Enhancing Lung CT Image Quality through Frequency Domain Artifact Suppression

Computed Tomography (CT) imaging plays a crucial role in the diagnosis and monitoring of lung-related diseases. However, the presence of high-density materials, such as metallic implants or surgical clips, often introduces artifacts that degrade image quality and hinder accurate clinical interpretation. This study presents a frequency domain filtering approach to suppress such reconstruction artifacts in lung CT images, thereby improving diagnostic clarity. The proposed method begins with preprocessing of CT slices, followed by transformation into the frequency domain using the Fast Fourier Transform (FFT). Specific frequency components responsible for artifacts are selectively attenuated using tailored notch and band-stop filters. This process effectively minimizes the streaking and blurring effects commonly observed around metallic objects. The filtering algorithm is first developed and tested in MATLAB, where its performance is evaluated using metrics such as Peak Signal-toNoise Ratio (PSNR) and Structural Similarity Index (SSIM). The results show a notable improvement in visual quality and structural preservation of the lung tissues. To explore the feasibility of hardware acceleration for real-time applications, the same filtering algorithm is implemented in Verilog and simulated on an FPGA platform. The hardware implementation demonstrates low-latency performance with efficient resource utilization, making it suitable for integration in embedded medical imaging systems. This combined softwarehardware approach underscores the potential of frequency domain filtering as a practical solution for metal artifact reduction in CT images and opens avenues for real-time deployment in clinical imaging workflows.