Akinori Hata1, Masahiro Yanagawa2, Osamu Honda2, Noriko Kikuchi2, Tomo Miyata2, Shinsuke Tsukagoshi3, Ayumi Uranishi3, Noriyuki Tomiyama2. 1. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: a-hata@radiol.med.osaka-u.ac.jp. 2. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Yamadaoka, Suita, Osaka 565-0871, Japan. 3. Department of CT system Division, Toshiba Medical Systems Corporation, Otawara, Tochigi, Japan.
Abstract
RATIONALE AND OBJECTIVES: This study aimed to assess the effect of matrix size on the spatial resolution and image quality of ultra-high-resolution computed tomography (U-HRCT). MATERIALS AND METHODS: Slit phantoms and 11 cadaveric lungs were scanned on U-HRCT. Slit phantom scans were reconstructed using a 20-mm field of view (FOV) with 1024 matrix size and a 320-mm FOV with 512, 1024, and 2048 matrix sizes. Cadaveric lung scans were reconstructed using 512, 1024, and 2048 matrix sizes. Three observers subjectively scored the images on a three-point scale (1 = worst, 3 = best), in terms of overall image quality, noise, streak artifact, vessel, bronchi, and image findings. The median score of the three observers was evaluated by Wilcoxon signed-rank test with Bonferroni correction. Noise was measured quantitatively and evaluated with the Tukey test. A P value of <.05 was considered significant. RESULTS: The maximum spatial resolution was 0.14 mm; among the 320-mm FOV images, the 2048 matrix had the highest resolution and was significantly better than the 1024 matrix in terms of overall quality, solid nodule, ground-glass opacity, emphysema, intralobular reticulation, honeycombing, and clarity of vessels (P < .05). Both the 2048 and 1024 matrices performed significantly better than the 512 matrix (P < .001), except for noise and streak artifact. The visual and quantitative noise decreased significantly in the order of 512, 1024, and 2048 (P < .001). CONCLUSION: In U-HRCT scans, a large matrix size maintained the spatial resolution and improved the image quality and assessment of lung diseases, despite an increase in image noise, when compared to a 512 matrix size.
RATIONALE AND OBJECTIVES: This study aimed to assess the effect of matrix size on the spatial resolution and image quality of ultra-high-resolution computed tomography (U-HRCT). MATERIALS AND METHODS: Slit phantoms and 11 cadaveric lungs were scanned on U-HRCT. Slit phantom scans were reconstructed using a 20-mm field of view (FOV) with 1024 matrix size and a 320-mm FOV with 512, 1024, and 2048 matrix sizes. Cadaveric lung scans were reconstructed using 512, 1024, and 2048 matrix sizes. Three observers subjectively scored the images on a three-point scale (1 = worst, 3 = best), in terms of overall image quality, noise, streak artifact, vessel, bronchi, and image findings. The median score of the three observers was evaluated by Wilcoxon signed-rank test with Bonferroni correction. Noise was measured quantitatively and evaluated with the Tukey test. A P value of <.05 was considered significant. RESULTS: The maximum spatial resolution was 0.14 mm; among the 320-mm FOV images, the 2048 matrix had the highest resolution and was significantly better than the 1024 matrix in terms of overall quality, solid nodule, ground-glass opacity, emphysema, intralobular reticulation, honeycombing, and clarity of vessels (P < .05). Both the 2048 and 1024 matrices performed significantly better than the 512 matrix (P < .001), except for noise and streak artifact. The visual and quantitative noise decreased significantly in the order of 512, 1024, and 2048 (P < .001). CONCLUSION: In U-HRCT scans, a large matrix size maintained the spatial resolution and improved the image quality and assessment of lung diseases, despite an increase in image noise, when compared to a 512 matrix size.
Authors: K Murayama; S Suzuki; H Nagata; J Oda; I Nakahara; K Katada; K Fujii; H Toyama Journal: AJNR Am J Neuroradiol Date: 2019-12-19 Impact factor: 3.825
Authors: David J Bartlett; Chi Wan Koo; Brian J Bartholmai; Kishore Rajendran; Jayse M Weaver; Ahmed F Halaweish; Shuai Leng; Cynthia H McCollough; Joel G Fletcher Journal: Invest Radiol Date: 2019-03 Impact factor: 6.016