| Project/Area Number |
15K11065
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Pathobiological dentistry/Dental radiology
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| Research Institution | Kitami Institute of Technology |
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Principal Investigator |
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| Research Collaborator |
Cornelia Kober Hamburg University of Applied Sciences, Faculty of Life Sciences, Prof. i.R.
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | X線CT画像 / 統計的画像再構成法 / 逐次近似法 / 圧縮センシング / 歯顎顔面画像診断 / 被ばく軽減 / 全変動正則化 / スパースモデリング |
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| Outline of Final Research Achievements |
The aim is to examine the effect of incomplete, or total elimination of, projection data on computed tomography images subjected to statistical reconstruction (SR) and/or compressed sensing (CS) algorithms. The algebraic reconstruction technique (ART) and the maximum likelihood-expectation maximization (ML-EM) method were compared with filtered back-projection. Effects were studied when the projection data of 360 deg. (360 projections) were decreased to 180 or 90 projections by reducing the collection angle or thinning the image data.
The total variation (TV) regularization method was applied to images processed by the ART. Both ART and ML-EM resulted in high quality at a spread of 90 projections over 180 deg. rotation. TV regularization made it possible to use only 36 projections while still achieving acceptable image quality. Despite heavier computational calculation loading, incomplete projection data made it possible to reduce the radiation dose with SR and CS algorithms.
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| Academic Significance and Societal Importance of the Research Achievements |
医療用X線撮影における「患者の被ばく線量を減らす」という社会的ニーズと，相反するニーズ「画質を劣化させないこと」を同時に実現するために，統計的画像再構成法とスパース（疎性）・モデリングを基にした圧縮センシング法を応用した。特に被ばく線量が問題となるX線CTに応用し，顎口腔領域の画像で検証した。360度ないし180度の投影データに対しART法とTV正則化を組み合わせて実行した。
計算負荷の増大という問題は多少あるが，プロジェクションデータ量で10分の1のＸ線量でCT画像を再構成できる可能性を示した。また，金属アーチファクトの削減効果もあった。医療におけるX線CT画像の使い方を変える可能性がある。
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