DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting

TL;DR

This paper introduces DiffKAN-Inpainting, a novel brain tumor inpainting method combining diffusion models with KAN architecture, achieving more realistic reconstructions than existing methods on the BraTS dataset.

Contribution

It presents a new diffusion-based inpainting approach that integrates KAN architecture and tumor information, improving brain tissue reconstruction quality.

Findings

Outperforms state-of-the-art methods in realism and detail

Achieves higher fidelity in tumor inpainting on BraTS dataset

Provides insights into balancing performance and computational cost

Abstract

Brain tumors delay the standard preprocessing workflow for further examination. Brain inpainting offers a viable, although difficult, solution for tumor tissue processing, which is necessary to improve the precision of the diagnosis and treatment. Most conventional U-Net-based generative models, however, often face challenges in capturing the complex, nonlinear latent representations inherent in brain imaging. In order to accomplish high-quality healthy brain tissue reconstruction, this work proposes DiffKAN-Inpainting, an innovative method that blends diffusion models with the Kolmogorov-Arnold Networks architecture. During the denoising process, we introduce the RePaint method and tumor information to generate images with a higher fidelity and smoother margin. Both qualitative and quantitative results demonstrate that as compared to the state-of-the-art methods, our proposed DiffKAN-Inpainting inpaints more detailed and realistic reconstructions on the BraTS dataset. The knowledge gained from ablation study provide insights for future research to balance performance with computing cost.