Brian R Herts1, Andrew Schreiner1, Frank Dong2, Andrew Primak3, Jennifer Bullen4, Wadih Karim1, Douglas Nachand1, Sara Hunter1, Mark E Baker1. 1. Cleveland Clinic, Imaging Institute - Desk L10, Cleveland, OH, USA. 2. Department of Medical Physics - Desk AC-211, Cleveland Clinic, Imaging Institute, Beachwood, OH, USA. 3. c/o Imaging Institute - Desk AC-221, Siemens Healthineers, Beachwood, OH, USA. 4. Department of Quantitative Health Sciences - JJN3, Cleveland Clinic, Cleveland, OH, USA.
Abstract
PURPOSE: The purpose of this study was to assess the effect of obesity and iterative reconstruction on the ability to reduce exposure by studying the accuracy for detection of low-contrast low-attenuation (LCLA) liver lesions on computed tomography (CT) using a phantom model. METHODS: A phantom with four unique LCLA liver lesions (5- to 15-mm spheres, -24 to -6 HU relative to 90-HU background) was scanned without ("thin" phantom) and with ("obese" phantom) a 5-cm thick fat-attenuation ring at 150 mAs (thin phantom) and 450 mAs (obese phantom) standard exposures and at 33% and 67% exposure reductions. Images were reconstructed using standard filtered back projection (FBP) and with iterative reconstruction (Adaptive Model-Based Iterative Reconstruction strength 3, ADMIRE). A noninferiority analysis of lesion detection was performed. RESULTS: Mean area under the curve (AUC) values for lesion detection were significantly higher for the thin phantom than for the obese phantom regardless of exposure level (P < 0.05) for both FBP and ADMIRE. At 33% exposure reduction, AUC was noninferior for both FBP and ADMIRE strength 3 (P < 0.0001). At 67% exposure reduction, AUC remained noninferior for the thin phantom (P < 0.0035), but was no longer noninferior for the obese phantom (P ≥ 0.7353). There were no statistically significant differences in AUC between FBP and ADMIRE at any exposure level for either phantom. CONCLUSIONS: Accuracy for lesion detection was not only significantly lower in the obese phantom at all relative exposures, but detection accuracy decreased sooner while reducing the exposure in the obese phantom. There was no significant difference in lesion detection between FBP and ADMIRE at equivalent exposure levels for either phantom.
PURPOSE: The purpose of this study was to assess the effect of obesity and iterative reconstruction on the ability to reduce exposure by studying the accuracy for detection of low-contrast low-attenuation (LCLA) liver lesions on computed tomography (CT) using a phantom model. METHODS: A phantom with four unique LCLA liver lesions (5- to 15-mm spheres, -24 to -6 HU relative to 90-HU background) was scanned without ("thin" phantom) and with ("obese" phantom) a 5-cm thick fat-attenuation ring at 150 mAs (thin phantom) and 450 mAs (obese phantom) standard exposures and at 33% and 67% exposure reductions. Images were reconstructed using standard filtered back projection (FBP) and with iterative reconstruction (Adaptive Model-Based Iterative Reconstruction strength 3, ADMIRE). A noninferiority analysis of lesion detection was performed. RESULTS: Mean area under the curve (AUC) values for lesion detection were significantly higher for the thin phantom than for the obese phantom regardless of exposure level (P < 0.05) for both FBP and ADMIRE. At 33% exposure reduction, AUC was noninferior for both FBP and ADMIRE strength 3 (P < 0.0001). At 67% exposure reduction, AUC remained noninferior for the thin phantom (P < 0.0035), but was no longer noninferior for the obese phantom (P ≥ 0.7353). There were no statistically significant differences in AUC between FBP and ADMIRE at any exposure level for either phantom. CONCLUSIONS: Accuracy for lesion detection was not only significantly lower in the obese phantom at all relative exposures, but detection accuracy decreased sooner while reducing the exposure in the obese phantom. There was no significant difference in lesion detection between FBP and ADMIRE at equivalent exposure levels for either phantom.
Authors: Achille Mileto; David A Zamora; Adam M Alessio; Carina Pereira; Jin Liu; Puneet Bhargava; Jonathan Carnell; Sophie M Cowan; Manjiri K Dighe; Martin L Gunn; Sooah Kim; Orpheus Kolokythas; Jean H Lee; Jeffrey H Maki; Mariam Moshiri; Ayesha Nasrullah; Ryan B O'Malley; Udo P Schmiedl; Erik V Soloff; Giuseppe V Toia; Carolyn L Wang; Kalpana M Kanal Journal: Radiology Date: 2018-07-17 Impact factor: 11.105
Authors: Ajit H Goenka; Brian R Herts; Nancy A Obuchowski; Andrew N Primak; Frank Dong; Wadih Karim; Mark E Baker Journal: Radiology Date: 2014-03-10 Impact factor: 11.105
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