Effects of model-based iterative reconstruction on image quality for low-dose computed tomographic angiography of the thoracic aorta in a swine model

Published

Journal Article

© 2015 Wolters Kluwer Health, Inc. Purpose: The aim of the study was to assess the image quality of multi-detector-row computed tomography (CT) angiographic images of the thoracic aorta reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) at different kVp and mA settings. Methods: A healthy 56.1-kg Yorkshire pig underwent sequential arterial CT angiograms on a 64-slice multi-detector-row CT scanner (Discovery CT 750HD; GE Healthcare Inc, Milwaukee, Wis) at progressively lower kVp and mA settings. At 120-, 100-, and 80-kVp levels, the pig was scanned at 700, 400, 200, 100, and 50 mA at, for a total of 15 scans. Each scan was reconstructed with FBP, adaptive statistical iterative reconstruction (50% blend), and MBIR. Relative noise and contrast-to-noise ratio (CNR) were calculated from regions of interest over the aorta and paraspinous muscle. In addition, selected axial and oblique sagittal images were scored subjectively for both aortic wall visibility and for overall image quality. Results: Averaged across all kVp and mAvariations, MBIR reduced relative noise by 73.9% and improved CNR by 227% compared with FBP; MBIR reduced relative noise by 63.4% and improved CNR by 107% compared with ASIR. The effects were more pronounced in lower tube output settings. At 100 kVp/700 mA, MBIR reduced noise by 57% compared with FBP and 40% compared with ASIR. At 100 kVp/50 mA, MBIR reduced noise by 82% compared with FBP and 75% compared with ASIR. Subjective improvements in image quality were noted only in higher noise settings. Conclusions:Model-based iterative reconstruction reduces relative noise and improves CNR compared with ASIR and FBP at all kVp and mA settings, which were significantly greater at lower mA settings.

Full Text

Duke Authors

Cited Authors

  • Caywood, D; Paxton, B; Boll, D; Nelson, R; Kim, C; Lowry, C; Seaman, D; Roos, JE; Hurwitz, LM

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 39 / 2

Start / End Page

  • 196 - 201

Electronic International Standard Serial Number (EISSN)

  • 1532-3145

International Standard Serial Number (ISSN)

  • 0363-8715

Digital Object Identifier (DOI)

  • 10.1097/RCT.0000000000000180

Citation Source

  • Scopus