OBJECTIVES: The objective of this study was to compare image quality (objective and subjective parameters) and confidence in lesion detection between 3 image reconstruction algorithms in computed tomographic (CT) examinations of the abdomen/pelvis. MATERIALS AND METHODS: This prospective institutional review board-approved study included 65 patients (mean [SD] age, 71.3 ± 9 years; mean [SD] body mass index, 24.4 [4.8] kg) who underwent routine CT examinations of the abdomen/pelvis followed immediately by 2 low-dose scans. Raw data sets were reconstructed by using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and a model-based iterative reconstruction (MBIR). Measurements of objective noise and CT numbers were compared using repeated-measures analysis of variance. Six subjective image quality parameters were scored. Diagnostic confidence and accuracy in detection of various elementary lesions were performed. RESULTS: Objectively, mean image noise for MBIR was significantly superior at all dose levels (P < 0.001). Subjectively, standard-dose ASIR and low-dose MBIR scans were better than standard-dose FBP scan in all parameters assessed (P < 0.05). Low-dose MBIR scans were comparable with standard-dose ASIR scans in all parameters except at noise index of 70 (approximately 85% dose reduction), where, in this case, the detection of liver lesions less than 5 mm were rated inferior (P < 0.05) with diagnostic accuracy reducing to 77.4%. CONCLUSIONS: Low-dose MBIR scan shows superior objective noise reduction compared with standard-dose FBP and ASIR. Subjectively, low-dose MBIR scans at 76% dose reduction were also superior compared with standard-dose FBP and ASIR. However, at dose reductions of 85%, small liver lesions may be missed.
RCT Entities:
OBJECTIVES: The objective of this study was to compare image quality (objective and subjective parameters) and confidence in lesion detection between 3 image reconstruction algorithms in computed tomographic (CT) examinations of the abdomen/pelvis. MATERIALS AND METHODS: This prospective institutional review board-approved study included 65 patients (mean [SD] age, 71.3 ± 9 years; mean [SD] body mass index, 24.4 [4.8] kg) who underwent routine CT examinations of the abdomen/pelvis followed immediately by 2 low-dose scans. Raw data sets were reconstructed by using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and a model-based iterative reconstruction (MBIR). Measurements of objective noise and CT numbers were compared using repeated-measures analysis of variance. Six subjective image quality parameters were scored. Diagnostic confidence and accuracy in detection of various elementary lesions were performed. RESULTS: Objectively, mean image noise for MBIR was significantly superior at all dose levels (P < 0.001). Subjectively, standard-dose ASIR and low-dose MBIR scans were better than standard-dose FBP scan in all parameters assessed (P < 0.05). Low-dose MBIR scans were comparable with standard-dose ASIR scans in all parameters except at noise index of 70 (approximately 85% dose reduction), where, in this case, the detection of liver lesions less than 5 mm were rated inferior (P < 0.05) with diagnostic accuracy reducing to 77.4%. CONCLUSIONS: Low-dose MBIR scan shows superior objective noise reduction compared with standard-dose FBP and ASIR. Subjectively, low-dose MBIR scans at 76% dose reduction were also superior compared with standard-dose FBP and ASIR. However, at dose reductions of 85%, small liver lesions may be missed.
Authors: Sonja Gordic; Lotus Desbiolles; Martin Sedlmair; Robert Manka; André Plass; Bernhard Schmidt; Daniela B Husarik; Francesco Maisano; Simon Wildermuth; Hatem Alkadhi; Sebastian Leschka Journal: Eur Radiol Date: 2015-06-03 Impact factor: 5.315
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: 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