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    Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm

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    Name:
    Rev2_OffsetDetector_BreastCT_C ...
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    Description:
    Final Accepted Manuscript
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    Author
    Tseng, Hsin Wu
    Karellas, Andrew
    Vedantham, Srinivasan
    Affiliation
    Department of Medical Imaging, The University of Arizona
    Department of Biomedical Engineering, The University of Arizona
    Issue Date
    2022-04-07
    Keywords
    breast CT
    cone-beam CT
    image quality
    iterative reconstruction
    offset detector
    
    Metadata
    Show full item record
    Publisher
    IOP Publishing
    Citation
    Tseng, H. W., Karellas, A., & Vedantham, S. (2022). Cone-beam breast CT using an offset detector: Effect of detector offset and image reconstruction algorithm. Physics in Medicine and Biology.
    Journal
    Physics in Medicine and Biology
    Rights
    © 2022 Institute of Physics and Engineering in Medicine.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Objective. A dedicated cone-beam breast computed tomography (BCT) using a high-resolution, low-noise detector operating in offset-detector geometry has been developed. This study investigates the effects of varying detector offsets and image reconstruction algorithms to determine the appropriate combination of detector offset and reconstruction algorithm. Approach. Projection datasets (300 projections in 360°) of 30 breasts containing calcified lesions that were acquired using a prototype cone-beam BCT system comprising a 40 × 30 cm flat-panel detector with 1024 × 768 detector pixels were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. The projection datasets were retrospectively truncated to emulate cone-beam datasets with sinograms of 768 × 768 and 640 × 768 detector pixels, corresponding to 5 cm and 7.5 cm lateral offsets, respectively. These datasets were reconstructed using the FDK algorithm with appropriate weights and an ASD-POCS-based Fast, total variation-Regularized, Iterative, Statistical reconstruction Technique (FRIST), resulting in a total of 4 offset-detector reconstructions (2 detector offsets × 2 reconstruction methods). Signal difference-to-noise ratio (SDNR), variance, and full-width at half-maximum (FWHM) of calcifications in two orthogonal directions were determined from all reconstructions. All quantitative measurements were performed on images in units of linear attenuation coefficient (1/cm). Results. The FWHM of calcifications did not differ (P > 0.262) among reconstruction algorithms and detector formats, implying comparable spatial resolution. For a chosen detector offset, the FRIST algorithm outperformed FDK in terms of variance and SDNR (P < 0.0001). For a given reconstruction method, the 5 cm offset provided better results. Significance. This study indicates the feasibility of using the compressed sensing-based, FRIST algorithm to reconstruct sinograms from offset-detectors. Among the reconstruction methods and detector offsets studied, FRIST reconstructions corresponding to a 30 cm × 30 cm with 5 cm lateral offset, achieved the best performance. A clinical prototype using such an offset geometry has been developed and installed for clinical trials.
    Note
    12 month embargo; published: 7 April 2022
    ISSN
    0031-9155
    EISSN
    1361-6560
    DOI
    10.1088/1361-6560/ac5fe1
    Version
    Final accepted manuscript
    Sponsors
    National Cancer Institute
    ae974a485f413a2113503eed53cd6c53
    10.1088/1361-6560/ac5fe1
    Scopus Count
    Collections
    UA Faculty Publications

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