Literature DB >> 35316793

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

Hsin Wu Tseng1, Andrew Karellas1, Srinivasan Vedantham1,2.   

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.
© 2022 Institute of Physics and Engineering in Medicine.

Entities:  

Keywords:  breast CT; cone-beam CT; image quality; iterative reconstruction; offset detector

Mesh:

Year:  2022        PMID: 35316793      PMCID: PMC9045275          DOI: 10.1088/1361-6560/ac5fe1

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   4.174


  47 in total

1.  Cone-beam breast computed tomography with a displaced flat panel detector array.

Authors:  Giovanni Mettivier; Paolo Russo; Nico Lanconelli; Sergio Lo Meo
Journal:  Med Phys       Date:  2012-05       Impact factor: 4.071

2.  NPS characterization and evaluation of a cone beam CT breast imaging system.

Authors:  Ricardo Betancourt Benítez; Ruola Ning; David Conover; Shaohua Liu
Journal:  J Xray Sci Technol       Date:  2009       Impact factor: 1.535

3.  Improved total variation-based CT image reconstruction applied to clinical data.

Authors:  Ludwig Ritschl; Frank Bergner; Christof Fleischmann; Marc Kachelriess
Journal:  Phys Med Biol       Date:  2011-02-16       Impact factor: 3.609

4.  Experimentally determined spectral optimization for dedicated breast computed tomography.

Authors:  Nicolas D Prionas; Shih-Ying Huang; John M Boone
Journal:  Med Phys       Date:  2011-02       Impact factor: 4.071

5.  Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT.

Authors:  Junguo Bian; Jeffrey H Siewerdsen; Xiao Han; Emil Y Sidky; Jerry L Prince; Charles A Pelizzari; Xiaochuan Pan
Journal:  Phys Med Biol       Date:  2010-10-20       Impact factor: 3.609

6.  Dedicated breast CT: geometric design considerations to maximize posterior breast coverage.

Authors:  Srinivasan Vedantham; Andrew Karellas; Margaret M Emmons; Lawrence J Moss; Sarwat Hussain; Stephen P Baker
Journal:  Phys Med Biol       Date:  2013-05-17       Impact factor: 3.609

7.  Technical note: Skin thickness measurements using high-resolution flat-panel cone-beam dedicated breast CT.

Authors:  Linxi Shi; Srinivasan Vedantham; Andrew Karellas; Avice M O'Connell
Journal:  Med Phys       Date:  2013-03       Impact factor: 4.071

8.  Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer.

Authors:  Donghwan Kim; Debashish Pal; Jean-Baptiste Thibault; Jeffrey A Fessler
Journal:  IEEE Trans Med Imaging       Date:  2013-06-07       Impact factor: 10.048

9.  Sparse-view, short-scan, dedicated cone-beam breast computed tomography: image quality assessment.

Authors:  Hsin Wu Tseng; Andrew Karellas; Srinivasan Vedantham
Journal:  Biomed Phys Eng Express       Date:  2020-09-28

10.  A residual dense network assisted sparse view reconstruction for breast computed tomography.

Authors:  Zhiyang Fu; Hsin Wu Tseng; Srinivasan Vedantham; Andrew Karellas; Ali Bilgin
Journal:  Sci Rep       Date:  2020-12-03       Impact factor: 4.379

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