An adaptive filter to approximate the Bayesian strategy for sonographic beamforming

IEEE Transactions on Medical Imaging
Nghia Q NguyenMichael F Insana

Abstract

A first-principles task-based approach to the design of medical ultrasonic imaging systems for breast lesion discrimination is described. This study explores a new approximation to the ideal Bayesian observer strategy that allows for object heterogeneity. The new method, called iterative Wiener filtering, is implemented using echo data simulations and a phantom study. We studied five lesion features closely associated with visual discrimination for clinical diagnosis. A series of human observer measurements for the same image data allowed us to quantitatively compare alternative beamforming strategies through measurements of visual discrimination efficiency. Employing the Smith-Wagner model observer, we were able to breakdown efficiency estimates and identify the processing stage at which performance losses occur. The methods were implemented using a commercial scanner and a cyst phantom to explore development of spatial filters for systems with shift-variant impulse response functions. Overall we found that significant improvements were realized over standard B-mode images using a delay-and-sum beamformer but at the cost of higher complexity and computational load.

References

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Citations

Jun 20, 2012·IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control·Craig K AbbeyMichael F Insana
Dec 19, 2012·IEEE Transactions on Medical Imaging·Nghia Q NguyenMichael F Insana
Dec 12, 2012·IEEE Transactions on Medical Imaging·Nghia Q NguyenMichael F Insana
Jan 6, 2016·IEEE Transactions on Medical Imaging·Nghia Q Nguyen, Richard W Prager
Sep 3, 2016·The Journal of the Acoustical Society of America·Nghia Q NguyenMichael F Insana
Apr 20, 2016·IEEE Transactions on Medical Imaging·Adam WunderlichCraig K Abbey
Jul 7, 2017·The Journal of the Acoustical Society of America·Nghia Q NguyenMichael F Insana

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