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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳志宏 | zh_TW |
| dc.contributor.advisor | Jyh-Horng Chen | en |
| dc.contributor.author | 蘇家怡 | zh_TW |
| dc.contributor.author | Gu-Yi Sue | en |
| dc.date.accessioned | 2025-08-20T16:18:28Z | - |
| dc.date.available | 2025-08-21 | - |
| dc.date.copyright | 2025-08-20 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-13 | - |
| dc.identifier.citation | References:
[1] T. Ohnishi, H. Matsuda, T. Tabira, T. Asada, and M. Uno, "Changes in brain morphology in Alzheimer disease and normal aging: is Alzheimer disease an exaggerated aging process?," American Journal of Neuroradiology, vol. 22, no. 9, pp. 1680-1685, 2001. [2] F. Cardinale et al., "Validation of FreeSurfer-estimated brain cortical thickness: comparison with histologic measurements," Neuroinformatics, vol. 12, pp. 535-542, 2014. [3] L. Zhao, W. Matloff, K. Ning, H. Kim, I. D. Dinov, and A. W. Toga, "Age-related differences in brain morphology and the modifiers in middle-aged and older adults," Cerebral Cortex, vol. 29, no. 10, pp. 4169-4193, 2019. [4] Q. Li et al., "Cortical thickness estimation in longitudinal stroke studies: a comparison of 3 measurement methods," NeuroImage: Clinical, vol. 8, pp. 526-535, 2015. [5] R. Seiger, S. Ganger, G. S. Kranz, A. Hahn, and R. Lanzenberger, "Cortical thickness estimations of FreeSurfer and the CAT12 toolbox in patients with Alzheimer's disease and healthy controls," Journal of Neuroimaging, vol. 28, no. 5, pp. 515-523, 2018. [6] M. Reuter, M. D. Tisdall, A. Qureshi, R. L. Buckner, A. J. van der Kouwe, and B. Fischl, "Head motion during MRI acquisition reduces gray matter volume and thickness estimates," Neuroimage, vol. 107, pp. 107-115, 2015. [7] J. E. Iglesias, C.-Y. Liu, P. M. Thompson, and Z. Tu, "Robust brain extraction across datasets and comparison with publicly available methods," IEEE transactions on medical imaging, vol. 30, no. 9, pp. 1617-1634, 2011. [8] M. Maggioni, V. Katkovnik, K. Egiazarian, and A. Foi, "Nonlocal transform-domain filter for volumetric data denoising and reconstruction," IEEE transactions on image processing, vol. 22, no. 1, pp. 119-133, 2012. [9] P. Coupé, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, "An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images," IEEE transactions on medical imaging, vol. 27, no. 4, pp. 425-441, 2008. [10] S. Zia, M. A. Jaffar, A. M. Mirza, and T.-S. Choi, "Rician noise removal from MR images using novel adapted selective non-local means filter," Multimedia tools and applications, vol. 72, no. 1, pp. 1-19, 2014. [11] N. Wiest-Daesslé, S. Prima, P. Coupé, S. P. Morrissey, and C. Barillot, "Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI," in International Conference on Medical Image Computing and Computer-assisted Intervention, 2008: Springer, pp. 171-179. [12] M. G. Harisinghani, A. O’Shea, and R. Weissleder, "Advances in clinical MRI technology," Science Translational Medicine, vol. 11, no. 523, p. eaba2591, 2019. [13] D. L. J. a. H. Y. Lee, "Recent developments in MRI acceleration techniques," Magn. Reson. Insights, vol. vol. 15, pp. pp. 1–10, 2020. [14] K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, "SENSE: sensitivity encoding for fast MRI," Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 42, no. 5, pp. 952-962, 1999. [15] F. A. Breuer, M. Blaimer, R. M. Heidemann, M. F. Mueller, M. A. Griswold, and P. M. Jakob, "Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi‐slice imaging," Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 53, no. 3, pp. 684-691, 2005. [16] M. C. H. Y. T. Chen, K. Y. Lin, "Development of single- and multi-excitation broadband MRI techniques," J. Magn. Reson, vol. 31, 4, pp. 211–220, 2023. [17] J. N. Giedd, R. P. Woods, and C. P. Branch, "GROWTH PATTERNS IN THE DEVELOPING HUMAN BRAIN DETECTED USING CONTINUUM-MECHANICAL TENSOR MAPPING." [18] E. Wu, J. Chen, and T. Chiueh, "Wideband MRI: theoretical analysis and its applications," in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010: IEEE, pp. 5681-5684. [19] V. Fonov et al., "Unbiased average age-appropriate atlases for pediatric studies," Neuroimage, vol. 54, no. 1, pp. 313-327, 2011. [20] B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, "A reproducible evaluation of ANTs similarity metric performance in brain image registration," Neuroimage, vol. 54, no. 3, pp. 2033-2044, 2011. [21] Y. Xiao et al., "Multi-contrast unbiased MRI atlas of a Parkinson’s disease population," International journal of computer assisted radiology and surgery, vol. 10, no. 3, pp. 329-341, 2015. [22] J. Ashburner and K. J. Friston, "Voxel-based morphometry—the methods," Neuroimage, vol. 11, no. 6, pp. 805-821, 2000. [23] A. W. Toga and P. M. Thompson, "The role of image registration in brain mapping," Image and vision computing, vol. 19, no. 1-2, pp. 3-24, 2001. [24] C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. Henson, K. J. Friston, and R. S. Frackowiak, "A voxel-based morphometric study of ageing in 465 normal adult human brains," Neuroimage, vol. 14, no. 1, pp. 21-36, 2001. [25] M. D. Fox and M. E. Raichle, "Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging," Nature reviews neuroscience, vol. 8, no. 9, pp. 700-711, 2007. [26] Y. Tang et al., "The construction of a Chinese MRI brain atlas: a morphometric comparison study between Chinese and Caucasian cohorts," Neuroimage, vol. 51, no. 1, pp. 33-41, 2010. [27] V. S. Fonov, A. C. Evans, R. C. McKinstry, C. R. Almli, and D. Collins, "Unbiased nonlinear average age-appropriate brain templates from birth to adulthood," NeuroImage, vol. 47, p. S102, 2009. [28] Y.-B. Xi et al., "Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication," The British Journal of Psychiatry, vol. 220, no. 6, pp. 339-346, 2022. [29] J. Ashburner et al., "SPM12 manual," Wellcome Trust Centre for Neuroimaging, London, UK, vol. 2464, no. 4, p. 53, 2014. [30] J. E. Iglesias et al., "SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry," Science advances, vol. 9, no. 5, p. eadd3607, 2023. [31] J. V. Manjón, P. Coupé, L. Martí‐Bonmatí, D. L. Collins, and M. Robles, "Adaptive non‐local means denoising of MR images with spatially varying noise levels," Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192-203, 2010. [32] Q. Tian et al., "Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising," NeuroImage, vol. 233, p. 117946, 2021. [33] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Transactions on image processing, vol. 16, no. 8, pp. 2080-2095, 2007. [34] X. Zhu, H.-I. Suk, and D. Shen, "A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis," NeuroImage, vol. 100, pp. 91-105, 2014. [35] N. J. Tustison et al., "N4ITK: improved N3 bias correction," IEEE transactions on medical imaging, vol. 29, no. 6, pp. 1310-1320, 2010. [36] K. Chen, X. Lin, X. Hu, J. Wang, H. Zhong, and L. Jiang, "An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images," BMC Medical Imaging, vol. 20, no. 1, p. 2, 2020. [37] W. Zhao, Y. Lv, Q. Liu, and B. Qin, "Detail-preserving image denoising via adaptive clustering and progressive PCA thresholding," IEEE Access, vol. 6, pp. 6303-6315, 2017. [38] T. Santini, F. Brito, S. Wood, T. Martins, J. Mettenburgh, and H. Aizenstein, "Noise mitigation from high-resolution 7T MRI images," in Proc of the 26th International Society of Magnetic Resonance in Medicine Annual Meeting; Paris, France2018, 2018. [39] R. C. Gonzalez, Digital image processing. Pearson education india, 2009. [40] A. Buades, B. Coll, and J.-M. Morel, "A non-local algorithm for image denoising," in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), 2005, vol. 2: Ieee, pp. 60-65. [41] J. Sijbers and A. J. den Dekker, "Maximum likelihood estimation of signal amplitude and noise variance from MR data," Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 51, no. 3, pp. 586-594, 2004. [42] W. K. Pratt, Digital image processing: PIKS Scientific inside. Wiley Online Library, 2007. [43] V. A. Magnotta, L. Friedman, and F. BIRN, "Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study," Journal of digital imaging, vol. 19, no. 2, pp. 140-147, 2006. [44] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004. [45] D. Brunet, E. R. Vrscay, and Z. Wang, "On the mathematical properties of the structural similarity index," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1488-1499, 2011. [46] O. Dietrich, J. G. Raya, S. B. Reeder, M. F. Reiser, and S. O. Schoenberg, "Measurement of signal‐to‐noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters," Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 26, no. 2, pp. 375-385, 2007. [47] D. Wang, R. K. Robison, Z. Li, and J. G. Pipe, "High SNR rapid T1‐weighted MPRAGE using spiral imaging with long readouts and improved deblurring," Magnetic Resonance in Medicine, vol. 89, no. 3, pp. 951-963, 2023. [48] J. D. Jutras, K. Wachowicz, G. Gilbert, and N. De Zanche, "SNR efficiency of combined bipolar gradient echoes: comparison of three‐dimensional FLASH, MPRAGE, and multiparameter mapping with VFA‐FLASH and MP2RAGE," Magnetic Resonance in Medicine, vol. 77, no. 6, pp. 2186-2202, 2017. [49] Q. Dou, Z. Wang, X. Feng, A. E. Campbell‐Washburn, J. P. Mugler III, and C. H. Meyer, "MRI denoising with a non‐blind deep complex‐valued convolutional neural network," NMR in Biomedicine, vol. 38, no. 1, p. e5291, 2025. [50] S. Fujita et al., "Advancing clinical MRI exams with artificial intelligence: Japan’s contributions and future prospects," Japanese Journal of Radiology, vol. 43, no. 3, pp. 355-364, 2025. [51] Z. Chen, K. Pawar, M. Ekanayake, C. Pain, S. Zhong, and G. F. Egan, "Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges," Journal of Digital Imaging, vol. 36, no. 1, pp. 204-230, 2023. [52] Q. Tian et al., "SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI," Neuroimage, vol. 253, p. 119033, 2022. [53] W. Du and S. Tian, "Transformer and GAN-based super-resolution reconstruction network for medical images," Tsinghua Science and Technology, vol. 29, no. 1, pp. 197-206, 2023. [54] H. Ali et al., "The role of generative adversarial networks in brain MRI: a scoping review," Insights into imaging, vol. 13, no. 1, p. 98, 2022. [55] E. Sizikova et al., "Synthetic data in radiological imaging: current state and future outlook," BJR| Artificial Intelligence, vol. 1, no. 1, p. ubae007, 2024. [56] Z. He et al., "A deep unrolled neural network for real-time MRI-guided brain intervention," Nature Communications, vol. 14, no. 1, p. 8257, 2023. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98925 | - |
| dc.description.abstract | 大腦圖譜在神經醫學與影像診斷上具有重要地位，能幫助研究大腦結構、功能連結與病灶變化。為了提升台灣族群的影像研究品質，本研究以寬頻磁振造影技術建構0.5 mm與1mm等向性高解析度的台灣人腦模板，並比較兩種降噪方法對影像品質的影響。
本研究招募114位受測者，排除不符條件者後，最終納入105位健康成人，皆簽署知情同意書。影像使用西門子 MRI 機器搭配寬頻序列掃描，取得 0.5 mm 及 1 mm 解析度的 T1 加權影像。針對影像中存在的低SNR與Rician雜訊，分別使用BM4D與AONLM兩種降噪方法處理，並進行圖譜構建、融合與分析。計算 SNR、SSIM 與 MAD 三項指標評估降噪成效。 降噪前，兩種解析度的影像皆呈現 SNR 偏低的現象。經處理後，BM4D 顯著提升 SNR， AONLM則更能保留邊緣與細節。個別影像中BM4D的SNR較高；但在群體融合影像中，0.5 mm解析度下 AONLM 效果更佳，1 mm 下 BM4D 則較穩定。融合處理能進一步提升影像穩定性與降噪品質。AONLM 在 0.5 mm 解析度下融合前後皆表現良好。 不同解析度適合不同降噪策略：高解析影像中，過度平滑會抹去細節，AONLM 更能保留結構資訊；較低解析度中，BM4D的全域平滑能有效提升SNR。融合處理能提升演算法效果，使降噪更精準。 本研究成功建構台灣高解析腦模板，並證實 BM4D 與 AONLM 均可有效提升影像品質。建議依據影像解析度與研究目的調整降噪策略與處理順序，以取得最佳分析結果。0.5 mm 等向性影像在掃描時間與品質間取得良好平衡，顯示其具實用性與應用潛力。 | zh_TW |
| dc.description.abstract | Brain atlases play a critical role in neuroimaging and clinical diagnosis by revealing brain structure, functional connections, and lesion localization. To enhance brain research quality for the Taiwanese population, this study developed a high-resolution 1mm and 0.5 mm isotropic brain template using wideband MRI and evaluated the effects of two denoising methods.
A total of 114 subjects were recruited, and 105 healthy adults were included after exclusions. All participants gave informed consent. T1-weighted brain images at 0.5 mm and 1 mm resolutions were acquired using a Siemens MRI scanner with wideband sequences. To address low SNR and Rician noise, BM4D and AONLM were applied. Images were fused and analyzed using metrics including SNR, SSIM, and MAD to evaluate denoising quality. Before denoising, both resolutions exhibited low SNR. BM4D significantly increased SNR, while AONLM better preserved edges and anatomical details. For individual images, BM4D performed better in SNR. In group-fused images, AONLM outperformed BM4D at 0.5 mm resolution, while BM4D was more effective at 1 mm. Fusion improved image stability and enhanced denoising results. AONLM showed consistent performance before and after fusion in high-resolution data. Denoising strategy should be resolution-specific: in high-resolution images, preserving fine structures is essential, favoring AONLM; in lower-resolution images, BM4D’s global smoothing better improves SNR. Fusion further enhances denoising effectiveness by providing more stable input data. This study successfully reconstructed a high-resolution Taiwanese brain template. Both BM4D and AONLM improved image quality. Choosing the appropriate method and processing sequence based on resolution and research objectives is essential for accurate analysis. The 0.5 mm isotropic images offer a good balance of scan time and quality, demonstrating strong potential for future applications. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-20T16:18:28Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-20T16:18:28Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 ii 誌謝 iii 中文摘要 iv ABSTRACT v CONTENTS vii LIST OF FIGURES x LIST OF TABLES xiv Chapter 1 Introduction 1 1.1 Reaserch Background 1 1.2 Reaserch Motivation and Objectives 6 Chapter 2 Materials and Methods 9 2.1 Reconstructing High Resolution Taiwanese Brain Template with Wideband MRI 9 2.1.1 Brain Template 9 2.1.2 Advantages of Using Wideband MRI for High-Resolution Imaging 11 2.1.3 Conceptual Foundation and Methodological Rationale 13 2.1.4 The Importance of Creating Population-Specific Bain Templates 16 2.2 MRI Brain template development workflow for 3D high-resolution Taiwanese brain atlas…… 18 2.2.1 Principle Introduction 18 2.2.2 MRI Brain Template Development Workflow for 3D High-Resolution Taiwanese Brain Atlas…… 19 2.2.3 Verification Criteria and Exclusion Standards in the MRI Image Processing Workflow and Their Impact on Image Quality 21 2.3 Development of Enhanced MRI Sequences and Image Reconstruction Techniques 27 2.4 Optimization and Limitations of Using 0.5 mm High-Resolution MRI in Brain Template Construction 28 2.5 Rationale for Selecting BM4D and AONLM as Denoising Techniques in Structural MRI: A Comparative Justification 30 2.6 Participant Recruitment and Imaging Protocol 32 2.7 Imaging Protocol and Template Construction 33 2.8 Sequence Parameter 34 2.9 MRI Denoising Technique 35 2.9.1 BM4D(Block-Matching 4D Filtering) 36 2.9.2 Adaptive Optimized Non-Local Means(AONLM) 39 2.9.3 Comparison of MRI Denoising Algorithms: BM4D and AONLM 43 2.10 Signal-to-Noise Ratio (SNR) in Image Quality Assessment 45 2.11 Filter Window Configurations in MRI Denoising and Image Quality Assessment 47 Chapter 3 Results and Discussion 49 3.1 Image Comparison 49 3.1.1 Isotropic 1mm V.S. 0.5mm WB 49 3.1.2 MRI Denoising Comparison: BM4D vs. AONLM(0.5mm) 51 3.2 MRI Denoising Comparison:BM4D vs. AONLM (Isotropic 1mm) 54 3.3 Impact of Denoising Techniques and Workflow Sequence on Brain Template Reconstruction 56 3.3.1 Optimizing the Pipeline: Merge vs. BM4D Denoising First in 105Fusion 57 3.3.2 Optimizing the Pipeline in 105Fusion(0.5mm): Merge or AONLM Denoising First 61 3.3.3 105-Subject Fusion (1 mm): BM4D Denoising Before vs. After Merging 63 3.3.4 Optimization of 105-Subject Fusion (1 mm): Comparing AONLM Denoising Before vs. After Merging 65 3.4 Statistical Analysis of Noise Effects on Gray and White Matter Segmentation in 105-Subject Brain Templates 67 3.5 Application and Justification of Image Quality Metrics for MRI Denoising Validation 70 3.6 Quantitative Evaluation of MRI Denoising Methods Using SSIM, MAD, and CNR 71 Chapter 4 Conclusion 79 4.1 Comparison and Analysis of Isotropic 0.5mm and 1 mm MRI Data 79 4.2 Comparison of BM4D and AONLM Denoising Techniques 81 4.3 Construction of a High-Resolution Taiwanese Brain Template and Comparative Evaluation of BM4D and AONLM Denoising Methods 82 Chapter 5 Future Work 84 5.1 Probabilistic Imaging Following Brain Template Segmentation 84 5.2 Future Outlook 89 REFERENCE 91 LIST OF FIGURES Fig. 2.1. Image processing was performed using the latest version of SPM12, an innovative and widely recognized medical imaging analysis software 18 Fig. 2.2. MRI brain template development workflow for a 3D high-resolution Taiwanese brain atlas. 19 Fig. 2.3. Comparison of the original sub-millimeter MPRAGE image with BM4D- and AONLM-denoised results 35 Fig. 2.4. BM4D demonstrates enhanced edge preservation in regions with high noise. 43 Fig. 2.5. Original 0.5 mm Isotropic Resolution MRI Image 43 Fig. 2.6. Comparison of denoised original isotropic 0.5 mm MRI images using the BM4D (left) and AONLM (right) methods 44 Fig. 2.7. Comparison of sagittal slices from isotropic 0.5 mm MRI images denoised using the BM4D and AONLM methods 44 Fig. 3.1. Comparison of original isotropic MRI images in sagittal, coronal, and axial views at 1 mm and 0.5 mm resolutions, without denoising 49 Fig. 3.2. Original 0.5 mm isotropic MRI slice from one subject 51 Fig. 3.3. Single-slice isotropic 0.5 mm MRI image from one subject after BM4D denoising, which represents the highest increase observed 51 Fig. 3.4. Single-slice isotropic 0.5 mm MRI image from one subject after AONLM denoising, indicating a notable increase 52 Fig. 3.5. The upper left shows a single-slice original isotropic 0.5 mm MRI image from one subject, and the upper right shows an enlarged view 53 Fig. 3.6. The upper left shows a single-slice isotropic 0.5 mm MRI image from one subject after BM4D denoising, and the upper right shows an enlarged view 53 Fig. 3.7. The upper left shows a single-slice isotropic 0.5 mm MRI image from one subject after AONLM denoising, and the upper right shows an enlarged view 53 Fig. 3.8. Single-slice original isotropic 1 mm MRI image from one subject 54 Fig. 3.9. Single-slice isotropic 1 mm MRI image from one subject after BM4D denoising 54 Fig. 3.10. Single-slice isotropic 1 mm MRI image from one subject after AONLM denoising 54 Fig. 3.11. Original fused image generated from isotropic 0.5 mm MRI scans of 105 subjects without denoising 58 Fig. 3.12. Fused brain template generated from isotropic 0.5 mm MRI scans of 105 subjects, using a “merge-first, then BM4D denoising” approach. 58 Fig. 3.13. Brain template generated from isotropic 0.5 mm MRI scans of 105 subjects using a “BM4D denoise-first, then merge” approach 59 Fig. 3.14. Fused brain image of 105 subjects acquired at 0.5 mm isotropic resolution without denoising. 60 Fig. 3.15. The original brain images of 105 subjects were first merged to create a 105- subject brain template. BM4D denoising was then applied to the merged 60 Fig. 3.16. The 0.5 mm isotropic brain images of 105 subjects were first denoised individually using BM4D. The denoised images were then merged to create the 105-subject brain template 61 Fig. 3.17. The isotropic 0.5 mm MRI scans of 105 subjects were first merged to create the 105-subject brain template, followed by AONLM denoising 61 Fig. 3.18. The isotropic 0.5 mm brain images of 105 subjects were first denoised individually using AONLM, and the denoised images were then merged to create the 105-subject brain template. 62 Fig. 3.19. Original isotropic 1 mm brain template from 105-subject fusion 63 Fig. 3.20. Brain template of 105 subjects merged first, followed by BM4D denoising. 64 Fig. 3.21. Brain template generated by applying BM4D denoising to individual scans prior to merging 105 subjects. 64 Fig. 3.22. Brain template of 105 subjects created by merging first, followed by AONLM denoising 65 Fig. 3.23. Brain template of 105 subjects generated by applying AONLM denoising to individual scans before merging. 65 Fig. 3.24. Gray matter volume differences between the 1 mm and 0.5 mm groups identified using a two-sample t-test. Results remained significant after family-wise error (FWE) correction 67 Fig. 3.25. White Matter Volume Difference Between 1mm and 0.5mm Without Denoising 69 Fig. 3.26. BM4D Structural Similarity Index (SSIM) Across Participants 71 Fig. 3.27. SSIM for AONLM Denoising across Participants 72 Fig. 3.28. Mean Absolute Deviation (MAD) Values after BM4D Denoising 73 Fig. 3.29. Mean Absolute Deviation (MAD) Values after AONLM Denoising 74 Fig. 3.30. Effect of BM4D Denoising on CNR in the 105-Subject Brain Template 75 Fig. 3.31. Effect of AONLM Denoising on CNR in the 105-Subject Brain Template. 77 Fig. 5.1. Probabilistic maps obtained after brain template segmentation at 1 mm isotropic resolution 85 Fig. 5.2. Probabilistic maps obtained after brain template segmentation at 0.5 mm isotropic resolution. 86 Fig. 5.3. Age-related Decline in Probabilistic Gray Matter Volume 87 Fig. 5.4. illustrates a gradual decline in the probability value of white matter volume as age increases. 88 LIST OF TABLES Table 1.1. Spatial Resolution Comparison. 3 Table 1.2. Spatial Resolution Noise Sensitivity Comparison 4 Table 2.1. MRI Scan Parameters for Template Construction 34 Table 2.2. Denoising Charateristics of AONLM 40 Table 4.1. Comparison Between 1 mm and 0.5 mm MRI Resolutions.. 80 Table 4.2. BM4D vs AONLM 82 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 寬頻磁振造影 | zh_TW |
| dc.subject | 大腦圖譜 | zh_TW |
| dc.subject | 高解析影像 | zh_TW |
| dc.subject | 自適應非局部平均法 | zh_TW |
| dc.subject | 區塊比對四維濾波法 | zh_TW |
| dc.subject | Block-Matching 4D Filtering | en |
| dc.subject | Adaptive Optimized Non-Local Means | en |
| dc.subject | high-resolution imaging | en |
| dc.subject | wideband MRI | en |
| dc.subject | Brain atlas | en |
| dc.title | 用寬頻磁振造影重建高解析度臺灣人腦圖譜 | zh_TW |
| dc.title | Reconstructing High Resolution Taiwanese Brain Template with Wideband MRI | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林慶波;宋孔彬;蘇家豪;郭立威 | zh_TW |
| dc.contributor.oralexamcommittee | Ching-Po Lin;Kung-Bin Sung;Chia-Hao Su;Li-Wei Kuo | en |
| dc.subject.keyword | 大腦圖譜,寬頻磁振造影,區塊比對四維濾波法,自適應非局部平均法,高解析影像, | zh_TW |
| dc.subject.keyword | Brain atlas,wideband MRI,Block-Matching 4D Filtering,Adaptive Optimized Non-Local Means,high-resolution imaging, | en |
| dc.relation.page | 111 | - |
| dc.identifier.doi | 10.6342/NTU202503342 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-15 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 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 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 2.75 MB | Adobe PDF |
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