Relationship between Image Quality and Reconstruction FOV in Deep Learning Reconstructed Images of CT
In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) in children. Standard deviation (SD) of the CT valu...
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| Published in | Japanese Journal of Radiological Technology Vol. 78; no. 10; pp. 1158 - 1166 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
Kyoto
Japanese Society of Radiological Technology
01.01.2022
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0369-4305 1881-4883 |
| DOI | 10.6009/jjrt.2022-1228 |
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| Abstract | In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) in children. Standard deviation (SD) of the CT value, noise power spectrum (NPS), and task-based modulation transfer function (TTF) were evaluated. DLR reduced image noise while maintaining sharpness, and the noise reduction effect showed a different characteristic depending on the size of reconstruction FOV from the conventional image reconstruction methods. The SD of CT value increased gradually in the range from 320 mm to 240 mm, but there was almost no change from 240 mm to 200 mm. The NPS showed completely different characteristics. The low-frequency component increased, and the high-frequency component decreased at 240 mm. However, the frequency component below 0.5 cycle/mm decreased at 200 mm and the peak frequency moved to the lower side at 320 mm. DLR showed the highest TTF value compared to the conventional reconstruction methods. |
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| AbstractList | In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) in children. Standard deviation (SD) of the CT value, noise power spectrum (NPS), and task-based modulation transfer function (TTF) were evaluated. DLR reduced image noise while maintaining sharpness, and the noise reduction effect showed a different characteristic depending on the size of reconstruction FOV from the conventional image reconstruction methods. The SD of CT value increased gradually in the range from 320 mm to 240 mm, but there was almost no change from 240 mm to 200 mm. The NPS showed completely different characteristics. The low-frequency component increased, and the high-frequency component decreased at 240 mm. However, the frequency component below 0.5 cycle/mm decreased at 200 mm and the peak frequency moved to the lower side at 320 mm. DLR showed the highest TTF value compared to the conventional reconstruction methods. |
| ArticleNumber | 2022-1228 |
| Author | Sakamoto, Hiroshi Takano, Hirokazu Kayano, Shingo Onodera, Shu Odagiri, Kanako |
| Author_xml | – sequence: 1 fullname: Odagiri, Kanako organization: Department of Radiology, Division of Medical Technology, Tohoku University Hospital – sequence: 1 fullname: Onodera, Shu organization: Department of Radiology, Division of Medical Technology, Tohoku University Hospital – sequence: 1 fullname: Sakamoto, Hiroshi organization: Department of Radiology, Division of Medical Technology, Tohoku University Hospital – sequence: 1 fullname: Kayano, Shingo organization: Department of Radiology, Division of Medical Technology, Tohoku University Hospital – sequence: 1 fullname: Takano, Hirokazu organization: Department of Radiology, Division of Medical Technology, Tohoku University Hospital |
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| Cites_doi | 10.1038/nmeth.2089 10.1118/1.4725171 10.6009/jjrt.KJ00003324909 10.6009/jjrt.2012_JSRT_68.12.1637 10.1088/0031-9155/52/14/002 10.6009/jjrt.KJ00003109523 10.1007/s00330-019-06170-3 10.1016/j.acra.2019.09.008 10.1007/s12194-014-0273-2 10.1007/s00330-019-06183-y 10.1007/s00330-017-4825-9 |
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| References_xml | – reference: 9） Ichikawa K, Hara T, Ohashi K, et al. CTmeasure. Japanese Society of CT Technology, 2012–2014．https://jsct-tech.org/en/software/（accessed 2021.6.7）. – reference: 2） Akagi M, Nakamura Y, Higaki T, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 2019; 29(11): 6163–6171. – reference: 17） 平野高望，安田光慶，鈴木航，他．逐次近似応用再構成法と逐次近似再構成法における物理評価と視覚評価について：フィルター補正逆投影法との比較．日放技誌 2019; 66(3): 237–242. – reference: 18） 辻岡勝美，安野泰史，片田和廣，他．高速CT装置によるヘリカルスキャンの基礎的検討：第9報：ヘリカルスキャンにおけるスライス厚測定法・評価法について（高速連続CT-6スライス厚特性）．日放技学誌 1993; 49(8): 1346. – reference: 19） 辻岡勝美．ヘリカルCTシステムの技術的問題点：性能評価と実際の運用（螺旋CTスキャンの技術的諸問題）．日放技学誌 1996; 52(3): 389–396. – reference: 14） 藤谷哲也，熊野正士，村上卓道．ダイナミックマルチスライスCTにおける多血性肝細胞癌検出に最適な造影剤量の検討．近畿大医誌 2012; 37(3,4): 155–162. – reference: 11） 日本放射線技術学会 監修．1・2・3 ノイズ特性の測定．標準X線CT画像計測 改訂2版．オーム社，東京，2018，78–80. – reference: 3） Boedeker K. AiCE Deep Learning Reconstruction: bringing the power of Ultra-High Resolution CT to routine imaging. Canon Medical Systems Corporation, 2019. – reference: 5） Higaki T, Nakamura Y, Zhou J, et al. Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 2020; 27(1): 82–87. – reference: 20） 西丸英治，市川勝弘，原孝則，他．逐次近似法を応用したCT画像の新しいNoise Power Spectrum測定法の検討．日放技学誌 2012; 68(12): 1637–1643. – reference: 13） Richard S, Husarik DB, Yadava G, et al. Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms. Med Phys 2012; 39(7Part1): 4115–4122. – reference: 15） Urikura A, Ichikawa K, Hara T, et al. Spatial resolution measurement for iterative reconstruction by use of image-averaging techniques in computed tomography. Radiol Phys Technol 2014; 7(2): 358–366. – reference: 16） Tatsugami F, Higaki T, Nakamura Y, et al. Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 2019; 29(10): 5322–5329. – reference: 6） 厚生労働省．平成27年国民健康・栄養調査報告 第2部 身体状況調査の結果．2016，118. – reference: 4） Higaki T, Nishimaru E, Nakamura Y, et al. Radiation dose reduction in CT using deep learning based reconstruction (DLR): a phantom study. European Society of Radiology 2018; C-1656. – reference: 1） Tatsugami F, Higaki T, Nakamura Y, et al. Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 2019; 29(10): 5322–5329. – reference: 8） Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012; 9(7): 671–675. – reference: 12） 市川勝弘，原孝則，丹羽伸次，他．CT画像におけるノイズパワースペクトル算出方法の比較評価．医用画像情報会誌 2008; 25(2): 29–34. – reference: 10） Boedeker KL, Cooper VN, McNitt-Gray MF. Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of noise power spectra and noise equivalent quanta. Phys Med Biol 2007; 52(14): 4027–4046. – reference: 7） 日本放射線技術学会 編．小児腹部．X線CTにおける標準化～GALACTIC～（改訂2版），放射線医療技術学叢書（27）日本放射線技術学会出版委員会，京都，2015，90. – reference: 21） Euler A, Stieltjes B, Szucs-Farkas Z, et al. Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages. Eur Radiol 2017; 27(12): 5252–5259. – ident: 8 doi: 10.1038/nmeth.2089 – ident: 13 doi: 10.1118/1.4725171 – ident: 17 – ident: 3 – ident: 18 doi: 10.6009/jjrt.KJ00003324909 – ident: 4 – ident: 12 – ident: 11 – ident: 20 doi: 10.6009/jjrt.2012_JSRT_68.12.1637 – ident: 10 doi: 10.1088/0031-9155/52/14/002 – ident: 14 – ident: 19 doi: 10.6009/jjrt.KJ00003109523 – ident: 2 doi: 10.1007/s00330-019-06170-3 – ident: 5 doi: 10.1016/j.acra.2019.09.008 – ident: 15 doi: 10.1007/s12194-014-0273-2 – ident: 1 doi: 10.1007/s00330-019-06183-y – ident: 16 doi: 10.1007/s00330-019-06183-y – ident: 6 – ident: 9 – ident: 7 – ident: 21 doi: 10.1007/s00330-017-4825-9 |
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| SubjectTerms | Computed tomography Deep learning deep learning reconstruction Image acquisition Image processing Image quality Image reconstruction Medical imaging Modulation transfer function noise power spectrum Noise reduction Peak frequency Standard deviation task-based modulation transfer function Tomography |
| Title | Relationship between Image Quality and Reconstruction FOV in Deep Learning Reconstructed Images of CT |
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