Dictionary-based image denoising for dual energy computed tomography
Mechlem K, Allner S, Mei K, Pfeiffer F, Noel PB (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: SPIE
Book Volume: 9783
Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Event location: San Diego, CA, USA
ISBN: 9781510600188
DOI: 10.1117/12.2216749
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.
Involved external institutions
Germany (DE)How to cite
APA:
Mechlem, K., Allner, S., Mei, K., Pfeiffer, F., & Noel, P.B. (2016). Dictionary-based image denoising for dual energy computed tomography. In Despina Kontos, Joseph Y. Lo, Thomas G. Flohr (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. San Diego, CA, USA: SPIE.
MLA:
Mechlem, Korbinian, et al. "Dictionary-based image denoising for dual energy computed tomography." Proceedings of the Medical Imaging 2016: Physics of Medical Imaging, San Diego, CA, USA Ed. Despina Kontos, Joseph Y. Lo, Thomas G. Flohr, SPIE, 2016.
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