Two-Dimmensional Blind Bayesian Deconvolution of Medical Ultrasound Images

Two-Dimmensional Blind Bayesian Deconvolution of Medical Ultrasound Images

Peer-reviewed article not indexed in WoS or Scopus

A new approach to two-dimensional blind deconvolution of ultrasonic images in a Bayesian framework is presented. The radiofrequency image data are modeled as a convolution of the point spread function and the tissue function, with additive white noise. The deconvolution algorithm is derived from statistical assumptions about the tissue function, the point spread function and the noise. It is solved as an iterative optimization problem. In each iteration, additional constraints are applied as a projection operator, to further stabilize the process. The proposedmethod is an extension of the homomorphic deconvolutionwhich is used here only to compute the initial estimate of the point spread function. Homomorphic deconvolution is based on the assumption that the point spread function and the tissue function lie in different bands of the cepstrum domain, which is not true. This limiting constraint is relaxed in the subsequent iterative deconvolution. The deconvolution is applied globally, to the complete radiofrequency image data. Thus, only the global part of the point spread function is considered. This approach, together with the need for only a few iterations, makes the deconvolution potentially useful for real-time applications. Tests on phantom and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images and than the results of the homomorphic deconvolution alone.

A new approach to two-dimensional blind deconvolution of ultrasonic images in a Bayesian framework is presented. The radiofrequency image data are modeled as a convolution of the point spread function and the tissue function, with additive white noise. The deconvolution algorithm is derived from statistical assumptions about the tissue function, the point spread function and the noise. It is solved as an iterative optimization problem. In each iteration, additional constraints are applied as a projection operator, to further stabilize the process. The proposedmethod is an extension of the homomorphic deconvolutionwhich is used here only to compute the initial estimate of the point spread function. Homomorphic deconvolution is based on the assumption that the point spread function and the tissue function lie in different bands of the cepstrum domain, which is not true. This limiting constraint is relaxed in the subsequent iterative deconvolution. The deconvolution is applied globally, to the complete radiofrequency image data. Thus, only the global part of the point spread function is considered. This approach, together with the need for only a few iterations, makes the deconvolution potentially useful for real-time applications. Tests on phantom and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images and than the results of the homomorphic deconvolution alone.

ultrasonic imaging, blind deconvolution

ultrasonic imaging, blind deconvolution

JIŘÍK, R.; TAXT, T.

2010

01.01.2008

IEEE

0885-3010

IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL

55

10

United States of America

2140

2153

14