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- Vol. 6 Issue 3
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Simulation-Based FPGA Implementation of MRI Image Reconstruction Using 2D IFFT in Vivado
Authors
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.
Article Information
- Published In
- Volume 6, Issue 3 (2026)
- Published Online
- May-June 2026
- Pages
- 203-208
- Article Type
- Original Article
Abstract
View PDFMagnetic resonance imaging (MRI) relies on transforming raw k-space measurements into clinically meaningful images, a process that is computationally intensive and time-sensitive. This work presents a simulation-based approach for MRI image reconstruction using a field-programmable gate array (FPGA) architecture developed in the Vivado design environment. The proposed method processes complex k-space data through a structured pipeline that includes memory-based data loading, two- dimensional inverse fast Fourier transform (2D-IFFT), magnitude computation, and intensity normalization to generate the final spatial- domain image. The design emphasizes a hardware-oriented workflow where data is handled in a sequential and parallel manner to improve processing efficiency compared to conventional software implementations. Initially validated in simulation, the system demonstrates correct reading and handling of memory-formatted k- space inputs and successful stage-wise transformation toward image reconstruction. The approach is designed with scalability in mind, allowing future integration with real FPGA hardware such as Artix-7 platforms and extension to multi-coil MRI systems. By focusing on efficient data flow and modular design, the proposed framework provides a practical foundation for developing real-time medical imaging solutions. This work contributes to the growing intersection of computational imaging and reconfigurable hardware, offering a pathway toward compact, high-performance MRI reconstruction systems suitable for next-generation biomedical applications.
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