Faezeh Fallah1,2, Jürgen Machann3,4,5, Petros Martirosian3, Fabian Bamberg6, Fritz Schick3, Bin Yang7. 1. Institute of Signal Processing and System Theory, University of Stuttgart, Pfaffenwaldring 47, 70569, Stuttgart, Germany. faezeh.fallah@iss.uni-stuttgart.de. 2. Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Clinic of Tübingen, Tübingen, Germany. faezeh.fallah@iss.uni-stuttgart.de. 3. Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Clinic of Tübingen, Tübingen, Germany. 4. Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center of Munich at University of Tübingen, Tübingen, Germany. 5. German Center for Diabetes Research (DZD), Tübingen, Germany. 6. Department of Diagnostic and Interventional Radiology, University Clinic of Tübingen, Tübingen, Germany. 7. Institute of Signal Processing and System Theory, University of Stuttgart, Pfaffenwaldring 47, 70569, Stuttgart, Germany.
Abstract
OBJECTIVES: To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE). MATERIALS AND METHODS: The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m2 (30.02 ± 6.63 kg/m2) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes. RESULTS: Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images. CONCLUSION: Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.
OBJECTIVES: To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE). MATERIALS AND METHODS: The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m2 (30.02 ± 6.63 kg/m2) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes. RESULTS: Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images. CONCLUSION: Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.
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