ARCHIVES
Original Article
Simulation-Based FPGA Implementation of MRI Image Reconstruction Using 2D IFFT in Vivado
Kushi K S1
Yashvanth Gowda K S2
Jagadeesh Gowda B3
Saraswathi J M4
Poorvika T P5
Dr. Manoj Kumar S B6
1 2 3 B.E. Students, Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B. G. Nagara, India. 4 5 Assistant Professor, Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B. G. Nagara, India. 6 Associate Professor, Department of Electronics and Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, B. G. Nagara, India.
Published Online: May-June 2026
Pages: 203-208
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260603024
References
1. M. B. Hossain, R. K. Shinde, S. Oh, K.-C. Kwon, and N. Kim, “A systematic review and identification of the challenges of deep learning
techniques for undersampled magnetic resonance image reconstruction,” Sensors 24, 753 (2024).
2. E. López-Ales, J. Martín-García, M. J. Ledesma-Carbayo, and A. Santos, “Multi-device parallel MRI reconstruction: Efficient
implementation of computationally demanding algorithms,” Sensors 24, 1313 (2024).
3. F. Tan, J. G. Delfino, and R. Zeng, “Evaluating machine learning-based MRI reconstruction using digital image quality phantoms,”
Bioengineering 11, 614 (2024).
4. Y. Peng, L. Huang, and X. Zhou, “Joint k-ω space image reconstruction and data fitting for chemical exchange saturation transfer magnetic
resonance imaging,” Tomography 10, 85 (2024).
5. M. F. Yalcinbas, C. Ozturk, O. Ozyurt, U. E. Emir, and U. Bagci, “Rosette trajectory MRI reconstruction with vision transformers,”
Tomography 11, 41 (2025).
6. M. Arshad, F. Najeeb, R. Khawaja, A. Ammar, K. Amjad, and H. Omer, “Cardiac MR image reconstruction using cascaded hybrid dual
domain deep learning framework,” PLOS ONE 20, e0313226 (2025).
7. J. Topalis, J. Dexl, K. Jeblick, R. Klaar, C. Kurz, and T. Löhr, “Fast machine learning image reconstruction of radially undersampled k-
space data for low-latency real-time MRI,” PLOS ONE 20, e0334604 (2025).
8. S. Kocaoğlu, O. Yildirim, and U. R. Acharya, “FPGA implementation of deep learning architecture for ankylosing spondylitis detection
from MRI,” Scientific Reports 15, 25920 (2025).
9. C. Oh, “A hybrid vision transformer-BiRNN architecture for direct k-space to image reconstruction in accelerated MRI,” Journal of
Imaging 12, 11 (2026).
10. E. Aridhi and K. Laabidi, “FPGA technology in healthcare: A comprehensive review of hardware and software solutions for diagnostics,
imaging, and patient care,” Healthcare Analytics 7, 100249 (2025)
techniques for undersampled magnetic resonance image reconstruction,” Sensors 24, 753 (2024).
2. E. López-Ales, J. Martín-García, M. J. Ledesma-Carbayo, and A. Santos, “Multi-device parallel MRI reconstruction: Efficient
implementation of computationally demanding algorithms,” Sensors 24, 1313 (2024).
3. F. Tan, J. G. Delfino, and R. Zeng, “Evaluating machine learning-based MRI reconstruction using digital image quality phantoms,”
Bioengineering 11, 614 (2024).
4. Y. Peng, L. Huang, and X. Zhou, “Joint k-ω space image reconstruction and data fitting for chemical exchange saturation transfer magnetic
resonance imaging,” Tomography 10, 85 (2024).
5. M. F. Yalcinbas, C. Ozturk, O. Ozyurt, U. E. Emir, and U. Bagci, “Rosette trajectory MRI reconstruction with vision transformers,”
Tomography 11, 41 (2025).
6. M. Arshad, F. Najeeb, R. Khawaja, A. Ammar, K. Amjad, and H. Omer, “Cardiac MR image reconstruction using cascaded hybrid dual
domain deep learning framework,” PLOS ONE 20, e0313226 (2025).
7. J. Topalis, J. Dexl, K. Jeblick, R. Klaar, C. Kurz, and T. Löhr, “Fast machine learning image reconstruction of radially undersampled k-
space data for low-latency real-time MRI,” PLOS ONE 20, e0334604 (2025).
8. S. Kocaoğlu, O. Yildirim, and U. R. Acharya, “FPGA implementation of deep learning architecture for ankylosing spondylitis detection
from MRI,” Scientific Reports 15, 25920 (2025).
9. C. Oh, “A hybrid vision transformer-BiRNN architecture for direct k-space to image reconstruction in accelerated MRI,” Journal of
Imaging 12, 11 (2026).
10. E. Aridhi and K. Laabidi, “FPGA technology in healthcare: A comprehensive review of hardware and software solutions for diagnostics,
imaging, and patient care,” Healthcare Analytics 7, 100249 (2025)
Related Articles
2026
A Strategic Framework for Depth-Dependent Hydroelectric Conversion along the Indian Coastline
2026
Reimagining Development in India: A Critical Analysis of the Viksit Bharat Vision
2026
AI-Enabled Image Description: Bridging the Gap for the Visually Impaired
2026
Perceived Occupational Risks of Emergency Medical Services Personnel
2026
Origin, Growth and recent Development of Integrated Reporting (IR): A theoretical Review
2026
