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  • 1.
    Yu, Sicong
    et al.
    KTH, School of Technology and Health (STH).
    Hamid Muhammad, Hamed
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Denoising of SPECT-image sinogram-data before reconstruction2014In: WMSCI 2014 - 18th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings, 2014, Vol. 1, p. 202-206Conference paper (Refereed)
    Abstract [en]

    Nuclear medicine images have low signal-to-noise ratio (SNR) due to several physical limitations which degrade the image quality considerably. In this study, the Gaussian filter and the patch confidence Gaussian filter (PCG) were used to improve the image quality for Single Photon Emission Computed Tomography (SPECT). The new approach applies these filtering methods on the acquired 2D-projections before reconstructing the image. The new approach was evaluated on a SPECT dataset and the performance was compared with several conventional methods presented in the literature.

  • 2.
    Yu, Sicong
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering.
    Hamid Muhammed, Hamed
    KTH, School of Technology and Health (STH), Health Systems Engineering.
    Comparison of Pre- and Post-Reconstruction Denoising Approaches in Positron Emission Tomography2016In: THE 1ST 2016 INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING (IBIOMED 2016), IEEE, 2016, p. 63-68Conference paper (Refereed)
    Abstract [en]

    In Positron Emission Tomography (PET), image quality is highly degraded by noise. Therefore, two main PETimage denoising approaches can be used: pre- and postreconstruction denoising. In the pre-reconstruction approach the PET sinogram is denoised before forwarding it to the image reconstruction algorithm. On the other hand, the reconstructed PET-image is denoised in the post-reconstruction approach. In this study, comparison of image quality of the resulting images of the pre- and post-reconstruction approaches is performed. In both types of approaches, the Gaussian filter, the Non-Local Means filter (NLM), the Block-Matching and 3D filter (BM3D), the K-Nearest Neighbors Filter (KNN) and the Patch Confidence K-Nearest Neighbors Filter (PCkNN) are utilized. These approaches are evaluated on a simulated PET-phantom dataset, a real-life physical thorax-phantom PET dataset as well as a reallife MicroPET-scan dataset of a mouse. The performance is measured using the Signal-to-Noise Ratio (SNR) in addition to the Contrast-to-Noise Ratio (CNR) in the resulting images.

  • 3.
    Yu, Sicong
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Hamid Muhammed, Hamed
    KTH, School of Technology and Health (STH), Health Systems Engineering.
    Noise Type Evaluation in Positron Emission Tomography Images2016In: THE 1ST 2016 INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING (IBIOMED 2016), IEEE, 2016, p. 101-106Conference paper (Refereed)
    Abstract [en]

    In Positron Emission Tomography (PET), the coincident emission of gamma photon pairs constitutes the useful signals that should be detected and processed to reconstruct the desired PET images of the studied objects. However, along with the useful signal, noise is also generated and added to the detected signals that are sorted with respect to their line-ofresponse and arranged as a sinogram for each two-dimensional slice. In this paper, the type and properties of noise in PET sinogram data will be evaluated. Furthermore, the effect of the used linear and non-linear image denoising and reconstruction procedures on the type of noise will be analyzed. For this purpose, the Gaussian filter, the Median filter, the Patch Confidence k-Nearest Neighbor filter (PCkNN) and the Block Matching 3D filter (BM3D) were used to denoise PET image data, as well as the maximum likelihood expectation maximization algorithm (MLEM) and the Filtered Back Projection algorithm (FBP) to reconstruct the PET images.

  • 4.
    Yu, Sicong
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    Hamid Muhammed, Hamed
    KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
    PET image improvement using the Patch Confidence K-Nearest Neighbors Filter2014Conference paper (Refereed)
    Abstract [en]

    In Positron Emission Tomography (PET), the resulted images are highly deteriorated by noise. In this study, we propose a new denoising framework using the Patch Confidence K-Nearest Neighbors Filter (PCKNN) to reduce noise in the sinogram before forwarding it to the reconstruction procedure. This method has been evaluated on a simulated PET image of a phantom, and the performance has been compared with several conventional methods in the literature. The results have shown that the PET image quality can be substantially improved in term of increased signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR

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