Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosis and treatment planning. However, due to limitations in imaging hardware, scan time, and patient compliance, the resolution of MRI images is often insufficient. Super-resolution (SR) techniques can enhance MRI resolution, reveal more detailed anatomical information, and improve the identification of complex structures, while also reducing scan time and patient discomfort. However, traditional population-based models trained on large datasets may introduce artifacts or hallucinated structures, which compromise their reliability in clinical applications.
Approach:
To address these challenges, we propose a patient-specific Knowledge Transfer Implicit Neural Representation (KT-INR) super-resolution model. The KT-INR model integrates a dual-head Implicit Neural Network (INR) with a pre-trained Generative Adversarial Network (GAN) model trained on a large-scale dataset. Anatomical information from different MRI sequences of the same patient, combined with the super-resolution mappings learned by the GAN model on population-based dataset, is transferred as prior knowledge to the INR. This integration enhances both the performance and reliability of the super resolution model.
Main Results:
We validated the effectiveness of the KT-INR model across three distinct clinical super-resolution tasks on the BRATS dataset. For Task 1, KT-INR achieved an average SSIM, PSNR, and LPIPS of 0.9813, 36.845, and 0.0186, respectively. In comparison, a state-of-the-art super resolution technique, ArSSR, attained average values of 0.9689, 33.4557, and 0.0309 for the same metrics. The experimental results demonstrate that KT-INR outperforms all other methods across all tasks and evaluation metrics, with particularly remarkable performance in resolving fine anatomical details.
Significance:
The KT-INR model significantly enhances the reliability of super-resolution results, effectively addressing the hallucination effects commonly seen in traditional models. It provides a robust solution for patient-specific MRI super-resolution.