ABSTRACT
Because the translation hypothesis of optical flow method can not accurately describe the form of motion after tissue compression, so we proposed a new ultrasonic elastic imaging algorithm. It was assumed that the deformation of the tissue was affine transformation when the probe was pressed to the tissue, and the displacement and strain distribution were estimated simultaneously by the optical flow method combined with the prior estimation. In order to verify the effectiveness of the algorithm, the imaging quality of the algorithm and the other imaging algorithm were compared with the simulated radio frequency echo signal. The results show that the new algorithm is higher in signal to noise ratio (SNRe), contrast to noise ratio (CNRe) and running speed than the contrast algorithm under 8% compression. The results show that the new proposed algorithm can effectively estimate axial displacement and axial strain in the case of large compression.
Subject(s)
Algorithms , Elasticity Imaging Techniques , Phantoms, Imaging , Signal-To-Noise Ratio , Stress, MechanicalABSTRACT
En este artículo se propone un enfoque no paramétrico para el registro elástico de imágenes médicas multimodales, cuya idea principal radica en el uso de medidas de variabilidad local, basadas en la entropía, la varianza o una combination de ambas. La metodología empleada consiste en encontrar el campo vectorial de los desplazamientos entre los pixeles de las imágenes candidata y patrón empleando una tecnica compuesta por tres pasos: primero, se obtiene una aproximación del campo vectorial por medio de un registro paramétrico entre ambas imágenes; segundo, se mapean las imágenes registradas paramétricamente a un espacio de intensidades donde pueden ser comparadas; tercero, se obtiene el flujo óptico entre las imágenes en el espacio al que fueron mapeadas. El algoritmo propuesto se evalúo usando un conjunto de imágenes de resonancia magnética y tomografía computarizada adquiridas desde diferentes vistas, las cuales fueron deformadas sintéticamente. Los resultados obtenidos en la estimación del campo de desplazamientos con las cuatro medidas de variabilidad local propuestas muestran un error medio menor que 1.4 mm, y en el caso de la entropía menor a 1 mm. Además, se demuestra la convergencia del algoritmo con ayuda de la entropía conjunta. Asó, la metodología descrita representa una nueva alternativa para el registro elástico multimodal de imágenes médicas.
In this work, we present a novel approach for multimodal elastic registration of medical images, where the key idea is to use local variability measures based on entropy, variance or a combination of these metrics. The proposed methodology relies on finding the displacements vector field between pixels of a source image and a target one, using the following three steps: first, an initial approximation of the vector field is achieved by using a parametric registration based on particle filtering between the images to align; second, the images previously registered are mapped to a common space where their intensities can be compared; and third, we obtain the optical flow between the images in this new space. To evaluate the proposed algorithm, a set of computed tomography and magnetic resonance images obtained in different views, were modified with synthetic deformation fields. The results obtained with the four proposed local variability measures show an average error of less than 1.4 mm, and in the case of the entropy less than 1 mm. In addition, the convergence of the algorithm is highlighted by the joint entropy. Therefore, the described methodology could be considered as a new alternative for multimodal elastic registration of medical images.