OBJECTIVE: The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. MATERIALS AND METHODS: Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. RESULTS: Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). CONCLUSION: In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.
OBJECTIVE: The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. MATERIALS AND METHODS: Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. RESULTS: Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). CONCLUSION: In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.
Authors: Ji Hoon Park; Se Hyung Kim; Hee Sun Park; Gi Hyeon Kim; Jae Young Lee; Jeong Min Lee; Joon Koo Han; Byung Ihn Choi Journal: Eur J Radiol Date: 2010-09-28 Impact factor: 3.528
Authors: Yoshiko Sagara; Amy K Hara; William Pavlicek; Alvin C Silva; Robert G Paden; Qing Wu Journal: AJR Am J Roentgenol Date: 2010-09 Impact factor: 3.959
Authors: Priyanka Prakash; Mannudeep K Kalra; Avinash K Kambadakone; Homer Pien; Jiang Hsieh; Michael A Blake; Dushyant V Sahani Journal: Invest Radiol Date: 2010-04 Impact factor: 6.016
Authors: Jens Altenbernd; Till A Heusner; Adrian Ringelstein; Susanne C Ladd; Michael Forsting; Gerald Antoch Journal: Eur Radiol Date: 2010-10-10 Impact factor: 5.315
Authors: Sarabjeet Singh; Mannudeep K Kalra; Jiang Hsieh; Paul E Licato; Synho Do; Homer H Pien; Michael A Blake Journal: Radiology Date: 2010-09-09 Impact factor: 11.105
Authors: Daniele Marin; Rendon C Nelson; Ehsan Samei; Erik K Paulson; Lisa M Ho; Daniel T Boll; David M DeLong; Terry T Yoshizumi; Sebastian T Schindera Journal: Radiology Date: 2009-04-03 Impact factor: 11.105
Authors: Daniele Marin; Rendon C Nelson; Sebastian T Schindera; Samuel Richard; Richard S Youngblood; Terry T Yoshizumi; Ehsan Samei Journal: Radiology Date: 2010-01 Impact factor: 11.105
Authors: Martin H Goodenberger; Nicolaus A Wagner-Bartak; Shiva Gupta; Xinming Liu; Ramon Q Yap; Jia Sun; Eric P Tamm; Corey T Jensen Journal: J Comput Assist Tomogr Date: 2018 Mar/Apr Impact factor: 1.826
Authors: K A Shpilberg; B N Delman; L N Tanenbaum; S J Esses; R Subramaniam; A H Doshi Journal: AJNR Am J Neuroradiol Date: 2014-07-17 Impact factor: 3.825
Authors: Corey T Jensen; Morgan E Telesmanich; Nicolaus A Wagner-Bartak; Xinming Liu; John Rong; Janio Szklaruk; Aliya Qayyum; Wei Wei; Adam G Chandler; Eric P Tamm Journal: J Comput Assist Tomogr Date: 2017-01 Impact factor: 1.826
Authors: Se Y Choi; Seung H Ahn; Jae D Choi; Jung H Kim; Byoung-Il Lee; Jeong-In Kim; Sung B Park Journal: Br J Radiol Date: 2015-11-18 Impact factor: 3.039
Authors: Ke Li; Daniel Gomez-Cardona; Jiang Hsieh; Meghan G Lubner; Perry J Pickhardt; Guang-Hong Chen Journal: Med Phys Date: 2015-09 Impact factor: 4.071