• Login
    View Item 
    •   etd@IISc
    • Division of Electrical, Electronics, and Computer Science (EECS)
    • Electrical Engineering (EE)
    • View Item
    •   etd@IISc
    • Division of Electrical, Electronics, and Computer Science (EECS)
    • Electrical Engineering (EE)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Performance Evaluation Of Fan-beam And Cone-beam Reconstruction Algorithms With No Backprojection Weight On Truncated Data Problems

    View/Open
    G25336.pdf (1.282Mb)
    Date
    2014-07-16
    Author
    Sumith, K
    Metadata
    Show full item record
    Abstract
    This work focuses on using the linear prediction based projection completion for the fan-beam and cone-beam reconstruction algorithm with no backprojection weight. The truncated data problems are addressed in the computed tomography research. However, the image reconstruction from truncated data perfectly has not been achieved yet and only approximately accurate solutions have been obtained. Thus research in this area continues to strive to obtain close result to the perfect. Linear prediction techniques are adopted for truncation completion in this work, because previous research on the truncated data problems also have shown that this technique works well compared to some other techniques like polynomial fitting and iterative based methods. The Linear prediction technique is a model based technique. The autoregressive (AR) and moving average (MA) are the two important models along with autoregressive moving average (ARMA) model. The AR model is used in this work because of the simplicity it provides in calculating the prediction coefficients. The order of the model is chosen based on the partial autocorrelation function of the projection data proved in the previous researches that have been carried out in this area of interest. The truncated projection completion using linear prediction and windowed linear prediction show that reasonably accurate reconstruction is achieved. The windowed linear prediction provide better estimate of the missing data, the reason for this is mentioned in the literature and is restated for the reader’s convenience in this work. The advantages associated with the fan-beam reconstruction algorithms with no backprojection weights compared to the fan-beam reconstruction algorithm with backprojection weights motivated us to use the fan-beam reconstruction algorithm with no backprojection weight for reconstructing the truncation completed projection data. The results obtained are compared with the previous work which used conventional fan-beam reconstruction algorithms with backprojection weight. The intensity plots and the noise performance results show improvements resulting from using the fan-beam reconstruction algorithm with no backprojection weight. The work is also extended to the Feldkamp, Davis, and Kress (FDK) reconstruction algorithm with no backprojection weight for the helical scanning geometry and the results obtained are compared with the FDK reconstruction algorithm with backprojection weight for the helical scanning geometry.
    URI
    https://etd.iisc.ac.in/handle/2005/2343
    Collections
    • Electrical Engineering (EE) [357]

    Related items

    Showing items related by title, author, creator and subject.

    • Image Reconstruction Based On Hilbert And Hybrid Filtered Algorithms With Inverse Distance Weight And No Backprojection Weight 

      Narasimhadhan, A V (2014-07-16)
      Filtered backprojection (FBP) reconstruction algorithms are very popular in the field of X-ray computed tomography (CT) because they give advantages in terms of the numerical accuracy and computational complexity. Ramp ...
    • Fusion of Sparse Reconstruction Algorithms in Compressed Sensing 

      Ambat, Sooraj K (2018-06-08)
      Compressed Sensing (CS) is a new paradigm in signal processing which exploits the sparse or compressible nature of the signal to significantly reduce the number of measurements, without compromising on the signal reconstruction ...
    • Automated Selection of Hyper-Parameters in Diffuse Optical Tomographic Image Reconstruction 

      Jayaprakash, * (2018-03-16)
      Diffuse optical tomography is a promising imaging modality that provides functional information of the soft biological tissues, with prime imaging applications including breast and brain tissue in-vivo. This modality uses ...

    etd@IISc is a joint service of SERC & J R D Tata Memorial (JRDTML) Library || Powered by DSpace software || DuraSpace
    Contact Us | Send Feedback | Thesis Templates
    Theme by 
    Atmire NV
     

     

    Browse

    All of etd@IIScCommunities & CollectionsTitlesAuthorsAdvisorsSubjectsBy Thesis Submission DateThis CollectionTitlesAuthorsAdvisorsSubjectsBy Thesis Submission Date

    My Account

    LoginRegister

    etd@IISc is a joint service of SERC & J R D Tata Memorial (JRDTML) Library || Powered by DSpace software || DuraSpace
    Contact Us | Send Feedback | Thesis Templates
    Theme by 
    Atmire NV