A multichannel block‐matching denoising algorithm for spectral photon‐counting CT images

Purpose We present a denoising algorithm designed for a whole‐body prototype photon‐counting computed tomography (PCCT) scanner with up to 4 energy thresholds and associated energy‐binned images. Methods Spectral PCCT images can exhibit low signal to noise ratios (SNRs) due to the limited photon cou...

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Vydáno v:Medical physics (Lancaster) Ročník 44; číslo 6; s. 2447 - 2452
Hlavní autoři: Harrison, Adam P., Xu, Ziyue, Pourmorteza, Amir, Bluemke, David A., Mollura, Daniel J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.06.2017
Témata:
Algorithms
Animals
block‐matching 3D
collaborative filtering
denoising
Dogs
Humans
multi‐channel
Phantoms, Imaging
Photons
photon‐counting computed tomography
Signal-To-Noise Ratio
Tomography, X-Ray Computed
denoising
collaborative filtering
multi-channel
photon-counting computed tomography
block-matching 3D
ISSN:0094-2405, 2473-4209, 2473-4209
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Shrnutí:Purpose We present a denoising algorithm designed for a whole‐body prototype photon‐counting computed tomography (PCCT) scanner with up to 4 energy thresholds and associated energy‐binned images. Methods Spectral PCCT images can exhibit low signal to noise ratios (SNRs) due to the limited photon counts in each simultaneously‐acquired energy bin. To help address this, our denoising method exploits the correlation and exact alignment between energy bins, adapting the highly‐effective block‐matching 3D (BM3D) denoising algorithm for PCCT. The original single‐channel BM3D algorithm operates patch‐by‐patch. For each small patch in the image, a patch grouping action collects similar patches from the rest of the image, which are then collaboratively filtered together. The resulting performance hinges on accurate patch grouping. Our improved multi‐channel version, called BM3D_PCCT, incorporates two improvements. First, BM3D_PCCT uses a more accurate shared patch grouping based on the image reconstructed from photons detected in all 4 energy bins. Second, BM3D_PCCT performs a cross‐channel decorrelation, adding a further dimension to the collaborative filtering process. These two improvements produce a more effective algorithm for PCCT denoising. Results Preliminary results compare BM3D_PCCT against BM3D_Naive, which denoises each energy bin independently. Experiments use a three‐contrast PCCT image of a canine abdomen. Within five regions of interest, selected from paraspinal muscle, liver, and visceral fat, BM3D_PCCT reduces the noise standard deviation by 65.0%, compared to 40.4% for BM3D_Naive. Attenuation values of the contrast agents in calibration vials also cluster much tighter to their respective lines of best fit. Mean angular differences (in degrees) for the original, BM3D_Naive, and BM3D_PCCT images, respectively, were 15.61, 7.34, and 4.45 (iodine); 12.17, 7.17, and 4.39 (galodinium); and 12.86, 6.33, and 3.96 (bismuth). Conclusion We outline a multi‐channel denoising algorithm tailored for spectral PCCT images, demonstrating improved performance over an independent, yet state‐of‐the‐art, single‐channel approach.
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ISSN:0094-2405
2473-4209
2473-4209
DOI:10.1002/mp.12225