[Recovery of medical ultrasound images based on an effective deconvolution of scanning data]. / Restavratsiia meditsinskikh ul'trazvukovykh izobrazhenii na osnove éffektivnoi dekonvoliutsii dannykh skanirovaniia.

Described in the paper is a method of data deconvolution of ultrasound scanning (solution of the deconvolution equation in the linear metric) that ensures a higher precision of images being restored versus the traditional Wiener filtering. Simplex algorithm with an originally modified efficiency function was used to solve the task. The results of experiments testifying to the efficiency of algorithms in the ultrasound image processing of the mammary gland of a patient are presented.

Noise robust one-dimensional blind deconvolution of medical ultrasound images.

Recently, several blind cepstral deconvolution methods for medical ultrasound images were compared experimentally. The results indicated that the generalized cepstrum or the complex cepstrum with phase unwrapping give the blind homomorphic deconvolution algorithms with the best performance. However, the frequency domain phase unwrapping for pulse estimation, which is an essential part of both methods, is sensitive to the sensor noise when the values of the spectrum are small due to the randomness of the tissue response. The noise introduces abrupt changes in the phase. The phase degradation due to the noise causes variable spatial and gray scale resolution in image sequences following deconvolution. This paper introduces a noise robust Bayesian phase unwrapping method using a noncausal Markov random chain model. The prior regularizing term accounts for the noise and smoothes the phase. The phase unwrapping is formulated as a least mean square optimization problem. The optimization is done noniteratively by solving a difference equation using the cosine transform. The resulting improvement in radial and lateral blind deconvolution is demonstrated on six short ultrasound image sequences recorded in vitro or in vivo.