurate prior estimation because small errors in their values lead to important deviations in the restoration results. The boundary problem and the sensitivity to estimations are the issues to resolve in this thesis by means of two iterative algorithms. The first algorithm copes with the boundary problem taking a truncated image in the field of view as a real observation. To resolve the nonlinearity in the observation, we use a neural network that minimizes a cost function mainly defined by the total variation regularization, but with neither prior assumption as regards the boundaries nor previous training in the net. It yields a restored image without ringing artifacts and, moreover, the truncated boundaries are reconstructed according to the original image size. The algorithm is based on the backpropagation method, which turns out an iterative cycle of two steps: forward and backward, simulating respectively restoration and degradation processes at each iteration. Following the same iterative concept of restoration-degradation, we present a second algorithm in the frequency domain to reduce the dependency on the estimation of parameters. Hence, a novel desensitized restoration filter is designed by applying an iterative algorithm over the original filter. Analyzing the sensitivity properties of this filter and setting a criterion to choose the number of iterations, we come up with an expression for the desensitized algorithm that is particularized to the Wiener and the Tikhonov filters. Experimental results demonstrate the desensitizing behavior with respect to the noise-dependent error and a consequent robustness to the noise parameters, although the desensitization also applies to the rest of the estimations

Study on blood flow imaging using high frame rate ultrasound and its application to functional imaging of arterial wall

富山大学・富理工博甲第212号・茂澄倫也・2023/3/23富山大学202