A multichannel block-matching denoising algorithm for spectral photon-counting CT images.
OBJECTIVE
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).


CONCLUSIONS
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.
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.
Adam P
AP
Harrison
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.
Ziyue
Z
Xu
Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.
Amir
A
Pourmorteza
Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.
David A
DA
Bluemke
Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.
Daniel J
DJ
Mollura
D000465:Algorithms; D000818:Animals; D004285:Dogs; D006801:Humans; D019047:Phantoms, Imaging; D017785:Photons; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed
block-matching 3D; collaborative filtering; denoising; multi-channel; photon-counting computed tomography