Deconvolution in medical ultrasonics: practical considerations.

Deconvolution can, in principle, enhance the spatial resolution of specular reflectors in medical ultrasonic imaging but, in practice, the resolution improvement offered is offset by the introduction of undesirable artefacts. In this study, several problems related to deconvolution were identified and practical suggestions for minimising artefacts were made. These include: fitting a three-dimensional surface to experimentally measured beam profiles in order to take into account the depth-dependence of the point spread function (PSF); adaptive detail-preserving noise filtering as a preprocessing tool in order to improve the quality of the data and reduce the speckle enhancement artefact; a histogram modification procedure in order to overcome the problems of ringing, over- and undershooting. Processing of a large number of A-scan data obtained from tissue-mimicking phantoms and the abdomens of normal volunteers demonstrated the efficiency of these techniques in reducing artefacts. The performance of deconvolution in terms of resolution improvement was satisfactory when data from resolution test objects were processed but poor with abdominal scans. This difference in performance raises the question as to how similar the PSF in tissue is to the experimentally measured PSF in water or even a tissue-mimicking material.

[1]  Mostafa Fatemi,et al.  Digital Processing for Improvement of Ultrasonic Abdominal Images , 1983, IEEE Transactions on Medical Imaging.

[2]  E. Hundt,et al.  Digital Processing of Ultrasonic Data by Deconvolution , 1980, IEEE Transactions on Sonics and Ultrasonics.

[3]  A. P. G. Hoeks,et al.  The Practical Significance of Two-Dimensional Deconvolution in Echography , 1987 .

[4]  Dickinson Rj Comment on Vaknine, R. and Lorenz, W.J. Lateral filtering of medical ultrasonic B-scans before image generation. , 1985 .

[5]  B. R. Hunt,et al.  The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer , 1973, IEEE Transactions on Computers.

[6]  W. Lorenz,et al.  Lateral Filtering of Medical Ultrasonic B-Scans before Image Generation , 1984 .

[7]  G. Cook-Martin,et al.  Texture Analysis And Speckle Reduction In Medical Echography , 1987, Other Conferences.

[8]  R. Bracewell The Fourier Transform and Its Applications , 1966 .

[9]  H. Schomberg,et al.  Lateral Inverse Filtering of Ultrasonic B-Scan Images , 1983 .

[10]  Elise de Doncker,et al.  D01 Chapter-Numerical Algorithms Group, in samenwerking met de andere D01-contributors. 1) NAG Fortran Mini Manual, Mark 8, D01 18p., , 1981 .

[11]  G. F. Vermeij,et al.  The Approximation of Image Blur Restoration Filters by Finite Impulse Responses , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  A K Boardman,et al.  Constrained optimisation and its application to scintigraphy. , 1979, Physics in medicine and biology.

[13]  T. Loupas,et al.  An adaptive weighted median filter for speckle suppression in medical ultrasonic images , 1989 .

[14]  M Fatemi,et al.  Ultrasonic B-scan imaging: theory of image formation and a technique for restoration. , 1980, Ultrasonic imaging.

[15]  T. Loupas,et al.  A HARDWARE FILTER FOR REAL-TIME SPECKLE SUPPRESSION IN ULTRASONOGRAPHIC SCANS , 1988 .

[16]  J C Bamber,et al.  Ultrasonic B-scanning: a computer simulation , 1980, Physics in medicine and biology.