\section{Conclusion}
This study demonstrates the effectiveness of self-guided Deep Image Prior for accelerated 3D LGE MRI reconstruction, enabling high-quality isotropic imaging (1.25mm$^3$) with acquisition times reduced to 2-4 minutes. Our comprehensive evaluation of the DIP variants reveals that self-guided DIP outperforms traditional compressed sensing and other DIP approaches. 

This acceleration in acquisition time may address several key clinical challenges by reducing patient discomfort, decreasing motion artifacts, and improving workflow efficiency. 
This acceleration in acquisition time could potentially facilitate high-resolution 3D LGE imaging, which may improve pre-ablation planning and post-procedure assessment.
The method shows particular strength in preserving the details of the thin left atrial wall which is critical to fibrosis assessment.

\cyan{While our results demonstrate significant promise, several limitations should be acknowledged. First, our evaluation was limited to 10 human subjects, and validation on larger cohorts is needed. Publicly available 3D isotropic LGE-MRI datasets (k-space) for reconstruction tasks are currently unavailable, making broader validation challenging.} \green{Second, our study was conducted retrospectively; prospective validation in clinical workflows is necessary to fully assess real-world performance and determine optimal stopping criteria without ground truth references.}

% Future work could explore integrating explicit motion compensation mechanisms. Additionally, investigating the method's generalizability across different scanner platforms and sequence parameters would further support clinical translation. On a more technical side, DIP models are susceptible to noise overfitting in high epochs and they tend to forget about keeping the high-frequency features from heavily undersampled data. In further iterations of our work, we plan to improve on that.

Future work should address three key directions. First, integrating explicit motion compensation mechanisms could further improve image quality in regions affected by respiratory motion. Second, comprehensive evaluation across different scanner platforms and sequence parameters would be essential to validate the method's robustness for potential clinical translation. Finally, addressing the fundamental limitations of DIP models - particularly their susceptibility to noise overfitting at high epochs and difficulty in capturing high-frequency features from undersampled data (spectral bias) \cite{shi2022measuring} - remains crucial for improving reconstruction quality. 