Dictionary-based image denoising for dual energy computed tomography

Korbinian Mechlem; Sebastian Allner; Kai Mei; Franz Pfeiffer; Peter B. Noël

doi:10.1117/12.2216749

22 March 2016 Dictionary-based image denoising for dual energy computed tomography

Korbinian Mechlem, Sebastian Allner, Kai Mei, Franz Pfeiffer, Peter B. Noël

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97830E (2016) https://doi.org/10.1117/12.2216749
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Compared to conventional computed tomography (CT), dual energy CT allows for improved material decomposition by conducting measurements at two distinct energy spectra. Since radiation exposure is a major concern in clinical CT, there is a need for tools to reduce the noise level in images while preserving diagnostic information. One way to achieve this goal is the application of image-based denoising algorithms after an analytical reconstruction has been performed. We have developed a modified dictionary denoising algorithm for dual energy CT aimed at exploiting the high spatial correlation between between images obtained from different energy spectra. Both the low-and high energy image are partitioned into small patches which are subsequently normalized. Combined patches with improved signal-to-noise ratio are formed by a weighted addition of corresponding normalized patches from both images. Assuming that corresponding low-and high energy image patches are related by a linear transformation, the signal in both patches is added coherently while noise is neglected. Conventional dictionary denoising is then performed on the combined patches. Compared to conventional dictionary denoising and bilateral filtering, our algorithm achieved superior performance in terms of qualitative and quantitative image quality measures. We demonstrate, in simulation studies, that this approach can produce 2d-histograms of the high- and low-energy reconstruction which are characterized by significantly improved material features and separation. Moreover, in comparison to other approaches that attempt denoising without simultaneously using both energy signals, superior similarity to the ground truth can be found with our proposed algorithm.

Citation Download Citation

Korbinian Mechlem, Sebastian Allner, Kai Mei, Franz Pfeiffer, and Peter B. Noël "Dictionary-based image denoising for dual energy computed tomography", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97830E (22 March 2016); https://doi.org/10.1117/12.2216749

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available