Deep learning reconstruction を用いた超高精細CTにおける肺気腫定量解析：逐次近似応用再構成法との比較

村松 駿, 佐藤 和宏

doi:10.6009/jjrt.2020_jsrt_76.11.1163

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Quantitative Analysis of Emphysema in Ultra-high-resolution CT by Using Deep Learning Reconstruction: Comparison with Hybrid Iterative Reconstruction
臨床技術 Deep learning reconstructionを用いた超高精細CTにおける肺気腫定量解析 : 逐次近似応用再構成法との比較
リンショウギジュツ Deep learning reconstruction オモチイタチョウコウセイサイ CT ニオケルハイキシュテイリョウカイセキ : チクジキンジオウヨウサイコウセイホウトノヒカク

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<p>Purpose: The noise generated in ultra-high-resolution computed tomography (U-HRCT) images affects the quantitative analysis of emphysema. In this study, we compared the physical properties of reconstructed images for hybrid iterative reconstruction (HIR) and deep learning reconstruction (DLR), which are reconstruction methods for reducing image noise. Using clinical evaluation, we evaluated the correlation between low attenuation volume (LAV) % obtained by CT and forced expiratory volume in 1 s per forced vital capacity (FEV₁/FVC) obtained by respiratory function tests. Materials and methods: CT data obtained by HIR and DLR were used for analysis (matrix size: 1024´1024, slice thickness: 0.25 mm). The physical characteristics were evaluated for the modulation transfer function (MTF) and noise power spectrum (NPS). Display-field of view (D-FOV) was analyzed by varying between 300 mm and 400 mm. The clinical data evaluated the relationship between LAV% and FEV₁/FVC by Spearman’s correlation coefficient. Result: The 10% MTFs were 1.3 cycles/mm (HIR) and 1.3 cycles/mm (DLR) at D-FOV 300 mm, and 1.2 cycles/mm (HIR) and 1.1 cycles/mm (DLR) at D-FOV 400 mm. The NPS had less noise in DLR than HIR in all frequency ranges. The correlation coefficients between LAV% and FEV₁/FVC were 0.64 and 0.71, respectively, in HIR and DLR. Conclusion: There was no difference in the resolution characteristics of HIR and DLR. DLR had better noise characteristics than HIR. The correlation between LAV% measured by HIR and DLR and FEV₁/FVC is equivalent. The noise characteristics of the DLR enable the reduction of exposure to emphysema quantitative analysis by CT.</p>

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日本放射線技術学会雑誌

日本放射線技術学会雑誌 76 (11), 1163-1172, 2020

公益社団法人日本放射線技術学会

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