Atsushi Nakamoto1, Masatoshi Hori2, Hiromitsu Onishi3, Takashi Ota4, Hideyuki Fukui5, Kazuya Ogawa6, Keigo Yano7, Mitsuaki Tatsumi8, Noriyuki Tomiyama9. 1. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: a-nakamoto@radiol.med.osaka-u.ac.jp. 2. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: horimsts@med.kobe-u.ac.jp. 3. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: h-onishi@radiol.med.osaka-u.ac.jp. 4. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: t-ota@radiol.med.osaka-u.ac.jp. 5. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: fukui-hide@radiol.med.osaka-u.ac.jp. 6. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: ogawa-kazu@radiol.med.osaka-u.ac.jp. 7. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: k-yano@radiol.med.osaka-u.ac.jp. 8. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: m-tatsumi@radiol.med.osaka-u.ac.jp. 9. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: tomiyama@radiol.med.osaka-u.ac.jp.
Abstract
PURPOSE: To evaluate the image quality of CT urography (CTU) obtained with ultra-high-resolution CT (U-HRCT) reconstructed with hybrid iterative reconstruction (IR) and model-based IR algorithms. METHOD: Forty-eight patients who underwent CTU using the U-HRCT system were enrolled in this retrospective study. Excretory phase images were reconstructed with three protocols: Protocol A: 1024-matrix, 0.25 mm-thickness, and model-based IR; Protocol B: 1024-matrix, 0.25 mm-thickness, and hybrid IR; Protocol C: 512-matrix, 0.5 mm-thickness, and model-based IR. Objective image noise and contrast-to-noise ratio (CNR) of the renal pelvis were compared among the protocols. Three-dimensional maximum intensity projection CTU images were generated from each image data set, and image quality was evaluated by two radiologists. RESULTS: Protocol C yielded the lowest objective image noise and highest CNR, whereas Protocol A had highest image noise and lowest CNR (P < 0.01). Regarding the detailed delineation of urinary tract structures on the images, the mean visual score was significantly higher for Protocol A than for Protocols B and C (P < 0.001), and the mean score for subjective image noise was significantly lower for Protocol A than for Protocols B and C (P < 0.001). CONCLUSIONS: CTU with a 1024-matrix and model-based IR depicted the structures of the urinary system in the most detail.
PURPOSE: To evaluate the image quality of CT urography (CTU) obtained with ultra-high-resolution CT (U-HRCT) reconstructed with hybrid iterative reconstruction (IR) and model-based IR algorithms. METHOD: Forty-eight patients who underwent CTU using the U-HRCT system were enrolled in this retrospective study. Excretory phase images were reconstructed with three protocols: Protocol A: 1024-matrix, 0.25 mm-thickness, and model-based IR; Protocol B: 1024-matrix, 0.25 mm-thickness, and hybrid IR; Protocol C: 512-matrix, 0.5 mm-thickness, and model-based IR. Objective image noise and contrast-to-noise ratio (CNR) of the renal pelvis were compared among the protocols. Three-dimensional maximum intensity projection CTU images were generated from each image data set, and image quality was evaluated by two radiologists. RESULTS: Protocol C yielded the lowest objective image noise and highest CNR, whereas Protocol A had highest image noise and lowest CNR (P < 0.01). Regarding the detailed delineation of urinary tract structures on the images, the mean visual score was significantly higher for Protocol A than for Protocols B and C (P < 0.001), and the mean score for subjective image noise was significantly lower for Protocol A than for Protocols B and C (P < 0.001). CONCLUSIONS: CTU with a 1024-matrix and model-based IR depicted the structures of the urinary system in the most detail.