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Magnetic 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.