References
1. J. J. Heit, M. Iv, and M. Wintermark, “Imaging of intracranial hemorrhage,” Journal of Stroke, vol. 19, no. 1. pp. 11–27, 2017, doi: 10.5853/jos.2016.00563.
[doi]
2. C. N. Osuafor et al., “Visualisation of lenticulostriate arteries using contrast-enhanced time-of-flight magnetic resonance angiography at 7 Tesla,” Sci. Rep., vol. 12, no. 1, p. 20306, 2022, doi: 10.1038/s41598-022-24832-z.
[doi]
3. M. Cirillo et al., “Comparison of 3D TOF-MRA and 3D CE-MRA at 3 T for imaging ofintracranial aneurysms,” Eur. J. Radiol., vol. 82, no. 12, pp. e853–e859, Dec. 2013, doi: 10.1016/j.ejrad.2013.08.052.
[doi]
4. M. S. Kang, S. H. Jin, D. K. Lee, and H. J. Cho, “MRI Visualization of Whole Brain Macro- and Microvascular Remodeling in a Rat Model of Ischemic Stroke: A Pilot Study,” Sci. Rep., vol. 10, no. 1, Dec. 2020, doi: 10.1038/s41598-020-61656-1.
[doi]
5. A. H. Kuo, P. Nagpal, B. B. Ghoshhajra, and S. S. Hedgire, “Vascular magnetic resonance angiography techniques,” Cardiovasc. Diagn. Ther., vol. 9, no. Suppl 1, pp. S28–S36, Aug. 2019, doi: 10.21037/CDT.2019.06.07.
[doi]
6. T. Suzuki et al., “Superior Visualization of Neovascularization with Silent Magnetic Resonance Angiography Compared to Time-of-Flight Magnetic Resonance Angiography After Bypass Surgery in Moyamoya Disease,” World Neurosurg., vol. 175, pp. e1292–e1299, 2023, doi: 10.1016/j.wneu.2023.04.119.
[doi]
7. J. C. Carr and T. J. Carroll, “Magnetic resonance angiography: Principles and applications,” Magn. Reson. Angiogr. Princ. Appl., pp. 1–412, Jan. 2012, doi: 10.1007/978-1-4419-1686-0/COVER.
[doi]
8. C. Kilburg, J. S. McNally, A. de Havenon, P. Taussky, M. Y. S. Kalani, and M. S. Park, “Advanced imaging in acute ischemic stroke,” Neurosurg. Focus, vol. 42, no. 4, p. E10, Apr. 2017, doi: 10.3171/2017.1.FOCUS16503.
[doi]
9. T. Suzuki et al., “Non-contrast-enhanced silent magnetic resonance angiography for assessing cerebral aneurysms after PulseRider treatment,” Jpn. J. Radiol., vol. 40, no. 9, pp. 979–985, 2022, doi: 10.1007/s11604-022-01276-z.
[doi]
10. M. Cosottini et al., “Time-of-flight MRA of intracranial vessels at 7 T,” Eur. Radiol. Exp., vol. 8, no. 1, pp. 1–13, Dec. 2024, doi: 10.1186/S41747-024-00463-Z/FIGURES/8.
[doi]
11. S. C. Thust et al., “Paediatric cerebrovascular CT angiography-towards better image quality.,” Quant. Imaging Med. Surg., vol. 4, no. 6, pp. 469–46974, 2014, doi: 10.3978/J.ISSN.2223-4292.2014.10.09.
[doi]
12. T. Schubert et al., “Ultra-High-Resolution Time-of-Flight MR-Angiography for the Noninvasive Assessment of Intracranial Aneurysms, Alternative to Preinterventional DSA?,” Clin. Neuroradiol., vol. 33, no. 4, pp. 1115–1122, Dec. 2023, doi: 10.1007/s00062-023-01320-z.
[doi]
13. D. Summerlin, J. Willis, R. Boggs, L. M. Johnson, and K. K. Porter, “Radiation Dose Reduction Opportunities in Vascular Imaging,” Tomography, vol. 8, no. 5. MDPI, pp. 2618–2638, Oct. 01, 2022, doi: 10.3390/tomography8050219.
[doi]
14. S. Kim, H. W. Park, and S. H. Park, “A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies,” Biomed. Eng. Lett. 2024 146, vol. 14, no. 6, pp. 1221–1242, Sep. 2024, doi: 10.1007/S13534-024-00425-9.
[doi]
15. Z. Xue et al., “A hybrid deep image prior and compressed sensing reconstruction method for highly accelerated 3D coronary magnetic resonance angiography,” Front. Cardiovasc. Med., vol. 11, p. 1408351, Sep. 2024, doi: 10.3389/FCVM.2024.1408351/BIBTEX.
[doi]
16. I. Sánchez and V. Vilaplana, “Brain MRI super-resolution using 3D generative adversarial networks,” 2018, Accessed: Sep. 23, 2023. [Online]. Available: https://github.com/imatge-upc/3D-GAN-superresolution.
17. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017, Feb. 2017, vol. 31, no. 1, pp. 4278–4284, doi: 10.1609/aaai.v31i1.11231.
[doi]
18. J. Wang, Y. Chen, Y. Wu, J. Shi, and J. Gee, “Enhanced generative adversarial network for 3D brain MRI super-resolution,” in Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 2020, pp. 3616–3625, doi: 10.1109/WACV45572.2020.9093603.
[doi]
19. A. Dosovitskiy et al., “AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE,” Oct. 2021, Accessed: Jul. 16, 2025. [Online]. Available: https://arxiv.org/pdf/2010.11929.
20. X. Liu, H. Peng, N. Zheng, Y. Yang, H. Hu, and Y. Yuan, “EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023, vol. 2023-June, pp. 14420–14430, doi: 10.1109/CVPR52729.2023.01386.
[doi]
21. K. P. Wicaksono et al., “Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation,” Eur. Radiol., vol. 33, no. 2, pp. 936–946, 2023, doi: 10.1007/s00330-022-09103-9.
[doi]
22. A. W. Tessema, S. Jin, Y. Gong, and H. Cho, “Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network,” Sci. Reports 2025 151, vol. 15, no. 1, pp. 1–11, Mar. 2025, doi: 10.1038/s41598-025-92493-9.
[doi]
23. H. Li et al., “SRDiff: Single image super-resolution with diffusion probabilistic models,” Neurocomputing, vol. 479, pp. 47–59, 2022, doi: 10.1016/j.neucom.2022.01.029.
[doi]
24. D. D. Blatter, D. L. Parker, and R. O. Robison, “Cerebral MR angiography with multiple overlapping thin slab acquisition: Part I. Quantitative analysis of vessel visibility,” Radiology, vol. 179, no. 3, pp. 805–811, Jun. 1991, doi: 10.1148/RADIOLOGY.179.3.2027996;PAGE:STRING:ARTICLE/CHAPTER.
[doi]
25. M. Maggioni, V. Katkovnik, K. Egiazarian, and A. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruction,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 119–133, 2013, doi: 10.1109/TIP.2012.2210725.
[doi]
26. P. Xu, B. Chen, L. Xue, J. Zhang, L. Zhu, and H. Duan, “A new MNF–BM4D denoising algorithm based on guided filtering for hyperspectral images,” ISA Trans., vol. 92, pp. 315–324, Sep. 2019, doi: 10.1016/J.ISATRA.2019.02.018.
[doi]
27. W. Weng and X. Zhu, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” IEEE Access, vol. 9, pp. 16591–16603, May 2015, doi: 10.1109/ACCESS.2021.3053408.
[doi]
28. I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” Nov. 2019, Accessed: May 12, 2025. [Online]. Available: https://arxiv.org/pdf/1711.05101.
29. S. Van Der Walt et al., “Scikit-image: Image processing in python,” PeerJ, vol. 2014, no. 1, p. e453, Jun. 2014, doi: 10.7717/peerj.453.
[doi]
30. P. Virtanen et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nat. Methods, vol. 17, no. 3, pp. 261–272, Mar. 2020, doi: 10.1038/S41592-019-0686-2;SUBJMETA=114,45,559,56,631,703,706;KWRD=BIOPHYSICAL+CHEMISTRY,COMPUTATIONAL+BIOLOGY+AND+BIOINFORMATICS,TECHNOLOGY.
[doi]
31. I. Koktzoglou, R. Huang, W. J. Ankenbrandt, M. T. Walker, and R. R. Edelman, “Super-resolution head and neck MRA using deep machine learning,” Magn Reson Med, vol. 86, pp. 335–345, 2021, doi: 10.1002/mrm.28738.
[doi]