Class-based Nonlinear Enhancement Strategy and Its Validation: An Application for Digital Subtraction Angiography (DSA)

L. Wei, D. Kumar, J. Coleman, R. Turlapthi, and J. Suri (USA)

Keywords

DSA, image enhancement, nonlinear normalization, SNR, observer

Abstract

Digital Subtraction Angiography (DSA) is a well established modality for the visualization of blood vessels in the human body. DSA helps interventional clinicians make decisions in analyzing vascular diseases. Though, there is a clear motivation to improve the image quality for these procedures, it suffers from challenges like system X-ray noise and motion artifacts due to patient movement. This paper presents a class-based image enhancement technique so-called nonlinear normalization to enhance the blood vessel there by suppressing the background. A lookup table (LUT) is developed in real time which enhances the dye injected blood vessels retaining its translucency and suppressing the background. Our protocol is evaluated on a database of 73 subjects by two different strategies: (a) using three trained human observers and (b) quantitatively, using signal-to noise (SNR) measurement. Using our performance evaluation protocol, SNR of the DSA embedded with nonlinear enhancement method demonstrates an improvement of 24.87% over conventional DSA. We validate our algorithm using GE’s PMMA phantoms. Our system runs on Eigen’s DSA workstation using C++ in Windows environment.

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