An effective noise reduction method for multi‐energy CT images that exploit spatio‐spectral features

Abstract

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. Conclusions 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.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

“…Moreover, for this particular detector design, only half of the subpixels were used for each of the energy bins in the four‐bin mode, reducing the dose efficiency, which leads to even higher noise levels. One strategy to compensate for this would be the application of spectral denoising techniques such as multienergy nonlocal means …”

“…One strategy to compensate for this would be the application of spectral denoising techniques such as multienergy nonlocal means. 40 The motivation for this study was the clear theoretical advantages of PCDs which would make PCD-CT interesting for SPR estimation, as an alternative to DECT. Currently, six different acquisition strategies for DECT imaging exist, dual source, kVp-switching between projections, kVp-switching between gantry rotations, two consecutive scans, split-filter, and dual-layer detectors.…”

“…This was done to simulate the same total dose for each CT configuration (full spectrum (one-bin), two-bin, and four-bin). We used three different noise levels r FS = [10,20,40] HU. For the high noise level, this resulted in a standard deviation for the four-bin images of 80 HU, which was comparable to what was seen for real PCD-CT images.…”

ROI-Wise Material Decomposition in Spectral Photon-Counting CT

“…F. Yang is with Beijing Jiaotong Univ, School of Computer and Information Technology, Beijing, China approach) [4]- [6], or firstly reconstruct the spatial image from each energy bin sinogram and then decompose the reconstructed spatial images corresponding to different energy bins into spatial material images (image-domain approach) [7]- [10], or directly reconstruct spatial material images from projection data (one-step approach) [11]- [13]. The advantage of both projection-domain and one-step approaches is the more precise model that they directly decompose projection data (raw data) rather than after the procedure of reconstruction.…”

“…To improve the reliability and accuracy of material decomposition, it is important to make full use of more information beside the aforementioned spectral information [10], [14], [15]. A straightforward way of realizing this is to exploit morphological information embedded in the reconstructed sCT images.…”