\section{Introduction}

LGE-MRI plays a role in the clinical management of atrial fibrillation patients \cite{mcgann2008new}. Current 3D LGE-MRI protocols, while effective, face significant challenges that limit their clinical utility. The standard ECG-gated and respiratory navigated sequences require lengthy acquisition times (typically 7-20 minutes), often resulting in patient discomfort and increased susceptibility to motion artifacts. Moreover, these sequences frequently produce inadequate image quality and suffer from anisotropic resolution limitations.
The clinical need for high-resolution isotropic imaging is particularly acute in cardiac applications, where precise visualization of thin atrial walls is crucial for diagnosis and treatment planning. 

While recent advances in fixed-time isotropic 3D LGE-MRI methods have shown promise in improving spatial resolution, the current standard of 12-15 minute acquisition times still presents challenges for patient comfort and workflow efficiency. Additionally, the prevalence of respiratory motion in these scans introduces unique complexities not encountered in other anatomical imaging sites, such as brain or knee imaging.
This research studies the efficacy to accelerate 3D LGE-MRI reconstruction using a family of Deep Image Prior (DIP) methods. Unlike traditional supervised deep learning approaches that require extensive training data, DIP-based methods offer several distinct advantages for cardiac imaging:

\begin{itemize}
\item They optimize network parameters for each specific case, allowing adaptation to patient-specific characteristics such as unique anatomical variations, motion patterns, and image contrast properties. This personalization is particularly valuable in cardiac imaging where inter-patient variability is high.
\item They maintain robust performance even with highly undersampled K-space data by leveraging the network architecture itself as a learned prior. This approach naturally handles the incoherent artifacts that arise from undersampling, making it particularly effective for accelerated acquisitions.
\item They avoid the need for large training datasets, which are particularly challenging to obtain in cardiac imaging due to the complexity of acquiring fully-sampled reference data and motion-related challenges.
% and the relative scarcity of cardiac MRI data compared to other anatomical regions.
\end{itemize}

This work aims to achieve high-quality isotropic reconstructions (1.25mm$^3$) from significantly reduced acquisition times (approximately 2-4 minutes), representing a 4-6X acceleration compared to current standards. The goal is to maintain diagnostic quality while addressing the fundamental limitations of current protocols, which could expand the accessibility of 3D LGE studies for atrial fibrillation patients.
