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An effective noise reduction method for multi‐energy CT images that exploit spatio‐spectral features
Author(s) -
Li Zhoubo,
Leng Shuai,
Yu Lifeng,
Manduca Armando,
McCollough Cynthia H.
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12174
Subject(s) - imaging phantom , noise reduction , noise (video) , image resolution , image noise , filter (signal processing) , pixel , image quality , artificial intelligence , energy (signal processing) , optical transfer function , computer science , sensitivity (control systems) , computer vision , optics , mathematics , physics , image (mathematics) , statistics , electronic engineering , engineering
Purpose To develop and evaluate an image‐domain noise reduction method for multi‐energy CT ( MECT ) data. Methods Multi‐Energy Non‐Local Means ( MENLM ) is a technique that uses the redundant information in MECT images to achieve noise reduction. In this method, spatio‐spectral features are used to determine the similarity between pixels, making the similarity evaluation more robust to image noise. The performance of this MENLM filter was tested on images acquired on a whole‐body research photon counting CT system. The impact of filtering on image quality was quantitatively evaluated in phantom studies in terms of image noise level (standard deviation of pixel values), noise power spectrum ( NPS ), in‐plane and cross‐plane spatial resolution, CT number accuracy, material decomposition performance, and subjective low‐contrast spatial resolution using the American College of Radiology ( ACR ) CT accreditation phantom. Clinical feasibility was assessed by performing MENLM on contrast‐enhanced swine images and unenhanced cadaver head images using clinically relevant doses and dose rates. Results The phantom studies demonstrated that the MENLM filter reduced noise substantially and still preserved the shape and peak frequency of the NPS . With 80% noise reduction, MENLM filtering caused no degradation of high‐contrast spatial resolution, as illustrated by the modulation transfer function ( MTF ) and slice sensitivity profile ( SSP ). CT number accuracy was also maintained for all energy channels, demonstrating that energy resolution was not affected by filtering. Material decomposition performance was improved with MENLM filtering. The subjective evaluation using the ACR phantom demonstrated an improvement in low‐contrast performance. MENLM achieved effective noise reduction in both contrast‐enhanced swine images and unenhanced cadaver head images, resulting in improved detection of subtle vascular structures and the differentiation of white/gray matter. Conclusion In MECT , MENLM achieved around 80% noise reduction and greatly improved material decomposition performance and the detection of subtle anatomical/low‐contrast features while maintaining spatial and energy resolution. MENLM filtering may improve diagnostic or functional analysis accuracy and facilitate radiation dose and contrast media reduction for MECT .