\section{Related Works}

The field of accelerated MRI reconstruction has evolved significantly over the past decade, progressing from traditional compressed sensing approaches to sophisticated deep learning methods. Compressed sensing (CS) leverages the inherent sparsity of MRI data in appropriate transform domains to enable high-quality reconstruction from undersampled K-space measurements. Such approaches usually combine data consistency terms with carefully chosen regularizers, such as Total Variation (TV) \cite{tv1, tv2} or wavelets \cite{wavelet1, wavelet2}, to promote sparsity while preserving image features. Ganesh et al.'s recent works include combining the above steps with Block-Matching and 3D filtering (BM3D), thus exploiting non-local self-similarity in images \cite{adluruisotropic}. However, these iterative reconstruction methods often require careful parameter tuning and can be computationally intensive, especially for high-resolution 3D acquisitions.

Early Deep Learning-based approaches focused on supervised learning, where networks were trained on paired datasets of undersampled and fully-sampled images \cite{ramanarayanan2023generalizing}. While effective, these methods face limitations in clinical deployment due to their dependence on high-quality training data and potential challenges in generalizing to different acquisition protocols or anatomical variations. Ulyanov et al. proposed Deep Image Prior (DIP) which bridges the gap between traditional optimization-based methods and deep learning \cite{ulyanov2018deep}. DIP leverages the structure of convolutional neural networks as an implicit prior, eliminating the need for training data. The framework has been successfully adapted to various image restoration tasks, including super-resolution, inpainting, and denoising \cite{jo2021rethinking}. 

In the context of MRI reconstruction, several DIP variants have been proposed. Yazdanpanah et al. have adapted the basic DIP framework to the MRI physics model \cite{pour2019deep}. Xue et al. have explored combining DIP with TV to leverage both the implicit network prior and explicit spatial regularization, on 3D CMRA data \cite{xue2024hybrid}. However, they perform the DIP in a slice-by-slice manner (2D), not utilizing the 3D resolution. Building on these developments, self-guided learning has emerged as a promising direction in unsupervised image reconstruction \cite{liang2024analysis}. Bell et al. have leveraged the network's predictions as a form of regularization, reducing the dependence on hand-crafted priors \cite{bell2023robust}. 

% Self-guided DIP has shown particular promise in handling the noise amplification challenges inherent in accelerated imaging. 

Late Gadolinium Enhancement (LGE) cardiac MRI presents unique challenges that make it an ideal testing ground for advanced reconstruction methods. The need to capture fine structural details in the heart while managing respiratory motion makes high-resolution isotropic imaging particularly challenging. Traditional approaches often rely on anisotropic acquisitions, sacrificing through-plane resolution for reduced scan time. Recent work has focused on enabling high-quality isotropic imaging through advanced reconstruction techniques \cite{adluruisotropic}, though maintaining diagnostic quality with highly accelerated acquisitions remains an open challenge. 