08_研究速報_3校_1002.indd
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1 196 MEDICAL IMAGING TECHNOLOGY Vol. 36 No. 4 September 2018 MRI MRI MRI ScSR MRI T1 T2 FLAIR TOF ScSR PSNR SSIM T1 T1 T2 FLAIR ScSR PSNR SSIM Bilinear Bicubic Lanczos p<0.05 TOF ScSR PSNR SSIM Bilinear Bicubic Lanczos ScSR MRI T1 T2 FLAIR TOF MRI Med Imag Tech 36 4 : , K 1 2 4K 3D X CT tishida@sahs.med.osaka-u.ac.jp MRI MRI 1 TR NEX 3 MRI 20 4 Sparse-cording Super-Resolution: ScSR 5-7 ScSR
2 197 Bicubic interpolation MRI T1 T2 ScSR MRI ScSR ScSR MRI 2. D h D l Y 5 5 y 4 y D l min α 1 s.t. FD l α Fy 2 ϵ. 1 α F 1 D l D h α x =D h α 3. the McGill Calibrated Color Image Database 91 National Cancer Institute The Cancer Imaging Archive MRI T1 15 T2 1 a b FLAIR Fluid attenuated IR 2 TOF Time of flight T1 T2 FLAIR TOF P Bicubic 2 1 Bicubic 2 Q P Q Bicubic 2 1 ScSR Bilinear Bicubic Lanczos 2 1
3 198 2 T1 a PSNR SSIM 4 T2 a PSNR b SSIM 3 T1 a PSNR SSIM 5 FLAIR a PSNR b SSIM 1 CPU Intel Core i GHz, RAM 12.0 GB Software MATLAB R2013a Student Version The MathWorks Inc., Natick, MA 1 CPU Intel Core i GHz RAM 10.0 GB Software MATLAB R2016a The MathWorks Inc., Natick, MA Peak signal-to-noise ratio PSNR PSNR 2 PSNR =10 log 10 MAX2 MSE 2 MAX MSE 2 Structural similarity: SSIM SSIM 2 PSNR 11 3 SSIM(a, b)= (2μ aμ b + C 1 )(2σ ab + C 2 ) (μ a 2 +μ b 2 + C 1 )(σ a 2 +σ b 2 + C 2 ) 3 μ a μ b σ a σ b σ ab a b C C SSIM Tukey R ver PSNR SSIM 2 5 T1 T2 FLAIR PSNR SSIM Boxplot T1 T2 FLAIR ScSR Bilinear Bicubic Lanczos PSNR SSIM TOF PSNR SSIM ScSR 3
4 199 1 PSNR db Bilinear Bicubic Lanczos ScSR T ± ± ± ±1.66 T ± ± ± ±2.01 T2 FLAIR TOF 31.21± ± ± ± ± ± ± ± ± ± ± ± SSIM Bilinear Bicubic Lanczos ScSR T ± ± ± ±0.01 T ± ± ± ±0.01 T2 FLAIR TOF 0.88 ± ± ± ± ± ± ± ± ± ± ± ±0.01 c d e 6 T1 a b Bilinear c Bicubic d Lanczos e ScSR c d e 7 T1 a b Bilinear c Bicubic d Lanczos e ScSR c d e 8 T2 a b Bilinear c Bicubic d Lanczos e ScSR ScSR Bilinear Bicubic Lanczos 11 15
5 200 図 9 FLAIR 画像での結果 オリジナル画像 Bilinear Bicubic Lanczos ScSR マトリクスサ イズ 図 10 TOF 画像における各手法による画像 オリジナル画像 Bilinear Bicubic Lanczos ScSR マトリクスサイズ 図 11 T1 画像におけるオリジナル画像と各手法で得られた画像のサブトラクション画像 オリジナル画像 Bilinear Bicubic Lanczos ScSR 図 12 T1 造影 画像におけるオリジナル画像と各手法で得られた画像のサブトラクション画像 オリジナル 画像 Bilinear Bicubic Lanczos ScSR た画像を示す Bilinear Bicubic Lanczos 補間 では差分像に多くの高周波成分が観察できるの に対し ScSR では 差分像に比較的 高周波成 分が残っていないことが観察できた 6. 考 察 画質が ScSR で最もよいと観察できるのは高 周波成分が起因していると考えられる 図 11 図 15 により各手法により失われた高
6 201 図 13 T2 画像におけるオリジナル画像と各手法で得られた画像のサブトラクション画像 オリジナル画像 Bilinear Bicubic Lanczos ScSR 図 14 FLAIR 画像におけるオリジナル画像と各手法で得られた画像のサブトラクション画像 オリジナル画像 Bilinear Bicubic Lanczos ScSR 図 15 TOF 画像におけるオリジナル画像と各手法で得られた画像のサブトラクション画像 オリジナル画像 Bilinear Bicubic Lanczos ScSR 周波成分を可視化することができる 例えば 図 15 に お い て は Bilinear Bicubic Lanczos では差分像に血管影が描出されていることか ら 拡大により多くの高周波成分が失われてい る それに対し ScSR では 差分像に血管影が ほとんど描出されていないことから 高周波成 分を保ったまま拡大をできていることが示唆さ れる 他のシーケンスの画像においても同様に 拡大補間法では脳室の辺縁や脳の辺縁が描出さ れているのに対し ScSR では 拡大補間法と比 較してオリジナル画像との差異が小さいことが わかる 5. 2 の画像の比較において ScSR が どのシーケンスでも画質が向上していることが 観察できたのはこのためであると考えられる ScSR は従来の拡大補間法より画質が向上す るので 実際の臨床で活用することができれ ば 今までは観察することができなかった小さ な病変を発見しうる可能性がある ただし こ の手法にはまだ課題があり その 1 つとして挙 げられるのが処理時間である 今回用いた頭部 MRI 画像のうち マトリクスサイズが であった T1 T2 FLAIR 画像について 本実 験の超解像処理での実行環境では 1 枚当たり 68.6 ± 3.8 秒を要した マトリクスサイズが大き くなるとさらに処理時間が増加する 処理時間 の短縮が臨床で活用する上では重要である 7. まとめ ScSR は従来の画像補間法 Bilinear Bicubic Lanczos よりも MRI の T1 強調画像 T2 強調
7 202 FLAIR TOF 1 Schaefer-Prokop CM, De Boo DW, Uffmann M, et al.: Recent advances in technology. Eur J Radiol 72: , k techon.nikkeibp.co.jp/article/event/ /417500/?st=health : p243 4 : 63: , Yang J, Wright J, Huang TS, et al.: Image super-resolution via sparse representation. IEEE Trans Image Process 19: , Yang J, Wright J, Ma Y, et al.: Image super resolution as sparse representation of raw image patches. In Proc IEEE Conf Comput Vision Pattern Recognit CVPR, Anchorage, 2008, pp1-8 7 CVIM 2013-CVIM-187: 1-9, Iwamoto Y, Han X, Siino A, et al.: Super resolution of 3D medical volumetric images using sparse representation and self-similarity. IEICE Trans 98: , Inagaki T, Shono H: Feature extraction with Sparsecoding for medical images. IPSJ SIG Technical Report 76, Ota J Umehara K, Ishimaru N, et al.: Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography. Proc SPIE 10133: S, Wang Z, Bovik AC, Sheikh HR, et al.: Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13: , 2004 Sparse Coding Super-Resolution Imaging for Enhancing Image Resolution in MRI Naoki ISHIMARU 1, Junko OTA 2, Kensuke UMEHARA 2, Takanori SUZUKI 1, Shunsuke OHNO 3, Kentaro OKAMOTO 4, Takayuki ISHIDA 1 1 Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University 2 National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology 3 Osaka International Cancer Institute 4 Tenri Hospital High-resolution medical images are desirable for practical application of high-resolution displays, however, it takes a long scanning time to improve image resolution in MRI. In this study, we applied and evaluated the sparse coding super-resolution ScSR, which is one of the image processing techniques to obtain high-resolution images, for enhancing image resolution in MRI. For evaluation, T1-weighted images T1, T2-weighted images T2, fluid attenuated IR images FLAIR, and time of flight images TOF were used as the test datasets. We up-sampled all images up to twice and compared the quality of the ScSR scheme and bilinear, bicubic, and lanczos interpolations, which are the traditional interpolation schemes. The image quality was evaluated by measuring peak signal-to-noise ratio PSNR and structural similarity SSIM. As a result, PSNR and SSIM of the ScSR were significantly higher p < 0.05 than those of other three interpolations in T1 original and contrast-enhanced, T2 and FLAIR. In TOF, PSNR and SSIM of the ScSR were higher than those of other three interpolations for all images. These results suggest that the ScSR schemes markedly improve PSNR and SSIM in T1, T2, FLAIR and TOF, in comparison with the traditional interpolation schemes. Key words: Super-resolution, Sparse coding, High resolution, MRI, Up-sampling Med Imag Tech 36 4 : , 2018 * * *
図 2: 高周波成分を用いた超解像 解像度度画像とそれらを低解像度化して得られる 低解像度画像との差により低解像度の高周波成分 を得る 高解像度と低解像度の高周波成分から位 置関係を保ったままパッチ領域をそれぞれ切り出 し 高解像度パッチ画像と低解像度パッチ画像の ペアとしてデータベースに登録する
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