S Gaddikeri1, J B Andre2, J Benjert3, D S Hippe4, Y Anzai2. 1. From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington sg272@uw.edu. 2. From the Department of Neuroradiology (S.G., J.B.A., Y.A.), University of Washington Medical Center, University of Washington, Seattle, Washington. 3. Department of Neuroradiology (J.B.), University of Washington and VA Puget Sound, Seattle, Washington. 4. Department of Radiology (D.S.H.), University of Washington, Seattle, Washington.
Abstract
BACKGROUND AND PURPOSE: Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck. MATERIALS AND METHODS: Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1-5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed. RESULTS: Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 (P = .016), though there was no difference at level 2 (P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx (P < .001) and oropharynx (P < .001) and for overall image quality (P < .001) and was scored lower at the vocal cords (P < .001) and sternoclavicular junction (P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction. CONCLUSIONS: Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction.
BACKGROUND AND PURPOSE: Improved image quality is clinically desired for contrast-enhanced CT of the neck. We compared 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction algorithms for the assessment of image quality of contrast-enhanced CT of the neck. MATERIALS AND METHODS: Neck contrast-enhanced CT data from 64 consecutive patients were reconstructed retrospectively by using 30% adaptive statistical iterative reconstruction and model-based iterative reconstruction. Objective image quality was assessed by comparing SNR, contrast-to-noise ratio, and background noise at levels 1 (mandible) and 2 (superior mediastinum). Two independent blinded readers subjectively graded the image quality on a scale of 1-5, (grade 5 = excellent image quality without artifacts and grade 1 = nondiagnostic image quality with significant artifacts). The percentage of agreement and disagreement between the 2 readers was assessed. RESULTS: Compared with 30% adaptive statistical iterative reconstruction, model-based iterative reconstruction significantly improved the SNR and contrast-to-noise ratio at levels 1 and 2. Model-based iterative reconstruction also decreased background noise at level 1 (P = .016), though there was no difference at level 2 (P = .61). Model-based iterative reconstruction was scored higher than 30% adaptive statistical iterative reconstruction by both reviewers at the nasopharynx (P < .001) and oropharynx (P < .001) and for overall image quality (P < .001) and was scored lower at the vocal cords (P < .001) and sternoclavicular junction (P < .001), due to artifacts related to thyroid shielding that were specific for model-based iterative reconstruction. CONCLUSIONS: Model-based iterative reconstruction offers improved subjective and objective image quality as evidenced by a higher SNR and contrast-to-noise ratio and lower background noise within the same dataset for contrast-enhanced neck CT. Model-based iterative reconstruction has the potential to reduce the radiation dose while maintaining the image quality, with a minor downside being prominent artifacts related to thyroid shield use on model-based iterative reconstruction.
Authors: B M Gramer; D Muenzel; V Leber; A-K von Thaden; H Feussner; A Schneider; M Vembar; N Soni; E J Rummeny; A M Huber Journal: Eur Radiol Date: 2012-07-03 Impact factor: 5.315
Authors: Frédéric A Miéville; Laureline Berteloot; Albane Grandjean; Paul Ayestaran; François Gudinchet; Sabine Schmidt; Francis Brunelle; François O Bochud; Francis R Verdun Journal: Pediatr Radiol Date: 2012-12-07
Authors: William P Shuman; Doug E Green; Janet M Busey; Orpheus Kolokythas; Lee M Mitsumori; Kent M Koprowicz; Jean-Baptiste Thibault; Jiang Hsieh; Adam M Alessio; Eunice Choi; Paul E Kinahan Journal: AJR Am J Roentgenol Date: 2013-05 Impact factor: 3.959
Authors: Amy K Hara; Robert G Paden; Alvin C Silva; Jennifer L Kujak; Holly J Lawder; William Pavlicek Journal: AJR Am J Roentgenol Date: 2009-09 Impact factor: 3.959
Authors: H Imhof; N Schibany; A Ba-Ssalamah; C Czerny; A Hojreh; F Kainberger; C Krestan; H Kudler; I Nöbauer; R Nowotny Journal: Eur J Radiol Date: 2003-07 Impact factor: 3.528
Authors: Cynthia H McCollough; Andrew N Primak; Natalie Braun; James Kofler; Lifeng Yu; Jodie Christner Journal: Radiol Clin North Am Date: 2009-01 Impact factor: 2.303
Authors: S Notohamiprodjo; R Stahl; M Braunagel; P M Kazmierczak; K M Thierfelder; K M Treitl; S Wirth; M Notohamiprodjo Journal: Eur Radiol Date: 2016-12-17 Impact factor: 5.315
Authors: Doris Leithner; Julian L Wichmann; Scherwin Mahmoudi; Simon S Martin; Moritz H Albrecht; Thomas J Vogl; Jan-Erik Scholtz Journal: Br J Radiol Date: 2018-03-08 Impact factor: 3.039
Authors: J-E Scholtz; M Kaup; K Hüsers; M H Albrecht; B Bodelle; S C Metzger; J M Kerl; R W Bauer; T Lehnert; T J Vogl; J L Wichmann Journal: AJNR Am J Neuroradiol Date: 2015-10-01 Impact factor: 3.825
Authors: Jan-Erik Scholtz; Julian L Wichmann; Kristina Hüsers; Moritz H Albrecht; Martin Beeres; Ralf W Bauer; Thomas J Vogl; Boris Bodelle Journal: Eur Radiol Date: 2015-11-11 Impact factor: 5.315
Authors: Ji Eun Lee; Seo-Youn Choi; Jeong Ah Hwang; Sanghyeok Lim; Min Hee Lee; Boem Ha Yi; Jang Gyu Cha Journal: Medicine (Baltimore) Date: 2021-05-14 Impact factor: 1.889
Authors: Jakob Weiss; Michael Maurer; Dominik Ketelsen; Mike Notohamiprodjo; Dominik Zinsser; Julian L Wichmann; Konstantin Nikolaou; Fabian Bamberg; Ahmed E Othman Journal: PLoS One Date: 2017-07-05 Impact factor: 3.240
Authors: Christian J Park; Weijie Chen; Ali Pirasteh; David H Kim; Scott B Perlman; Jessica B Robbins; Alan B McMillan Journal: J Comput Assist Tomogr Date: 2021 Jul-Aug 01 Impact factor: 1.826