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

Adam P Harrison¹, Ziyue Xu¹, Amir Pourmorteza²

¹Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.

Medical Physics

|March 24, 2017

Summary

A new denoising algorithm, BM3D_PCCT, significantly reduces noise in photon-counting computed tomography (PCCT) spectral images by leveraging inter-energy-bin correlations. This advanced method improves image quality more effectively than single-channel denoising techniques.

Keywords:

block-matching 3D collaborative filtering denoising multi-channel photon-counting computed tomography

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Area of Science:

Medical Imaging
Computational Imaging
Image Processing

Background:

Spectral photon-counting computed tomography (PCCT) offers multi-energy information but faces challenges with low signal-to-noise ratios (SNRs) in individual energy bins.
Limited photon counts per energy bin in PCCT can degrade image quality, necessitating advanced denoising solutions.

Purpose of the Study:

To develop and evaluate a novel multi-channel denoising algorithm, BM3D_PCCT, specifically for whole-body prototype PCCT scanners with multiple energy thresholds.
To enhance the SNR and diagnostic quality of spectral PCCT images by exploiting correlations between energy-binned data.

Main Methods:

Adapted the block-matching 3D (BM3D) algorithm for multi-channel PCCT data, creating BM3D_PCCT.
Implemented shared patch grouping across all energy bins and introduced cross-channel decorrelation to improve denoising performance.
Evaluated the algorithm on a canine abdomen PCCT dataset with three contrast agents.

Main Results:

BM3D_PCCT reduced noise standard deviation by 65.0% in key regions, outperforming the independent denoising approach (BM3D_Naive) at 40.4%.
The algorithm significantly improved the accuracy of contrast agent attenuation values, evidenced by tighter clustering around best-fit lines.
Mean angular differences were substantially reduced, indicating superior preservation of spectral information (e.g., 4.45° for iodine with BM3D_PCCT vs. 15.61° original).

Conclusions:

The developed BM3D_PCCT algorithm is an effective multi-channel denoising solution tailored for spectral PCCT imaging.
BM3D_PCCT demonstrates superior performance compared to state-of-the-art single-channel denoising methods for PCCT data.