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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 程子翔 | zh_TW |
| dc.contributor.advisor | Kevin T. Chen | en |
| dc.contributor.author | 許汎助 | zh_TW |
| dc.contributor.author | Fan-Chu Hsu | en |
| dc.date.accessioned | 2026-08-21T16:45:38Z | - |
| dc.date.available | 2026-08-22 | - |
| dc.date.copyright | 2026-08-21 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-08-17 13:56:15 | - |
| dc.identifier.citation | [1] M. Boulanger, J.-C. Nunes, H. Chourak, A. Largent, S. Tahri, O. Acosta, R. De Crevoisier, C. Lafond, and A. Barateau, “Deep learning methods to generate synthetic ct from mri in radiotherapy: a literature review,” Physica Medica, vol. 89, pp. 265–281, 2021. 2
[2] M. A. Bahloul, S. Jabeen, S. Benoumhani, H. A. Alsaleh, Z. Belkhatir, and A. Al-Wabil, “Advancements in synthetic ct generation from mri: A review of techniques, and trends in radiation therapy planning,” Journal of Applied Clinical Medical Physics, vol. 25, no. 11, p. e14499, 2024. 2 [3] F. Liu, H. Jang, R. Kijowski, T. Bradshaw, and A. B. McMillan, “Deep learning mr imaging–based attenuation correction for pet/mr imaging,” Radiology, vol. 286, no. 2, pp. 676–684, 2018. 2, 6 [4] M. Miscouridou, J. A. Pineda-Pardo, C. J. Stagg, B. E. Treeby, and A. Stanziola, “Classical and learned mr to pseudo-ct mappings for accurate transcranial ultrasound simulation,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 69, no. 10, pp. 2896–2905, 2022. 2, 8 [5] S. N. Yaakub, T. A. White, E. Kerfoot, L. Verhagen, A. Hammers, and E. F. Fouragnan, “Pseudo-cts from t1-weighted mri for planning of low-intensity transcranial focused ultrasound neuromodulation: An open-source tool,” Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, vol. 16, no. 1, pp. 75–78, 2023. 3 [6] S. Bambach and M.-L. Ho, “Deep learning for synthetic ct from bone mri in the head and neck,” American Journal of Neuroradiology, vol. 43, no. 8, pp. 1172–1179, 2022. 3 [7] W. Luo, Y. Li, R. Urtasun, and R. Zemel, “Understanding the effective receptive field in deep convolutional neural networks,” Advances in neural information processing systems, vol. 29, 2016. 4, 26, 27 [8] F. Ahmed, N. S. Naz, S. Khan, A. U. Rehman, W. M. Ismael, and M. A. Khan, “Explainable artificial intelligence (xai) in medical imaging: a systematic review of techniques, applications, and challenges,” BMC Medical Imaging, 2026. 4, 9 [9] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in 2017 IEEE conference on computer vision and pattern recognition (CVPR). Ieee, 2017, pp. 5967–5976. 5 [10] S. S. Kaushik, M. Bylund, C. Cozzini, D. Shanbhag, S. F. Petit, J. J. Wyatt, M. I. Menzel, C. Pirkl, B. Mehta, V. Chauhan et al., “Region of interest focused mri to synthetic ct translation using regression and segmentation multi-task network,” Physics in Medicine & Biology, vol. 68, no. 19, p. 195003, 2023. 7 [11] S. Dayarathna, K. T. Islam, S. Uribe, G. Yang, M. Hayat, and Z. Chen, “Deep learning based synthesis of mri, ct and pet: Review and analysis,” Medical image analysis, vol. 92, p. 103046, 2024. 9 [12] T. Wang, Y. Lei, Y. Fu, J. F. Wynne, W. J. Curran, T. Liu, and X. Yang, “A review on medical imaging synthesis using deep learning and its clinical applications,” Journal of applied clinical medical physics, vol. 22, no. 1, pp. 11–36, 2021. 9 [13] N. J. Tustison, P. A. Cook, A. J. Holbrook, H. J. Johnson, J. Muschelli, G. A. Devenyi, J. T. Duda, S. R. Das, N. C. Cullen, D. L. Gillen, M. A. Yassa, J. R. Stone, J. C. Gee, and B. B. Avants, “The ANTsX ecosystem for quantitative biological and medical imaging,” Scientific Reports, vol. 11, no. 1, p. 9068, Apr. 2021. [Online]. Available: https://doi.org/10.1038/s41598-021-87564-6 12 [14] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. 12 [15] N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee, “N4itk: Improved n3 bias correction,” IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1310–1320, 2010. 12 [16] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 11 976–11 986. 13, 30 [17] J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450, 2016. 14 [18] D. Hendrycks and K. Gimpel, “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415, 2016. 14 [18] D. Hendrycks and K. Gimpel, "Guassian error linear units(gelus)," arXiv preprint arXiv: 1606.08415, 2016. 14 [19] X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6848–6856. 14 [20] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3–19. 14, 27 [21] F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122, 2015. 15 [22] S. Wang, V. K. Singh, E. Cheah, X. Wang, Q. Li, S.-H. Chou, C. D. Lehman, V. Kumar, and A. E. Samir, “Stacked dilated convolutions and asymmetric architecture for u-net-based medical image segmentation,” Computers in biology and medicine, vol. 148, p. 105891, 2022. 15 [23] H. Qi, W. Wang, Y. Shi, and X. Wang, “Ad-dunet: A dual-branch encoder approach by combining axial transformer with cascaded dilated convolutions for liver and hepatic tumor segmentation,” Biomedical Signal Processing and Control, vol. 95, p. 106397, 2024. 15 [24] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241. 16, 30 [25] J. M. J. Valanarasu and V. M. Patel, “Unext: Mlp-based rapid medical image segmentation network,” in International conference on medical image computing and computer-assisted intervention. Springer, 2022, pp. 23–33. 16 [26] H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y.-W. Chen, and J. Wu, “Unet 3+: A full-scale connected unet for medical image segmentation,” in ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). Ieee, 2020, pp. 1055–1059. 16 [27] M. Mathieu, C. Couprie, and Y. LeCun, “Deep multi-scale video prediction beyond mean square error,” arXiv preprint arXiv:1511.05440, 2015. 18 [28] J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European conference on computer vision. Springer, 2016, pp. 694–711. 18 [29] 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. 18, 21 [30] J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala, “PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation,” in 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS ’24). ACM, Apr. 2024. [Online]. Available: https://docs.pytorch.org/assets/pytorch2-2.pdf 20 [31] I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017. 20 [32] ——, “Sgdr: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983, 2016. 20 [33] C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. Fernández del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature, vol. 585, p. 357–362, 2020. 21 [34] S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality (complete samples),” Biometrika, vol. 52, no. 3-4, pp. 591–611, 1965. 23 [35] Student, “The probable error of a mean,” Biometrika, pp. 1–25, 1908. 23 [36] F. Wilcoxon, “Individual comparisons by ranking methods,” in Breakthroughs in statistics: Methodology and distribution. Springer, 1992, pp. 196–202. 23 [36] F. Wilcoxon, "Individual comparisons by ranking methods," in Break-throughs in statistics: Methodology and distribution. Springer, 1992, pp.196-202. 23 [37] Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal statistical society: series B (Methodological), vol. 57, no. 1, pp. 289–300, 1995. 24 [38] J. Cohen, Statistical power analysis for the behavioral sciences. routledge, 2013. 24 [39] S. B. Morris and R. P. DeShon, “Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs.” Psychological methods, vol. 7, no. 1, p. 105, 2002. 24 [40] A. Salehi and M. Balasubramanian, “Ddcnet: Deep dilated convolutional neural network for dense prediction,” Neurocomputing, vol. 523, pp. 116– 129, 2023. 26 [41] S. N. Yaakub, “BRIC TUS Simulation Tools,” Jun. 2022. [Online]. Available: https://github.com/sitiny/BRIC_TUS_Simulation_Tools 31 [42] L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945. [Online]. Available: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/1932409 35 [43] T. Sφrensen, “A method of establishing groups of equal amplitude in plant sociologybased on similarity of species content and its application to analyses of the vegettation on danish commons,” Biol. Skr., K. danske Vidensk, vol. 5, pp. 1–34, 1948. 35 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/104111 | - |
| dc.description.abstract | 由磁振造影（MR）影像生成偽電腦斷層（pseudo-CT）影像，能在降低對電腦斷層（CT）掃描依賴的同時，保留治療應用所需之物理資訊。然而現有偽電腦斷層生成網路通常仰賴複雜的網路架構與高計算成本，限制其實際應用與部署。因此，本研究提出一種超輕量化且具可解釋性的偽電腦斷層生成網路INet。
相較於傳統使用的UNet，INet僅需20.5%的浮點運算量（FLOPs）以及0.58%的參數數量，即可維持相當的影像重建效能。在獨立測試集上，INet 在多項定量評估指標中皆達到與UNet及UNet3+具統計相當性的表現，並顯著優於UNeXt。定性分析結果進一步顯示，INet 能更有效保留顱骨結構連續性、皮質骨邊界以及局部解剖結構，同時降低重建過程中產生的偽影。透過有效感受野（ERF）與注意力圖（attention map）進行可解釋性分析後發現，INet能有效利用更廣泛的解剖脈絡資訊，並產生具任務適應性的特徵響應，說明其輕量化架構仍能維持良好效能的原因。 為評估生成偽電腦斷層影像之物理保真度，本研究進一步利用真實電腦斷層與偽電腦斷層影像進行穿顱聚焦超音波（tFUS）模擬。由INet生成之偽電腦斷層影像所推導出的聲學模擬參數，與真實電腦斷層所得到之結果未呈現統計上的顯著差異，顯示INet能保留可靠超音波傳播模型所需之顱骨特性。此外使用INet偽電腦斷層進行模擬所得到的結果，與真實電腦斷層模擬結果具有相當的一致性，並且在大部分評估之模擬指標中皆較UNet偽電腦斷層表現出更佳的結果。 綜合以上結果，INet在影像重建準確度、計算效率以及模型可解釋性之間取得良好的平衡。本研究所提出的方法能以輕量化架構生成偽電腦斷層影像，同時保留進行下游穿顱聚焦超音波模擬所需之結構與物理特性，顯示其具備應用於僅使用磁振造影影像之超音波治療規劃流程的潛力。 | zh_TW |
| dc.description.abstract | Pseudo-CT generation from MR images provides a potential solution for reducing the reliance on CT acquisition while preserving the physical information required for therapeutic applications. However, existing pseudo-CT generation networks often rely on complex architectures with high computational costs, limiting their practical deployment. In this study, we propose INet, an ultra-lightweight and explainable network for pseudo-CT generation.
Compared with the conventional UNet, INet reduces computational complexity to 20.5% in FLOPs and 0.58% in parameter count while maintaining comparable reconstruction performance. On the independent testing set, INet achieved statistically comparable performance to UNet and UNet3+ across multiple quantitative metrics, and significantly outperformed UNeXt. Qualitative analysis further demonstrated that INet better preserved skull continuity, cortical boundaries, and local anatomical structures while reducing reconstruction artifacts. Explainability analysis using ERF and attention maps revealed that INet effectively utilizes broader anatomical context and generates task-adaptive feature responses, providing insights into how the lightweight architecture maintains competitive performance. To evaluate the physical fidelity of the generated pseudo-CT images, tFUS simulation was performed using real CT and pseudo-CT images. The acoustic simulation parameters derived from INet-generated pseudo-CT images showed no statistically significant differences compared with those obtained from real CT images, indicating that INet preserves the skull characteristics required for reliable ultrasound propagation modeling. Furthermore, simulations using INet-generated pseudo-CT images achieved comparable agreement with real CT-based simulations and consistently demonstrated improved performance compared with UNet-generated pseudo-CT images across most evaluated simulation metrics. These results demonstrate that INet provides an effective balance between reconstruction accuracy, computational efficiency, and interpretability. The proposed framework enables lightweight pseudo-CT generation while preserving the structural and physical properties required for downstream tFUS simulation, supporting its potential application in MR-only ultrasound treatment planning workflows. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-08-21T16:45:38Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-08-21T16:45:38Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
Chinese Abstract iii Abstract v Contents vii List of Figures xi List of Tables xv List of Abbreviations xix 1 Introduction 1 2 Related Works 5 2.1 Image Translation . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Pseudo-CT Generation . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 tFUS Simulation with Pseudo-CT . . . . . . . . . . . . . . . . . 7 2.4 Limitation of Prior Works . . . . . . . . . . . . . . . . . . . . . . 9 2.5 Purpose of This Study . . . . . . . . . . . . . . . . . . . . . . . . 10 3 Materials and Methods 11 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Data Information . . . . . . . . . . . . . . . . . . . . . . 11 3.1.2 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . 12 3.2 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.1 Proposed Network . . . . . . . . . . . . . . . . . . . . . 13 3.2.2 Benchmark Network . . . . . . . . . . . . . . . . . . . . 16 3.3 Training Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3.1 Data Augmentation . . . . . . . . . . . . . . . . . . . . . 17 3.3.2 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . 17 3.4 Inference Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.4.1 Test-Time Augmentation . . . . . . . . . . . . . . . . . . 19 3.4.2 Validation Mode Inference . . . . . . . . . . . . . . . . . 20 3.4.3 Testing Mode Inference . . . . . . . . . . . . . . . . . . 20 3.5 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.5.1 Implementation Framework . . . . . . . . . . . . . . . . 20 3.5.2 Hyperparameter . . . . . . . . . . . . . . . . . . . . . . . 21 3.5.3 Randomness Control . . . . . . . . . . . . . . . . . . . . 21 3.6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.6.1 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.6.2 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . 23 3.6.3 Computation Consumption . . . . . . . . . . . . . . . . . 24 3.7 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.8 Explainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.8.1 Effective Receptive Field . . . . . . . . . . . . . . . . . . 26 3.8.2 Attention Map . . . . . . . . . . . . . . . . . . . . . . . 27 3.9 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.9.1 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . 28 3.9.2 Network Architecture . . . . . . . . . . . . . . . . . . . . 30 3.10 Downstream Task . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.10.1 Simulation Details . . . . . . . . . . . . . . . . . . . . . 31 3.10.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.10.3 Visualization . . . . . . . . . . . . . . . . . . . . . . . . 36 4 Results 51 4.1 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . 51 4.1.1 Metrics & Hypothesis Testing . . . . . . . . . . . . . . . 51 4.1.2 Computational Consumption . . . . . . . . . . . . . . . . 52 4.1.3 Performance Gain of Testing Mode Inference . . . . . . . 53 4.2 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3 Explainability Analysis . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.1 Effective Receptive Field . . . . . . . . . . . . . . . . . . 55 4.3.2 Attention Map . . . . . . . . . . . . . . . . . . . . . . . 56 4.4 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.2 Network Architecture . . . . . . . . . . . . . . . . . . . . 58 4.5 Downstream Task . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.5.1 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 59 4.5.2 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . 61 5 Discussion 93 5.1 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . 93 5.1.1 Generalizability . . . . . . . . . . . . . . . . . . . . . . . 93 5.1.2 Computational Trade-off . . . . . . . . . . . . . . . . . . 94 5.1.3 Computational Efficiency . . . . . . . . . . . . . . . . . 95 5.2 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.3.1 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . 97 5.3.2 Network Architecture . . . . . . . . . . . . . . . . . . . . 99 5.4 Downstream Task . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.5 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.5.1 Inference Speed . . . . . . . . . . . . . . . . . . . . . . . 104 5.5.2 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . 105 6 Conclusion 109 Reference 111 | - |
| dc.language.iso | en | - |
| dc.subject | 偽電腦斷層影像生成 | - |
| dc.subject | 穿顱聚焦超音波模擬 | - |
| dc.subject | 輕量化網路 | - |
| dc.subject | 深度學習網路 | - |
| dc.subject | 網路可解釋性 | - |
| dc.subject | 計算複雜度 | - |
| dc.subject | Pseudo-CT generation | - |
| dc.subject | tFUS simulation | - |
| dc.subject | Lightweight network | - |
| dc.subject | Deep learning network | - |
| dc.subject | Network explainability | - |
| dc.subject | Computational complexity | - |
| dc.title | INet：用於磁振造影至電腦斷層影像轉換之超輕量化模型 | zh_TW |
| dc.title | INet: An Ultra-Lightweight Network for MR-to-CT Translation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉浩澧;吳文超 | zh_TW |
| dc.contributor.oralexamcommittee | Hao-Li Liu;Wen-Chau Wu | en |
| dc.subject.keyword | 偽電腦斷層影像生成; 穿顱聚焦超音波模擬; 輕量化網路; 深度學習網路; 網路可解釋性; 計算複雜度 | zh_TW |
| dc.subject.keyword | Pseudo-CT generation; tFUS simulation; Lightweight network; Deep learning network; Network explainability; Computational complexity | en |
| dc.relation.page | 117 | - |
| dc.identifier.doi | 10.6342/NTU202601680 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2026-08-20 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 醫學工程學系 | - |
| dc.date.embargo-lift | 2028-08-31 | - |
| 顯示於系所單位： | 醫學工程學研究所 | |
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| ntu-114-2.pdf 此日期後於網路公開 2028-08-31 | 40.9 MB | Adobe PDF |
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