請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80611完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳志宏(Jyh-Horng Chen) | |
| dc.contributor.author | Po-Ting Chen | en |
| dc.contributor.author | 陳柏廷 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:10:42Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-24T03:10:42Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-27 | |
| dc.identifier.citation | 【1】Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., ... Davatzikos, C.（2017）. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data, 4（1）, 1-13. 【2】González, R. G.（2012）. Clinical MRI of acute ischemic stroke. Journal of Magnetic Resonance Imaging, 36（2）, 259-271. 【3】Aerts, H. J.（2016）. The potential of radiomic-based phenotyping in precision medicine：a review. JAMA oncology, 2（12）, 1636-1642. 【4】Pruessmann, K.P., Weiger, M., Scheidegger, M.B. and Boesiger, P.（1999）, SENSE：Sensitivity encoding for fast MRI. Magn. Reson. Med., 42：952-962. 【5】Emsell L., Van Hecke W., Tournier JD.（2016）Introduction to Diffusion Tensor Imaging. In：Van Hecke W., Emsell L., Sunaert S.（eds）Diffusion Tensor Imaging. Springer, New York, NY. 【6】Frank, L. R.（2002）. Characterization of anisotropy in high angular resolution diffusion‐weighted MRI. Magnetic Resonance in Medicine：An Official Journal of the International Society for Magnetic Resonance in Medicine, 47（6）, 1083-1099. 【7】Mukherjee, P., Berman, J. I., Chung, S. W., Hess, C. P., Henry, R. G.（2008）. Diffusion tensor MR imaging and fiber tractography：theoretic underpinnings. American journal of neuroradiology, 29（4）, 632-641. 【8】Kitajima, K., Kaji, Y., Kuroda, K., Sugimura, K.（2008）. High b-value diffusion-weighted imaging in normal and malignant peripheral zone tissue of the prostate：effect of signal-to-noise ratio. Magnetic Resonance in Medical Sciences, 7（2）, 93-99. 【9】Basser, P. J., Mattiello, J., LeBihan, D.（1994）. MR diffusion tensor spectroscopy and imaging. Biophysical journal, 66（1）, 259-267. 【10】Frank, L. R.（2001）. Anisotropy in high angular resolution diffusion‐weighted MRI. Magnetic Resonance in Medicine：An Official Journal of the International Society for Magnetic Resonance in Medicine, 45（6）, 935-939. 【11】Polders, D. L., Leemans, A., Hendrikse, J., Donahue, M. J., Luijten, P. R., Hoogduin, J. M.（2011）. Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla. Journal of Magnetic Resonance Imaging, 33（6）, 1456-1463. 【12】Farrell, J. A., Landman, B. A., Jones, C. K., Smith, S. A., Prince, J. L., Van Zijl, P. C., Mori, S.（2007）. Effects of signal‐to‐noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. Journal of Magnetic Resonance Imaging：An Official Journal of the International Society for Magnetic Resonance in Medicine, 26（3）, 756-767. 【13】Macovski, A.（1996）, Noise in MRI. Magn. Reson. Med., 36：494-497. 【14】Henkelman, R. M.（1985）. Measurement of signal intensities in the presence of noise in MR images. Medical physics, 12（2）, 232-233. 【15】Gudbjartsson, H., Patz, S.（1995）. The Rician distribution of noisy MRI data. Magnetic resonance in medicine, 34（6）, 910-914. 【16】He, L., Greenshields, I. R.（2008）. A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE transactions on medical imaging, 28（2）, 165-172. 【17】Rice, S. O.（1944）. Mathematical analysis of random noise. The Bell System Technical Journal, 23（3）, 282-332. 【18】Sijbers, J., den Dekker, A. J., Van Audekerke, J., Verhoye, M., Van Dyck, D.（1998）. Estimation of the noise in magnitude MR images. Magnetic resonance imaging, 16（1）, 87-90. 【19】Dietrich, O., Raya, J. G., Reiser, M. F.（2008）. Magnetic resonance noise measurements and signal‐quantization effects at very low noise levels. Magnetic Resonance in Medicine：An Official Journal of the International Society for Magnetic Resonance in Medicine, 60（6）, 1477-1487. 【20】Landman, B., Bazin, P. L., Prince, J.（2007, October）. Diffusion tensor estimation by maximizing Rician likelihood. In 2007 IEEE 11th International Conference on Computer Vision（pp. 1-8）. IEEE. 【21】Sijbers, J., Poot, D., den Dekker, A. J., Pintjens, W.（2007）. Automatic estimation of the noise variance from the histogram of a magnetic resonance image. Physics in Medicine Biology, 52（5）, 1335. 【22】Manjón, J. V., Coupe, P.（2018, September）. MRI denoising using deep learning. In International Workshop on Patch-based Techniques in Medical Imaging（pp. 12-19）. Springer, Cham. 【23】Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., ... Iyengar, S. S.（2018）. A survey on deep learning：Algorithms, techniques, and applications. ACM Computing Surveys（CSUR）, 51（5）, 1-36. 【24】Erhan, D., Courville, A., Bengio, Y., Vincent, P.（2010, March）. Why does unsupervised pre-training help deep learning?. In Proceedings of the thirteenth international conference on artificial intelligence and statistics（pp. 201-208）. JMLR Workshop and Conference Proceedings. 【25】Arulkumaran, K., Deisenroth, M. P., Brundage, M., Bharath, A. A.（2017）. Deep reinforcement learning：A brief survey. IEEE Signal Processing Magazine, 34（6）, 26-38.. 【26】Hayou, S., Doucet, A., Rousseau, J.（2019, May）. On the impact of the activation function on deep neural networks training. In International conference on machine learning（pp. 2672-2680）. PMLR. 【27】Nwankpa, C., Ijomah, W., Gachagan, A., Marshall, S.（2018）. Activation functions：Comparison of trends in practice and research for deep learning. arXiv preprint arXiv：1811.03378. 【28】LeCun, Y., Bengio, Y., Hinton, G.（2015）. Deep learning. nature, 521（7553）, 436-444. 【29】Sibi, P., Jones, S. A., Siddarth, P.（2013）. Analysis of different activation functions using back propagation neural networks. Journal of theoretical and applied information technology, 47（3）, 1264-1268. 【30】Agarap, A. F.（2018）. Deep learning using rectified linear units（relu）. arXiv preprint arXiv：1803.08375. 【31】Taylor, G., Burmeister, R., Xu, Z., Singh, B., Patel, A., Goldstein, T.（2016, June）. Training neural networks without gradients：A scalable admm approach. In International conference on machine learning（pp. 2722-2731）. PMLR. 【32】Nicolae, A.（2018）. PLU：The piecewise linear unit activation function. arXiv preprint arXiv：1809.09534. 【33】Agostinelli, F., Hoffman, M., Sadowski, P., Baldi, P.（2014）. Learning activation functions to improve deep neural networks. arXiv preprint arXiv：1412.6830. 【34】Clevert, D. A., Unterthiner, T., Hochreiter, S.（2015）. Fast and accurate deep network learning by exponential linear units（elus）. arXiv preprint arXiv：1511.07289. 【35】Du, B., Wei, Q., Liu, R.（2019）. An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Transactions on Geoscience and Remote Sensing, 57（8）, 6003-6017. 【36】Li, S., Yin, H., Fang, L.（2012）. Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Transactions on biomedical engineering, 59（12）, 3450-3459. 【37】Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.（2007）. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16（8）, 2080-2095. 【38】Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.（2009, September）. Non-local sparse models for image restoration. In 2009 IEEE 12th international conference on computer vision（pp. 2272-2279）. IEEE. 【39】Dong, W., Zhang, L., Shi, G., Li, X.（2012）. Nonlocally centralized sparse representation for image restoration. IEEE transactions on Image Processing, 22（4）, 1620-1630. 【40】Gu, S., Zhang, L., Zuo, W., Feng, X.（2014）. Weighted nuclear norm minimization with application to image denoising. In Proceedings of the IEEE conference on computer vision and pattern recognition（pp. 2862-2869）. 【41】Barbu, A.（2009, June）. Learning real-time MRF inference for image denoising. In 2009 IEEE conference on computer vision and pattern recognition（pp. 1574-1581）. IEEE. 【42】Rudin, L. I., Osher, S., Fatemi, E.（1992）. Nonlinear total variation based noise removal algorithms. Physica D：nonlinear phenomena, 60（1-4）, 259-268. 【43】Buades, A., Coll, B., Morel, J. M.（2005, June）. A non-local algorithm for image denoising. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition（CVPR'05）（Vol. 2, pp. 60-65）. IEEE. 【44】Chen, G., Zhang, P., Wu, Y., Shen, D., Yap, P. T.（2016）. Denoising magnetic resonance images using collaborative non-local means. Neurocomputing, 177, 215-227. 【45】Manjón, J. V., Carbonell-Caballero, J., Lull, J. J., García-Martí, G., Martí-Bonmatí, L., Robles, M.（2008）. MRI denoising using non-local means. Medical image analysis, 12（4）, 514-523. 【46】Manjón, J. V., Coupé, P., Buades, A.（2015）. MRI noise estimation and denoising using non-local PCA. Medical image analysis, 22（1）, 35-47. 【47】Zhang, K., Zuo, W., Zhang, L.（2018）. FFDNet：Toward a fast and flexible solution for CNN-based image denoising. IEEE Transactions on Image Processing, 27（9）, 4608-4622. 【48】Ioffe, S., Szegedy, C.（2015, June）. Batch normalization：Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning（pp. 448-456）. PMLR. 【49】Wu, Y., He, K.（2018）. Group normalization. In Proceedings of the European conference on computer vision（ECCV）（pp. 3-19）. 【50】He, K., Zhang, X., Ren, S., Sun, J.（2016）. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition（pp. 770-778）. 【51】Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.（2017）. Beyond a gaussian denoiser：Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26（7）, 3142-3155. 【52】Tripathi, P. C., Bag, S.（2020）. CNN-DMRI：a convolutional neural network for denoising of magnetic resonance images. Pattern Recognition Letters, 135, 57-63. 【53】Monti, R. P., Tootoonian, S., Cao, R.（2018, October）. Avoiding degradation in deep feed-forward networks by phasing out skip-connections. In International Conference on Artificial Neural Networks（pp. 447-456）. Springer, Cham. 【54】Yeh, F. C.（2017）. Diffusion mri reconstruction in dsi studio. Advanced Biomedical MRI Lab, National Taiwan University Hospital. Available online at：http：//dsi-studio. labsolver. org/Manual/Reconstruction# TOC-Q-Space-Diffeomorphic-Reconstruction-QSDR. 【55】Tian, Q., Bilgic, B., Fan, Q., Liao, C., Ngamsombat, C., Hu, Y., ... Huang, S. Y.（2020）. DeepDTI：High-fidelity six-direction diffusion tensor imaging using deep learning. NeuroImage, 219, 117017. 【56】Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... Rabinovich, A.（2015）. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition（pp. 1-9）. 【57】Tian, C., Xu, Y., Zuo, W.（2020）. Image denoising using deep CNN with batch renormalization. Neural Networks, 121, 461-473. 【58】Ronneberger, O., Fischer, P., Brox, T. （2015, October）. U-net：Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention （pp. 234-241）. Springer, Cham. 【59】Luo, W., Li, Y., Urtasun, R., Zemel, R.（2016, December）. Understanding the effective receptive field in deep convolutional neural networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems（pp. 4905-4913）. 【60】Wu, Y., He, K.（2018）. Group normalization. In Proceedings of the European conference on computer vision（ECCV）（pp. 3-19）. 【61】Kingma, D. P., Ba, J.（2014）. Adam：A method for stochastic optimization. arXiv preprint arXiv：1412.6980. 【62】He, K., Zhang, X., Ren, S., Sun, J.（2016）. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition（pp. 770-778）. 【63】Kim, J., Lee, J. K., Lee, K. M.（2016）. Deeply-recursive convolutional network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition（pp. 1637-1645）. 【64】Huynh-Thu, Q., Ghanbari, M. （2008）. Scope of validity of PSNR in image/video quality assessment. Electronics letters, 44（13）, 800-801. 【65】Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P.（2004）. Image quality assessment：from error visibility to structural similarity. IEEE transactions on image processing, 13（4）, 600-612. 【66】Hore, A., Ziou, D.（2010, August）. Image quality metrics：PSNR vs. SSIM. In 2010 20th international conference on pattern recognition（pp. 2366-2369）. IEEE. 【67】Lin, C. P., Wedeen, V. J., Chen, J. H., Yao, C., Tseng, W. Y. I.（2003）. Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms. Neuroimage, 19（3）, 482-495. 【68】Farrell, J. A., Landman, B. A., Jones, C. K., Smith, S. A., Prince, J. L., van Zijl, P. C., Mori, S.（2007）. Effects of SNR on the accuracy and reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T. Journal of magnetic resonance imaging：JMRI, 26（3）, 756. 【69】Welvaert, M., Rosseel, Y. (2013). On the definition of signal-to-noise ratio and contrast-to-noise ratio for fMRI data. PloS one, 8(11), e77089. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80611 | - |
| dc.description.abstract | 在近十年來，擴散張量成像技術藉由其能夠計算大腦水分子的擴散程度和方向，進而標示神經連結走向的能力，在神經科學及臨床使用上成為不可或缺的工具。 然而擴散張量成像對於影像品質相當要求，過低的影像訊雜比或是解析度都會造成判讀的困難，提升解析度的同時又會造成訊雜比的下降。因此本論文的研究主要目的就是藉由創新的神經網路後處理方式對擴散張量成像進行背景噪音的去除，從而增加影像的訊雜比，並且保留原影像的特徵。 首先，我們將試驗於大鼠的腦成像，藉由提高影像平均次數來獲取高訊雜比的擴散張量圖當作神經網路學習的標的，縱使這會花費較多時間，不過對於神經網路的學習將會有正向的幫助。接下來將會收取低平均次數的影像來當作訓練資料集，藉由殘差學習的方式讓神經網路學習擴散張量成像的背景噪音模式，最後將可以得到去噪後的結果。 在訓練完神經網路之後再取測試集來進行驗證，驗證項目包括進行擴散分析前結構影像的影像相似度，以及分析後會產生的不等向性分率圖、神經走向圖以及與目標圖的角度差異，藉由這些量化指標證明此神經網路的成果足以可信。以目前結果而言，我們的神經網路成果的確可以對大鼠腦的擴散張量影像有相當顯著的去噪效果，可以將訊雜比從30提升至90，等效減少9倍的掃描時間。 不過因為醫學影像資料集的匱乏和取得不易，對於本次所訓練出的模型使用上將有一定限制，包括輸入影像的訊雜比以及掃描位置等各項參數。儘管如此對於適用圖像的各種量化分析方法的計算顯示，在圖像域中影像相似度達到0.984，與標準圖像角度差也達到16.076度，而這個結果是優於過去模型的成效。 總的來說，本論文提供一個對於擴散張量成像之去噪有相當成果的一個神經網路模型，若可以持續擴大訓練資料集，對於神經科學與心理研究和臨床判讀上有莫大的助力。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:10:42Z (GMT). No. of bitstreams: 1 U0001-2210202114373500.pdf: 5725881 bytes, checksum: 7f81c6099c6fedb24a97617e8ffedecd (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審訂書 i 致謝 ii 中文摘要 iii 英文摘要 v 目錄 vii 圖目錄 x 表目錄 xii 第一章 緒論 1-1 研究背景 1 1-2 動機與目的 2 1-3 擴散張量成像 2 1-3-1 擴散張量影像原理 2 1-3-2 擴散張量影像計算 5 1-3-3 擴散張量影像分析 7 1-4 神經網路用於MRI影像去噪背景介紹 10 1-4-1 MRI影像雜訊介紹 10 1-4-2 神經網路原理 12 1-4-3 神經網路用於去噪課題 15 1-4-4 CNN用對於MRI影像去噪課題 17 第二章 方法與材料 2-1 DTI資料蒐集 20 2-1-1 儀器介紹 20 2-1-2 資料參數 21 2-1-3 圖像前處理 22 2-2 神經網路模型設計 24 2-2-1 模型架構 24 2-2-2 模型參數 25 2-3 模型訓練方式 27 2-4 量化分析 28 第三章 人腦結構提升2倍SNR結果 3-1 模型訓練成果 31 3-2 外加雜訊模擬去噪結果 32 3-3 真實人腦影像去噪結果 34 第四章 鼠腦DTI提升三倍SNR結果 4-1 B-Null圖像去噪結果分析 39 4-2 FA Map去噪分布結果 41 4-2-1 FA Map在胼胝體的局部分析 43 4-2-2 FA Map在海馬迴的局部分析 45 4-2-3 FA Map在皮質的局部分析 46 4-3 神經追蹤與角度差分析 50 第五章 討論 5-1 資料集蒐集討論 53 5-2 模型設計討論 55 5-3 DTI去噪討論 56 5-3-1 與其他模型成果比較 56 5-3-2 去噪效能極限 63 第六章 總結與未來展望 6-1 總結 65 6-2 未來展望 66 參考文獻 69 | |
| dc.language.iso | zh-TW | |
| dc.subject | 去噪 | zh_TW |
| dc.subject | 神經網路 | zh_TW |
| dc.subject | 擴散張量成像 | zh_TW |
| dc.subject | 高訊雜比 | zh_TW |
| dc.subject | 神經連結 | zh_TW |
| dc.subject | neural network | en |
| dc.subject | diffusion tensor imaging | en |
| dc.subject | signal-to-noise ratio | en |
| dc.subject | neural tracking | en |
| dc.subject | denoising | en |
| dc.title | 創新邊緣偵測去噪神經網路：提升擴散張量影像訊雜比 | zh_TW |
| dc.title | New Denoising Neural Network with Edge Detection for Diffusion Tensor Imaging Signal-to-Noise Ratio Enhancement | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 傅楸善(Hsin-Tsai Liu),黃從仁(Chih-Yang Tseng),林慶波,郭立威,趙一平 | |
| dc.subject.keyword | 擴散張量成像,神經網路,去噪,高訊雜比,神經連結, | zh_TW |
| dc.subject.keyword | diffusion tensor imaging,neural network,denoising,signal-to-noise ratio,neural tracking, | en |
| dc.relation.page | 77 | |
| dc.identifier.doi | 10.6342/NTU202104028 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2210202114373500.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 5.59 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。