PROJECT TITLE :

Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter

ABSTRACT:

Goal: Spectral computed tomography (SCT) images reconstructed by an analytical approach typically suffer from a poor signal-to-noise ratio and strong streak artifacts when sufficient photon counts don't seem to be accessible in SCT imaging. In reducing noise-induced artifacts in SCT pictures, during this study, we propose a mean image-induced nonlocal means that (aviNLM) filter for every energy-specific image restoration.  Methods:  The current aviNLM algorithm exploits redundant data in the whole energy domain. Specifically, the proposed aviNLM algorithm yields the restored results by performing a nonlocal weighted average operation on the noisy energy-specific images with the nonlocal weight matrix between the target and previous pictures, in that the previous image is generated from all of the pictures reconstructed in every energy bin.  Results: Qualitative and quantitative studies are conducted to guage the aviNLM filter by using the info of digital phantom, physical phantom, and clinical patient knowledge acquired from the energy-resolved and -integrated detectors, respectively. Experimental results show that the current aviNLM filter will achieve promising results for SCT image restoration in terms of noise-induced artifact suppression, cross profile, and contrast-to-noise ratio and material decomposition assessment. Conclusion and Significance: The present aviNLM algorithm has useful potential for radiation dose reduction by lowering the mAs in SCT imaging, and it may be helpful for another clinical applications, like in myocardial perfusion imaging and radiotherapy.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability ABSTRACT: Environmental, lighting, atmospheric, and temporal variables can all contribute to hyperspectral image spectral
PROJECT TITLE :Spectral Domain Sampling of Graph Signals - 2018ABSTRACT:Sampling ways for graph signals within the graph spectral domain are presented. Though the standard sampling of graph signals will be considered sampling
PROJECT TITLE :Quantized Spectral Compressed Sensing: Cramer–Rao Bounds and Recovery Algorithms - 2018ABSTRACT:Efficient estimation of wideband spectrum is of nice importance for applications like cognitive radio. Recently,
PROJECT TITLE :Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering - 2018ABSTRACT:One in every of the longstanding open issues in spectral graph clustering (SGC) is the thus-called model order
PROJECT TITLE :Spectral and Energy Efficiency Analysis for SLNR Precoding in Massive MIMO Systems With Imperfect CSI - 2018ABSTRACT:We have a tendency to derive tractable bound expressions on achievable spectral potency for a

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry