Investigation of Noise-Resolution Tradeoff for Digital Radiographic Imaging: A Simulation Study

In digital radiographic systems, a tradeoff exists between image resolution (or blur) and noise characteristics. An imaging system may only be superior in one image quality characteristic while being inferior to another in the other characteristic. In this work, a computer simulation model is presented that is to use mutual-information (MI) metric to examine tradeoff behavior between resolution and noise. MI is used to express the amount of information that an output image contains about an input object. The basic idea is that when the amount of the uncertainty associated with an object before and after imaging is reduced, the difference of the uncertainty is equal to the value of MI. The more the MI value provides, the better the image quality is. The simulation model calculated MI as a function of signal-to-noise ratio and that of resolution for two image contrast levels. Our simulation results demonstrated that MI associated with overall image quality is much more sensitive to noise compared to blur, although tradeoff relationship between noise and blur exists. However, we found that overall image quality is primarily determined by image blur at very low noise levels.

[1]  Willi A. Kalender,et al.  On the Correlation of Pixel Noise, Spatial Resolution and Dose in Computed Tomography : Theoretical Prediction and Verification by Simulation and Measurement , 2003 .

[2]  Harrison H. Barrett,et al.  What does DQE say about lesion detectability in digital radiography? , 2001, SPIE Medical Imaging.

[3]  M J Yaffe,et al.  Analysis of the spatial-frequency-dependent DQE of optically coupled digital mammography detectors. , 1994, Medical physics.

[4]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[5]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[6]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[7]  Ehsan Samei,et al.  A method for modifying the image quality parameters of digital radiographic images. , 2003, Medical physics.

[8]  Du-Yih Tsai,et al.  Mutual information-based evaluation of image quality with its preliminary application to assessment of medical imaging systems , 2009, J. Electronic Imaging.

[9]  Swatee Singh,et al.  Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance. , 2007, Medical physics.

[10]  M L Giger,et al.  Investigation of basic imaging properties in digital radiography. 6. MTFs of II-TV digital imaging systems. , 1985, Medical physics.

[11]  M L Giger,et al.  Investigation of basic imaging properties in digital radiography. 7. Noise Wiener spectra of II-TV digital imaging systems. , 1986, Medical physics.

[12]  Du-Yih Tsai,et al.  Information Entropy Measure for Evaluation of Image Quality , 2008, Journal of Digital Imaging.

[13]  Jiang Hsieh,et al.  Resolution and noise trade-off analysis for volumetric CT. , 2007, Medical physics.

[14]  Du-Yih Tsai,et al.  Physical characterization of digital radiological images by use of transmitted information metric , 2008, SPIE Medical Imaging.

[15]  Bostjan Likar,et al.  A protocol for evaluation of similarity measures for rigid registration , 2006, IEEE Transactions on Medical Imaging.

[16]  Patrik Sund,et al.  What is worse: decreased spatial resolution or increased noise? , 2002, SPIE Medical Imaging.

[17]  M K Markey,et al.  Application of the mutual information criterion for feature selection in computer-aided diagnosis. , 2001, Medical physics.