Measuring dynamic CT perfusion based on time-resolved quantitative DECT iodine maps

• Verfasst von: Skornitzke, Stephan [VerfasserIn]
• Verfasst von: Kauczor, Hans-Ulrich [VerfasserIn]
• Verfasst von: Stiller, Wolfram [VerfasserIn]
• Titel: Measuring dynamic CT perfusion based on time-resolved quantitative DECT iodine maps
• Titelzusatz: comparison to conventional perfusion at 80 kVp for Pancreatic Carcinoma
• Verf.angabe: Stephan Skornitzke, Hans-Ulrich Kauczor, Wolfram Stiller
• Jahr: 2019
• Umfang: 8 S.
• Fussnoten: Gesehen am 17.01.2020
• Titel Quelle: Enthalten in: Investigative radiology
• Ort Quelle: Philadelphia, Pa. : Lippincott Williams & Wilkins, 1966
• Jahr Quelle: 2019
• Band/Heft Quelle: 54(2019), 11, Seite 689-696
• ISSN Quelle: 1536-0210
• DOI: doi:10.1097/RLI.0000000000000591
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1687624186

Abstract

Objectives: Using dual-energy computed tomography (DECT) for quantifying iodine content after injection of contrast agent could provide a quantitative basis for dynamic computed tomography (CT) perfusion measurements by means of established mathematical models of contrast agent kinetics, thus improving results by combining the strength of both techniques, which was investigated in this study. Materials and Methods: A dynamic DECT acquisition over 51 seconds performed at 80/Sn140 kVp in 17 patients with pancreatic carcinoma was used to calculate iodine-enhancement images for each time point by means of 3-material decomposition. After motion correction, perfusion maps of blood flow were calculated using the maximum-slope model from both 80 kVp image data and iodine-enhancement images. Blood flow was measured in regions of interest placed in healthy pancreatic tissue and carcinoma for both of the derived perfusion maps. To assess image quality of input data, an adjusted contrast-to-noise ratio was calculated for 80 kVp images and iodine-enhancement images. Susceptibility of perfusion results to residual patient breathing motion during acquisition was investigated by measuring blood flow in fatty tissue surrounding the pancreas, where blood flow should be negligible compared with the pancreas. Results: For both 80 kVp and iodine-enhancement images, blood flow was significantly higher in healthy tissue (114.2 +/- 37.4 mL/100 mL/min or 115.1 +/- 36.2 mL/100 mL/min, respectively) than in carcinoma (46.5 +/- 26.6 mL/100 mL/min or 49.7 +/- 24.7 mL/100 mL/min, respectively). Differences in blood flow between 80 kVp image data and iodine-enhancement images were statistically significant in healthy tissue, but not in carcinoma. For 80 kVp images, adjusted contrast-to-noise ratio was significantly higher (1.3 +/- 1.1) than for iodine-enhancement images (1.1 +/- 0.9). When evaluating fatty tissue surrounding the pancreas for estimating influence of patient motion, measured blood flow was significantly lower for iodine-enhancement images (30.7 +/- 12.0 mL/100 mL/min) than for 80 kVp images (39.0 +/- 19.1 mL/100 mL/min). Average patient radiation exposure was 8.01 mSv for dynamic DECT acquisition, compared with 4.60 mSv for dynamic 80 kVp acquisition. Discussion: Iodine enhancement images can be used to calculate CT perfusion maps of blood flow, and compared with 80 kVp images, results showed only a small difference of 1 mL/100 mL/min in blood flow in healthy tissue, whereas patient radiation exposure was increased for dynamic DECT. Perfusion maps calculated based on iodine-enhancement images showed lower blood flow in fatty tissues surrounding the pancreas, indicating reduced susceptibility to residual patient breathing motion during the acquisition.