\documentclass{midl} % Include author names

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\jmlrvolume{-- 253}
\jmlryear{2025}
\jmlrworkshop{Full Paper -- MIDL 2025}
\editors{Accepted for publication at MIDL 2025}

\usepackage{multirow}
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\usepackage{graphicx}

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\title[Deep Image Prior Family for fast 3D LGE-MRI
]{Family of Deep Image Prior Networks for Accelerated 3D LGE-MRI Acquisition with Enhanced Reconstruction}

 % Use \Name{Author Name} to specify the name.
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 % enclose the diacritic in braces, e.g. \Name{{\'E}louise Smith}

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%\footnotetext[1]{Contributed equally}

% More complicate cases, e.g. with dual affiliations and joint authorship
\midlauthor{\Name{Md Hasibul Husain Hisham\nametag{$^{1}$}} 
\Email{hasibul.hisham@utah.edu}\\
% \addr $^{2}$ Address 2 \AND
\Name{Shireen Elhabian\nametag{$^{1}$}} \Email{shireen@sci.utah.edu}\\
\Name{Ganesh Adluru\nametag{$^{2}$}} \Email{ganeshsharma.adluru@hsc.utah.edu}\\
\Name{Andrew Arai\nametag{$^{2,3}$}} \Email{andrew.arai@hsc.utah.edu}\\
% \addr $^{3}$ Eugene Kholmovski \AND
\Name{Eugene Kholmovski\nametag{$^{2}$}} \Email{Evgueni.Kholmovski@hsc.utah.edu}\\
\Name{Ravi Ranjan\nametag{$^{3}$}} \Email{ravi.ranjan@hsc.utah.edu}\\
\Name{Edward DiBella\nametag{$^{2}$}} \Email{edward.dibella@hsc.utah.edu}\\
\addr $^{1}$ Kahlert School of Computing, University of Utah, Salt Lake City, UT, United States \\
\addr $^{2}$ Radiology \& Imaging Sciences, University of Utah, Salt Lake City, UT, United States \\
\addr $^{3}$ Cardiology, University of Utah, Salt Lake City, UT, United States
}

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\begin{document}

% Kahlert School of Computing, University of Utah, Salt Lake City, UT, United States, Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, United States

\maketitle

\begin{abstract}
% Late Gadolinium Enhancement (LGE) Magnetic Resonance Imaging is critical for visualizing left atrial fibrosis in atrial fibrillation patients, but current 3D LGE MRI protocols are constrained by lengthy acquisition times and variable image quality. This study comprehensively evaluates a family of Deep Image Prior (DIP) methods for accelerated 3D LGE MRI reconstruction. Using clinical data from 10 human subjects, the research systematically assesses these methods' performance across multiple acceleration factors, ranging from 1/6th to full acquisition time. The self-guided DIP variant demonstrated superior performance, reducing scan time from 10-12 minutes to 2-4 minutes while maintaining diagnostic image quality. Quantitative analysis revealed the self-guided DIP achieved higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to traditional compressed sensing techniques. These findings provide insights into the potential of unsupervised learning approaches for accelerating cardiac MRI reconstruction.

Late Gadolinium Enhancement (LGE) MRI is essential for visualizing and treating left atrial fibrosis, but current protocols require lengthy acquisition times (7-20 minutes) and often produce suboptimal image quality. While recent advances in isotropic imaging have shown promise, scan times of 12-15 minutes still present clinical challenges. This study evaluates the efficacy of existing Deep Image Prior (DIP) frameworks for accelerated 3D LGE-MRI reconstruction. We comprehensively assess multiple DIP variants - vanilla DIP, reference-guided DIP, DIP with Total Variation, and self-guided DIP - on their ability to reconstruct high-quality isotropic (1.25mm$^3$) images from highly undersampled k-space data. Using data from 10 subjects, we demonstrate that self-guided DIP achieves superior reconstruction quality (PSNR: 32.8±1.2 dB, SSIM: 0.891±0.015 at 1/4th of acquisition time) compared to traditional compressed sensing and other DIP variants. Our evaluation shows that DIP-based reconstruction can maintain diagnostic quality with acquisition times reduced to 2-4 minutes, particularly in preserving thin left atrial wall details. These findings suggest that DIP-based methods could improve clinical workflow efficiency and patient comfort in high-resolution 3D LGE studies for atrial fibrillation patients.

\end{abstract}

\begin{keywords}
MRI Image Acquision \& Reconstruction, Deep Image Prior, Unsupervised Learning
\end{keywords}

\input{sections/introduction}
\input{sections/related_works}
\input{sections/dataset}
\input{sections/methods}
\input{sections/results}
\input{sections/conclusion}



\clearpage  % Acknowledgements, references, and appendix do not count toward the page limit (if any)
% Acknowledgments---Will not appear in anonymized version
\midlacknowledgments{This work was supported by The National Heart, Lung, and blood Institute of the National Institute of Health under award
R01HL162353. The content is solely the responsibility of the authors and does not necessarily represent the official views of the
National Institute of Health.}

\bibliography{midl25_253}


% \appendix

% \section{Proof of Theorem 1}

% This is a boring technical proof of
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% \cos^2\theta + \sin^2\theta \equiv 1.
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% \section{Proof of Theorem 2}

% This is a complete version of a proof sketched in the main text.

\end{document}
