@article{uus2020deformable,
  title={Deformable slice-to-volume registration for motion correction of fetal body and placenta MRI},
  author={Uus, Alena and Zhang, Tong and Jackson, Laurence H and Roberts, Thomas A and Rutherford, Mary A and Hajnal, Joseph V and Deprez, Maria},
  journal={IEEE transactions on medical imaging},
  volume={39},
  number={9},
  pages={2750--2759},
  year={2020},
  publisher={IEEE}
}

@article{uus2023retrospective,
  title={Retrospective motion correction in foetal {MRI} for clinical applications: existing methods, applications and integration into clinical practice},
  author={Uus, Alena U and Egloff Collado, Alexia and Roberts, Thomas A and Hajnal, Joseph V and Rutherford, Mary A and Deprez, Maria},
  journal={The British journal of radiology},
  volume={96},
  number={1147},
  pages={20220071},
  year={2023},
  publisher={The British Institute of Radiology.}
}

@article{ebner2020automated,
  title={An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain {MRI}},
  author={Ebner, Michael and Wang, Guotai and Li, Wenqi and Aertsen, Michael and Patel, Premal A and Aughwane, Rosalind and Melbourne, Andrew and Doel, Tom and Dymarkowski, Steven and De Coppi, Paolo and others},
  journal={NeuroImage},
  volume={206},
  pages={116324},
  year={2020},
  publisher={Elsevier}
}

@article{tourbier2015efficient,
  title={An efficient total variation algorithm for super-resolution in fetal brain {MRI} with adaptive regularization},
  author={Tourbier, S{\'e}bastien and Bresson, Xavier and Hagmann, Patric and Thiran, Jean-Philippe and Meuli, Reto and Cuadra, Meritxell Bach},
  journal={NeuroImage},
  volume={118},
  pages={584--597},
  year={2015},
  publisher={Elsevier}
}

@article{gholipour2014fetal,
  title={Fetal {MRI}: a technical update with educational aspirations},
  author={Gholipour, Ali and Estroff, Judith A and Barnewolt, Carol E and Robertson, Richard L and Grant, P Ellen and Gagoski, Borjan and Warfield, Simon K and Afacan, Onur and Connolly, Susan A and Neil, Jeffrey J and others},
  journal={Concepts in Magnetic Resonance Part A},
  volume={43},
  number={6},
  pages={237--266},
  year={2014},
  publisher={Wiley Online Library}
}

@article{saleem2014fetal,
  title={Fetal {MRI}: An approach to practice: A review},
  author={Saleem, Sahar N},
  journal={Journal of advanced research},
  volume={5},
  number={5},
  pages={507--523},
  year={2014},
  publisher={Elsevier}
}

@article{Xu2023,
  title={{NeSVoR}: implicit neural representation for slice-to-volume reconstruction in {MRI}},
  author={Xu, Junshen and Moyer, Daniel and Gagoski, Borjan and Iglesias, Juan Eugenio and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar},
  journal={IEEE transactions on medical imaging},
  volume={42},
  number={6},
  pages={1707--1719},
  year={2023},
  publisher={IEEE}
}

@inproceedings{Wu2021,
  title={{IREM}: High-resolution magnetic resonance image reconstruction via implicit neural representation},
  author={Wu, Qing and Li, Yuwei and Xu, Lan and Feng, Ruiming and Wei, Hongjiang and Yang, Qing and Yu, Boliang and Liu, Xiaozhao and Yu, Jingyi and Zhang, Yuyao},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part VI 24},
  pages={65--74},
  year={2021},
  organization={Springer}
}

@article{Gholipour2010,
  title={Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain {MRI}},
  author={Gholipour, Ali and Estroff, Judy A and Warfield, Simon K},
  journal={IEEE transactions on medical imaging},
  volume={29},
  number={10},
  pages={1739--1758},
  year={2010},
  publisher={IEEE}
}

@article{Jiang2007,
  title={{MRI} of moving subjects using multislice snapshot images with volume reconstruction ({SVR}): application to fetal, neonatal, and adult brain studies},
  author={Jiang, Shuzhou and Xue, Hui and Glover, Alan and Rutherford, Mary and Rueckert, Daniel and Hajnal, Joseph V},
  journal={IEEE transactions on medical imaging},
  volume={26},
  number={7},
  pages={967--980},
  year={2007},
  publisher={IEEE}
}

@article{Kuklisova2012,
  title={Reconstruction of fetal brain {MRI} with intensity matching and complete outlier removal},
  author={Kuklisova-Murgasova, Maria and Quaghebeur, Gerardine and Rutherford, Mary A and Hajnal, Joseph V and Schnabel, Julia A},
  journal={Medical image analysis},
  volume={16},
  number={8},
  pages={1550--1564},
  year={2012},
  publisher={Elsevier}
}

@article{Rousseau2006,
  title={Registration-based approach for reconstruction of high-resolution in utero fetal {MR} brain images},
  author={Rousseau, Francois and Glenn, Orit A and Iordanova, Bistra and Rodriguez-Carranza, Claudia and Vigneron, Daniel B and Barkovich, James A and Studholme, Colin},
  journal={Academic radiology},
  volume={13},
  number={9},
  pages={1072--1081},
  year={2006},
  publisher={Elsevier}
}

@article{Sitzmann2020,
  title={Implicit neural representations with periodic activation functions},
  author={Sitzmann, Vincent and Martel, Julien and Bergman, Alexander and Lindell, David and Wetzstein, Gordon},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={7462--7473},
  year={2020}
}

@article{Mildenhall2021,
  title={Nerf: Representing scenes as neural radiance fields for view synthesis},
  author={Mildenhall, Ben and Srinivasan, Pratul P and Tancik, Matthew and Barron, Jonathan T and Ramamoorthi, Ravi and Ng, Ren},
  journal={Communications of the ACM},
  volume={65},
  number={1},
  pages={99--106},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@article{tancik2020fourfeat,
  title={Fourier features let networks learn high frequency functions in low dimensional domains},
  author={Tancik, Matthew and Srinivasan, Pratul and Mildenhall, Ben and Fridovich-Keil, Sara and Raghavan, Nithin and Singhal, Utkarsh and Ramamoorthi, Ravi and Barron, Jonathan and Ng, Ren},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={7537--7547},
  year={2020}
}

@InProceedings{tancik2020meta,
  title={Learned initializations for optimizing coordinate-based neural representations},
  author={Tancik, Matthew and Mildenhall, Ben and Wang, Terrance and Schmidt, Divi and Srinivasan, Pratul P and Barron, Jonathan T and Ng, Ren},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2846--2855},
  year={2021}
}

@InProceedings{Yuce_2022_CVPR,
  author    = {Y\"uce, Gizem and Ortiz-Jim\'enez, Guillermo and Besbinar, Beril and Frossard, Pascal},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {A Structured Dictionary Perspective on Implicit Neural Representations},
  year      = {2022},
  month     = {June},
  pages     = {19228-19238},
}

@inproceedings{saragadam2023wire,
  title={Wire: Wavelet implicit neural representations},
  author={Saragadam, Vishwanath and LeJeune, Daniel and Tan, Jasper and Balakrishnan, Guha and Veeraraghavan, Ashok and Baraniuk, Richard G},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={18507--18516},
  year={2023}
}

@Article{mueller2022instant,
  author     = {Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
  journal    = {ACM Trans. Graph.},
  title      = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
  year       = {2022},
  month      = {jul},
  number     = {4},
  pages      = {102:1--102:15},
  volume     = {41},
  articleno  = {102},
  doi        = {10.1145/3528223.3530127},
  issue_date = {July 2022},
  numpages   = {15},
  publisher  = {ACM},
}

@article{NEURIPS2020_MetaSDF,
  title={Metasdf: Meta-learning signed distance functions},
  author={Sitzmann, Vincent and Chan, Eric and Tucker, Richard and Snavely, Noah and Wetzstein, Gordon},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={10136--10147},
  year={2020}
}

@Article{nichol2018first,
  author  = {Nichol, Alex and Achiam, Joshua and Schulman, John},
  journal = {arXiv preprint arXiv:1803.02999},
  title   = {On first-order meta-learning algorithms},
  year    = {2018},
}

@InProceedings{finn2017MAML,
  author       = {Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  booktitle    = {International conference on machine learning},
  title        = {Model-agnostic meta-learning for fast adaptation of deep networks},
  year         = {2017},
  organization = {PMLR},
  pages        = {1126--1135},
}

@Article{perez-garcia_torchio_2021,
  author  = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, S{\'e}bastien},
  journal = {Computer Methods and Programs in Biomedicine},
  title   = {{TorchIO}: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
  year    = {2021},
  issn    = {0169-2607},
  pages   = {106236},
  doi     = {https://doi.org/10.1016/j.cmpb.2021.106236},
}

@Article{isensee2021nnu,
  author    = {Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal   = {Nature methods},
  title     = {{nnU-Net}: a self-configuring method for deep learning-based biomedical image segmentation},
  year      = {2021},
  number    = {2},
  pages     = {203--211},
  volume    = {18},
  publisher = {Nature Publishing Group},
}

@inproceedings{svort,
  title={{SVoRT}: Iterative transformer for slice-to-volume registration in fetal brain {MRI}},
  author={Xu, Junshen and Moyer, Daniel and Grant, P Ellen and Golland, Polina and Iglesias, Juan Eugenio and Adalsteinsson, Elfar},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={3--13},
  year={2022},
  organization={Springer}
}

@Article{SSIM,
  author  = {Zhou Wang and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P.},
  journal = {IEEE Transactions on Image Processing},
  title   = {Image quality assessment: from error visibility to structural similarity},
  year    = {2004},
  number  = {4},
  pages   = {600-612},
  volume  = {13},
  doi     = {10.1109/TIP.2003.819861},
}

@article{HCP_VANESSEN20122222,
  title={The Human Connectome Project: a data acquisition perspective},
  author={Van Essen, David C and Ugurbil, Kamil and Auerbach, Edward and Barch, Deanna and Behrens, Timothy EJ and Bucholz, Richard and Chang, Acer and Chen, Liyong and Corbetta, Maurizio and Curtiss, Sandra W and others},
  journal={Neuroimage},
  volume={62},
  number={4},
  pages={2222--2231},
  year={2012},
  publisher={Elsevier}
}

@article{HCP_preproc_GLASSER2013105,
  title={The minimal preprocessing pipelines for the Human Connectome Project},
  author={Glasser, Matthew F and Sotiropoulos, Stamatios N and Wilson, J Anthony and Coalson, Timothy S and Fischl, Bruce and Andersson, Jesper L and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R and others},
  journal={Neuroimage},
  volume={80},
  pages={105--124},
  year={2013},
  publisher={Elsevier}
}

@Article{FreeSurfer_FISCHL2012774,
  author   = {Bruce Fischl},
  journal  = {NeuroImage},
  title    = {FreeSurfer},
  year     = {2012},
  issn     = {1053-8119},
  note     = {20 YEARS OF f{MRI}},
  number   = {2},
  pages    = {774-781},
  volume   = {62},
  doi      = {https://doi.org/10.1016/j.neuroimage.2012.01.021},
}

@Article{prayer2004fetal,
  author    = {Prayer, Daniela and Brugger, Peter Christian and Prayer, Lucas},
  journal   = {Pediatric radiology},
  title     = {Fetal {MRI}: techniques and protocols},
  year      = {2004},
  pages     = {685--693},
  volume    = {34},
  publisher = {Springer},
}

@Article{NMI_STUDHOLME199971,
  author   = {C. Studholme and D.L.G. Hill and D.J. Hawkes},
  journal  = {Pattern Recognition},
  title    = {An overlap invariant entropy measure of 3D medical image alignment},
  year     = {1999},
  issn     = {0031-3203},
  number   = {1},
  pages    = {71-86},
  volume   = {32},
  doi      = {https://doi.org/10.1016/S0031-3203(98)00091-0},
}

@Article{Flirt_JENKINSON2002825,
  author   = {Mark Jenkinson and Peter Bannister and Michael Brady and Stephen Smith},
  journal  = {NeuroImage},
  title    = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
  year     = {2002},
  issn     = {1053-8119},
  number   = {2},
  pages    = {825-841},
  volume   = {17},
  doi      = {https://doi.org/10.1006/nimg.2002.1132},
}

@article{N4_Tustison,
  title={{N4ITK}: improved {N3} bias correction},
  author={Tustison, Nicholas J and Avants, Brian B and Cook, Philip A and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A and Gee, James C},
  journal={IEEE transactions on medical imaging},
  volume={29},
  number={6},
  pages={1310--1320},
  year={2010},
  publisher={IEEE}
}

@Misc{ranzini2021monaifbs,
  title={{MONAI}fbs: {MONAI}-based fetal brain {MRI} deep learning segmentation},
  author={Ranzini, Marta and Fidon, Lucas and Ourselin, S{\'e}bastien and Modat, Marc and Vercauteren, Tom},
  journal={arXiv preprint arXiv:2103.13314},
  year={2021}
}

@inproceedings{price2019dhcpACQ,
  title={The developing Human Connectome Project ({dHCP}): fetal acquisition protocol},
  author={Price, Anthony N and Cordero-Grande, Lucilio and Hughes, Emer and Hiscocks, Suzanne and Green, Elaine and McCabe, Laura and Hutter, Jana and Ferrazzi, Giulio and Deprez, Maria and Roberts, Thomas and others},
  booktitle={Proceedings of the annual meeting of the International Society of Magnetic Resonance in Medicine (ISMRM)},
  volume={244},
  year={2019}
}

@InProceedings{cordero2019dhcpRec,
  author    = {Cordero-Grande, L and Price, AN and Hughes, EJ and Wright, R and Rutherford, MA and Hajnal, Joseph V},
  booktitle = {ISMRM},
  title     = {Automating motion compensation in {3T} fetal brain imaging: localize, align and reconstruct},
  year      = {2019},
  pages     = {1000},
  volume    = {2019},
}

@InProceedings{cordero2019dhcpRec2,
  author    = {Cordero-Grande, L and Price, AN and Hughes, EJ and Wright, R and Rutherford, MA and Hajnal, Joseph V},
  booktitle = {ISMRM},
  title     = {Automating fetal brain reconstruction using distance regression learning},
  year      = {2019},
  volume    = {27th},
}

@Article{corderoautomating,
  author = {Cordero-Grande, Lucilio and Price, Anthony N and Hughes, Emer J and  Hajnal, Joseph V.},
  title  = {Automating fetal brain reconstruction using distance regression learning},
  year = {2019},
  journal = {International Society for Magnetic Resonance in Medicine},
}

@Article{kingma2014adam,
  author  = {Kingma, Diederik P and Ba, Jimmy},
  journal = {arXiv preprint arXiv:1412.6980},
  title   = {Adam: A method for stochastic optimization},
  year    = {2014},
}

@data{fetalAtlasGholipour2023,
author = {Gholipour, Ali and Velasco-Annis, Clemente and Rollins, Caitlin K. and Vasung, Lana and Ouaalam, Abdelhakim and Ortinau, Cynthia and Akhondi-Asl, Alireza and Clancy, Sean and Yang, Edward and Estroff, Judy and Warfield, Simon K.},
publisher = {Harvard Dataverse},
title = {{IMAGINE Fetal T2-weighted MRI Atlas}},
year = {2023},
version = {V1},
doi = {10.7910/DVN/WE9JVR},
url = {https://doi.org/10.7910/DVN/WE9JVR}
}

@inproceedings{chen20223equi,
  title={3d equivariant graph implicit functions},
  author={Chen, Yunlu and Fernando, Basura and Bilen, Hakan and Nie{\ss}ner, Matthias and Gavves, Efstratios},
  booktitle={European Conference on Computer Vision},
  pages={485--502},
  year={2022},
  organization={Springer}
}

@inproceedings{McGinnisMultiModal2023,
  title={Single-subject multi-contrast {MRI} super-resolution via implicit neural representations},
  author={McGinnis, Julian and Shit, Suprosanna and Li, Hongwei Bran and Sideri-Lampretsa, Vasiliki and Graf, Robert and Dannecker, Maik and Pan, Jiazhen and Stolt-Ans{\'o}, Nil and M{\"u}hlau, Mark and Kirschke, Jan S and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={173--183},
  year={2023},
  organization={Springer}
}

@InProceedings{Park_2019_CVPR,
title={Deepsdf: Learning continuous signed distance functions for shape representation},
author={Park, Jeong Joon and Florence, Peter and Straub, Julian and Newcombe, Richard and Lovegrove, Steven},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={165--174},
year={2019}
}

@inproceedings{sitzmann2019srns,
author = {Sitzmann, Vincent 
          and Zollh{\"o}fer, Michael
          and Wetzstein, Gordon},
title = {Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations},
booktitle = {Advances in Neural Information Processing Systems},
year={2019}
}


@article{kainz_2015,
  title={Fast volume reconstruction from motion corrupted stacks of 2D slices},
  author={Kainz, Bernhard and Steinberger, Markus and Wein, Wolfgang and Kuklisova-Murgasova, Maria and Malamateniou, Christina and Keraudren, Kevin and Torsney-Weir, Thomas and Rutherford, Mary and Aljabar, Paul and Hajnal, Joseph V and others},
  journal={IEEE transactions on medical imaging},
  volume={34},
  number={9},
  pages={1901--1913},
  year={2015},
  publisher={IEEE}
}

@article{hou20183,
  title={3-D reconstruction in canonical co-ordinate space from arbitrarily oriented 2-D images},
  author={Hou, Benjamin and Khanal, Bishesh and Alansary, Amir and McDonagh, Steven and Davidson, Alice and Rutherford, Mary and Hajnal, Jo V and Rueckert, Daniel and Glocker, Ben and Kainz, Bernhard},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={8},
  pages={1737--1750},
  year={2018},
  publisher={IEEE}
}
@inproceedings{hou2018computing,
  title={Computing CNN loss and gradients for pose estimation with Riemannian geometry},
  author={Hou, Benjamin and Miolane, Nina and Khanal, Bishesh and Lee, Matthew CH and Alansary, Amir and McDonagh, Steven and Hajnal, Jo V and Rueckert, Daniel and Glocker, Ben and Kainz, Bernhard},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I},
  pages={756--764},
  year={2018},
  organization={Springer}
}

@article{salehi2018real,
  title={Real-time deep pose estimation with geodesic loss for image-to-template rigid registration},
  author={Salehi, Seyed Sadegh Mohseni and Khan, Shadab and Erdogmus, Deniz and Gholipour, Ali},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={2},
  pages={470--481},
  year={2018},
  publisher={IEEE}
}

@inproceedings{pei2020anatomy,
  title={Anatomy-guided convolutional neural network for motion correction in fetal brain MRI},
  author={Pei, Yuchen and Wang, Lisheng and Zhao, Fenqiang and Zhong, Tao and Liao, Lufan and Shen, Dinggang and Li, Gang},
  booktitle={Machine Learning in Medical Imaging: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 11},
  pages={384--393},
  year={2020},
  organization={Springer}
}

@article{yeung2021learning,
  title={Learning to map 2D ultrasound images into 3D space with minimal human annotation},
  author={Yeung, Pak-Hei and Aliasi, Moska and Papageorghiou, Aris T and Haak, Monique and Xie, Weidi and Namburete, Ana IL},
  journal={Medical Image Analysis},
  volume={70},
  pages={101998},
  year={2021},
  publisher={Elsevier}
}
@inproceedings{young2024fully,
  title={Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI},
  author={Young, Sean I and Balbastre, Ya{\"e}l and Fischl, Bruce and Golland, Polina and Iglesias, Juan Eugenio},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11535--11545},
  year={2024}
}

@InProceedings{SVRTK_Reorient,
author="Uus, Alena U.
and Hall, Megan
and Payette, Kelly
and Hajnal, Joseph V.
and Deprez, Maria
and Rutherford, Mary A.
and Hutter, Jana
and Story, Lisa",
editor="Link-Sourani, Daphna
and Abaci Turk, Esra
and Macgowan, Christopher
and Hutter, Jana
and Melbourne, Andrew
and Licandro, Roxane",
title="Combined Quantitative T2* Map and Structural T2-Weighted Tissue-Specific Analysis for Fetal Brain MRI: Pilot Automated Pipeline",
booktitle="Perinatal, Preterm and Paediatric Image Analysis",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="28--38",
isbn="978-3-031-45544-5"
}

@article{singh2020deep,
  title={Deep predictive motion tracking in magnetic resonance imaging: application to fetal imaging},
  author={Singh, Ayush and Salehi, Seyed Sadegh Mohseni and Gholipour, Ali},
  journal={IEEE transactions on medical imaging},
  volume={39},
  number={11},
  pages={3523--3534},
  year={2020},
  publisher={IEEE}
}
@article{zhang2024motion,
  title={Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function},
  author={Zhang, Lei and Wang, Xiaoke and Rawson, Michael and Balan, Radu and Herskovits, Edward H and Melhem, Elias R and Chang, Linda and Wang, Ze and Ernst, Thomas},
  journal={Algorithms},
  volume={17},
  number={5},
  pages={215},
  year={2024},
  publisher={MDPI}
}
@ARTICLE{MetaLearningReview2022,
  author={Hospedales, Timothy and Antoniou, Antreas and Micaelli, Paul and Storkey, Amos},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Meta-Learning in Neural Networks: A Survey}, 
  year={2022},
  volume={44},
  number={9},
  pages={5149-5169},
  keywords={Task analysis;Optimization;Training;Machine learning algorithms;Predictive models;Neural networks;Deep learning;Meta-learning;learning-to-learn;few-shot learning;transfer learning;neural architecture search},
  doi={10.1109/TPAMI.2021.3079209}}

@ARTICLE{Cordero2023,
  author={Cordero-Grande, Lucilio and Ortuño-Fisac, Juan Enrique and del Hoyo, Alejandra Aguado and Uus, Alena and Deprez, Maria and Santos, Andres and Hajnal, Joseph V. and Ledesma-Carbayo, María J.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration}, 
  year={2023},
  volume={42},
  number={3},
  pages={810-822},
  keywords={Image reconstruction;Strain;Magnetic resonance imaging;Estimation;Brain modeling;Reconstruction algorithms;Extraterrestrial measurements;Fetal magnetic resonance imaging;slice to volume reconstruction;generative image priors;diffeomorphic image registration;gestational age prediction},
  doi={10.1109/TMI.2022.3217725}}

@ARTICLE{itksnap,
  author = {Paul A. Yushkevich and Joseph Piven and Cody Hazlett, Heather and
    Gimpel Smith, Rachel and Sean Ho and James C. Gee and Guido Gerig},
  title = {User-Guided {3D} Active Contour Segmentation of
    Anatomical Structures: Significantly Improved Efficiency and Reliability},
  journal = {Neuroimage},
  year = {2006},
  volume = {31},
  number = {3},
  pages = {1116--1128},
}

@inproceedings{stolt2023nisf,
  title={Nisf: Neural implicit segmentation functions},
  author={Stolt-Ans{\'o}, Nil and McGinnis, Julian and Pan, Jiazhen and Hammernik, Kerstin and Rueckert, Daniel},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={734--744},
  year={2023},
  organization={Springer}
}
%Manually added ref

@article{kuklisova-murgasova_reconstruction_2012,
	title = {Reconstruction of fetal brain {MRI} with intensity matching and complete outlier removal},
	volume = {16},
	issn = {13618415},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841512000965},
	doi = {10.1016/j.media.2012.07.004},
	abstract = {We propose a method for the reconstruction of volumetric fetal MRI from 2D slices, comprising superresolution reconstruction of the volume interleaved with slice-to-volume registration to correct for the motion. The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identiﬁed misregistered or corrupted voxels and slices. The reconstruction method is applied to motion-corrupted data simulated from MRI of a preterm neonate, as well as 10 clinically acquired thick-slice fetal MRI scans and three scan-sequence optimized thin-slice fetal datasets. The proposed method produced high quality reconstruction results from all the datasets to which it was applied. Quantitative analysis performed on simulated and clinical data shows that both intensity matching and robust statistics result in statistically signiﬁcant improvement of super-resolution reconstruction. The proposed novel EM-based robust statistics also improves the reconstruction when compared to previously proposed Huber robust statistics. The best results are obtained when thin-slice data and the correct approximation of the point spread function is used. This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientiﬁc literature. Ó 2012 Elsevier B.V. All rights reserved.},
	language = {en},
	number = {8},
	urldate = {2022-10-05},
	journal = {Medical Image Analysis},
	author = {Kuklisova-Murgasova, Maria and Quaghebeur, Gerardine and Rutherford, Mary A. and Hajnal, Joseph V. and Schnabel, Julia A.},
	month = dec,
	year = {2012},
	keywords = {Reconstruction, Registration, Outlier removal},
	pages = {1550--1564},
	annote = {Open in Adobe, pb with figures displaying

},
	file = {Kuklisova-Murgasova et al. - 2012 - Reconstruction of fetal brain MRI with intensity m.pdf:C\:\\Users\\jiast\\Zotero\\storage\\TZI9DL9J\\Kuklisova-Murgasova et al. - 2012 - Reconstruction of fetal brain MRI with intensity m.pdf:application/pdf},
}

@article{muller_instant_2022,
	title = {Instant {Neural} {Graphics} {Primitives} with a {Multiresolution} {Hash} {Encoding}},
	volume = {41},
	issn = {0730-0301, 1557-7368},
	url = {http://arxiv.org/abs/2201.05989},
	doi = {10.1145/3528223.3530127},
	abstract = {Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations. We achieve a combined speedup of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of \$\{1920{\textbackslash}!{\textbackslash}times{\textbackslash}!1080\}\$.},
	language = {en},
	number = {4},
	urldate = {2023-07-13},
	journal = {ACM Transactions on Graphics},
	author = {Müller, Thomas and Evans, Alex and Schied, Christoph and Keller, Alexander},
	month = jul,
	year = {2022},
	note = {arXiv:2201.05989 [cs]},
	keywords = {Hash Encoding, Hashgrid},
	pages = {1--15},
	annote = {Comment: To appear in ACM Transactions on Graphics (SIGGRAPH 2022). 15 pages, 13 figures, 3 tables},
	file = {Müller et al. - 2022 - Instant Neural Graphics Primitives with a Multires.pdf:C\:\\Users\\jiast\\Zotero\\storage\\9TJ7BAFL\\Müller et al. - 2022 - Instant Neural Graphics Primitives with a Multires.pdf:application/pdf},
}


@article{schultz_extraction_1996,
	title = {Extraction of high-resolution frames from video sequences},
	volume = {5},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {1057-7149, 1941-0042},
	url = {https://ieeexplore.ieee.org/document/503915/},
	doi = {10.1109/83.503915},
	number = {6},
	urldate = {2025-10-23},
	journal = {IEEE Transactions on Image Processing},
	author = {Schultz, R.R. and Stevenson, R.L.},
	month = jun,
	year = {1996},
	pages = {996--1011},
	file = {PDF:C\:\\Users\\jiast\\Zotero\\storage\\4669ZX9E\\Schultz and Stevenson - 1996 - Extraction of high-resolution frames from video sequences.pdf:application/pdf},
}

@inproceedings{ohbuchi_incremental_1992,
	address = {Chapel Hill, NC},
	title = {Incremental volume reconstruction and rendering for 3-{D} ultrasound imaging},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1002278},
	doi = {10.1117/12.131087},
	abstract = {In this paper, we present approaches toward an interactive visualization of a real time input, applied to 3D visualizations of 2D ultrasound echography data. The first, 3 degrees-of-freedom (DOF) incremental system visualizes a 3D volume acquired as a stream of 2D slices with location and orientation with 3 DOF. As each slice arrives, the system reconstructs a regular 3D volume and renders it. Rendering is done by an incremental image-order ray-casting algorithm which stores and reuses the results of expensive resampling along the rays for speed. The second is our first experiment toward real-time 6 DOF acquisition and visualization. 2D slices with 6 DOF are reconstructed off-line, and visualized at an interactive rate using a parallel volume rendering code running on the graphics multicomputer Pixel-Planes 5.},
	language = {en},
	urldate = {2025-10-23},
	author = {Ohbuchi, Ryutarou and Chen, David and Fuchs, Henry},
	editor = {Robb, Richard A.},
	month = sep,
	year = {1992},
	pages = {312--323},
	file = {PDF:C\:\\Users\\jiast\\Zotero\\storage\\VCGM45WN\\Ohbuchi et al. - 1992 - Incremental volume reconstruction and rendering for 3-D ultrasound imaging.pdf:application/pdf},
}

@inproceedings{zhang_image-gs_2025,
	title = {Image-{GS}: {Content}-{Adaptive} {Image} {Representation} via {2D} {Gaussians}},
	shorttitle = {Image-{GS}},
	url = {http://arxiv.org/abs/2407.01866},
	doi = {10.1145/3721238.3730596},
	abstract = {Neural image representations have emerged as a promising approach for encoding and rendering visual data. Combined with learning-based workflows, they demonstrate impressive trade-offs between visual fidelity and memory footprint. Existing methods in this domain, however, often rely on fixed data structures that suboptimally allocate memory or compute-intensive implicit models, hindering their practicality for real-time graphics applications. Inspired by recent advancements in radiance field rendering, we introduce Image-GS, a content-adaptive image representation based on 2D Gaussians. Leveraging a custom differentiable renderer, Image-GS reconstructs images by adaptively allocating and progressively optimizing a group of anisotropic, colored 2D Gaussians. It achieves a favorable balance between visual fidelity and memory efficiency across a variety of stylized images frequently seen in graphics workflows, especially for those showing non-uniformly distributed features and in low-bitrate regimes. Moreover, it supports hardware-friendly rapid random access for real-time usage, requiring only 0.3K MACs to decode a pixel. Through error-guided progressive optimization, Image-GS naturally constructs a smooth level-of-detail hierarchy. We demonstrate its versatility with several applications, including texture compression, semantics-aware compression, and joint image compression and restoration.},
	urldate = {2025-11-27},
	booktitle = {Proceedings of the {Special} {Interest} {Group} on {Computer} {Graphics} and {Interactive} {Techniques} {Conference} {Conference} {Papers}},
	author = {Zhang, Yunxiang and Li, Bingxuan and Kuznetsov, Alexandr and Jindal, Akshay and Diolatzis, Stavros and Chen, Kenneth and Sochenov, Anton and Kaplanyan, Anton and Sun, Qi},
	month = aug,
	year = {2025},
	note = {arXiv:2407.01866 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics},
	pages = {1--11},
	annote = {Comment: ACM SIGGRAPH 2025 Conference Proceedings},
	file = {Full Text PDF:C\:\\Users\\jiast\\Zotero\\storage\\84FMQYN8\\Zhang et al. - 2025 - Image-GS Content-Adaptive Image Representation via 2D Gaussians.pdf:application/pdf;Snapshot:C\:\\Users\\jiast\\Zotero\\storage\\GAS8DKR7\\2407.html:text/html},
}

@misc{kerbl_3d_2023,
	title = {{3D} {Gaussian} {Splatting} for {Real}-{Time} {Radiance} {Field} {Rendering}},
	url = {http://arxiv.org/abs/2308.04079},
	doi = {10.48550/arXiv.2308.04079},
	abstract = {Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time ({\textgreater}= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.},
	urldate = {2025-11-27},
	publisher = {arXiv},
	author = {Kerbl, Bernhard and Kopanas, Georgios and Leimkühler, Thomas and Drettakis, George},
	month = aug,
	year = {2023},
	note = {arXiv:2308.04079 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics},
	annote = {Comment: https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/},
	file = {Preprint PDF:C\:\\Users\\jiast\\Zotero\\storage\\DCV9WGCW\\Kerbl et al. - 2023 - 3D Gaussian Splatting for Real-Time Radiance Field Rendering.pdf:application/pdf;Snapshot:C\:\\Users\\jiast\\Zotero\\storage\\I5727JNS\\2308.html:text/html},
}

@article{mildenhall_nerf_2022,
	title = {{NeRF}: representing scenes as neural radiance fields for view synthesis},
	volume = {65},
	issn = {0001-0782, 1557-7317},
	shorttitle = {{NeRF}},
	url = {https://dl.acm.org/doi/10.1145/3503250},
	doi = {10.1145/3503250},
	abstract = {We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (
              x
              ,
              y
              ,
              z
              ) and viewing direction (
              θ, ϕ
              )) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis.},
	language = {en},
	number = {1},
	urldate = {2025-11-27},
	journal = {Communications of the ACM},
	author = {Mildenhall, Ben and Srinivasan, Pratul P. and Tancik, Matthew and Barron, Jonathan T. and Ramamoorthi, Ravi and Ng, Ren},
	month = jan,
	year = {2022},
	pages = {99--106},
	file = {Full Text PDF:C\:\\Users\\jiast\\Zotero\\storage\\KW4MUJA7\\Mildenhall et al. - 2022 - NeRF representing scenes as neural radiance fields for view synthesis.pdf:application/pdf},
}


@misc{dannecker_meta-learning_2025,
	title = {Meta-learning {Slice}-to-{Volume} {Reconstruction} in {Fetal} {Brain} {MRI} using {Implicit} {Neural} {Representations}},
	url = {http://arxiv.org/abs/2505.09565},
	doi = {10.48550/arXiv.2505.09565},
	abstract = {High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjects, such as fetal brain Magnetic Resonance Imaging (MRI). Existing solutions struggle with image artifacts and severe subject motion or require slice pre-alignment to achieve satisfying reconstruction performance. We propose a novel SVR method to enable fast and accurate MRI reconstruction even in cases of severe image and motion corruption. Our approach performs motion correction, outlier handling, and super-resolution reconstruction with all operations being entirely based on implicit neural representations. The model can be initialized with task-specific priors through fully self-supervised meta-learning on either simulated or real-world data. In extensive experiments including over 480 reconstructions of simulated and clinical MRI brain data from different centers, we prove the utility of our method in cases of severe subject motion and image artifacts. Our results demonstrate improvements in reconstruction quality, especially in the presence of severe motion, compared to state-of-the-art methods, and up to 50\% reduction in reconstruction time.},
	urldate = {2025-09-02},
	publisher = {arXiv},
	author = {Dannecker, Maik and Sanchez, Thomas and Cuadra, Meritxell Bach and Turgut, Özgün and Price, Anthony N. and Cordero-Grande, Lucilio and Kyriakopoulou, Vanessa and Hajnal, Joseph V. and Rueckert, Daniel},
	month = may,
	year = {2025},
	note = {arXiv:2505.09565 [eess]},
	keywords = {Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: 10 pages, 6 figures},
	file = {Full Text PDF:C\:\\Users\\jiast\\Zotero\\storage\\92NGB2QE\\Dannecker et al. - 2025 - Meta-learning Slice-to-Volume Reconstruction in Fe.pdf:application/pdf;Snapshot:C\:\\Users\\jiast\\Zotero\\storage\\979RCTTK\\2505.html:text/html},
}

@article{rousseau_non-local_2010,
	title = {A non-local approach for image super-resolution using intermodality priors},
	volume = {14},
	issn = {1361-8423},
	doi = {10.1016/j.media.2010.04.005},
	abstract = {Image enhancement is of great importance in medical imaging where image resolution remains a crucial point in many image analysis algorithms. In this paper, we investigate brain hallucination (Rousseau, 2008), or generating a high-resolution brain image from an input low-resolution image, with the help of another high-resolution brain image. We propose an approach for image super-resolution by using anatomical intermodality priors from a reference image. Contrary to interpolation techniques, in order to be able to recover fine details in images, the reconstruction process is based on a physical model of image acquisition. Another contribution to this inverse problem is a new regularization approach that uses an example-based framework integrating non-local similarity constraints to handle in a better way repetitive structures and texture. The effectiveness of our approach is demonstrated by experiments on realistic Brainweb Magnetic Resonance images and on clinical images from ADNI, generating automatically high-quality brain images from low-resolution input.},
	language = {eng},
	number = {4},
	journal = {Medical Image Analysis},
	author = {Rousseau, François and {Alzheimer's Disease Neuroimaging Initiative} and {}},
	month = aug,
	year = {2010},
	pmid = {20580893},
	pmcid = {PMC2947386},
	keywords = {Humans, Magnetic Resonance Imaging, Brain, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique},
	pages = {594--605},
	file = {Accepted Version:C\:\\Users\\jiast\\Zotero\\storage\\38EZZXC2\\Rousseau and Alzheimer's Disease Neuroimaging Initiative - 2010 - A non-local approach for image super-resolution us.pdf:application/pdf},
}

@inproceedings{shoemake1985animating,
  title={Animating rotation with quaternion curves},
  author={Shoemake, Ken},
  booktitle={Proceedings of the 12th annual conference on Computer graphics and interactive techniques},
  pages={245--254},
  year={1985}
}
@article{johnson2019billion,
  title={Billion-scale similarity search with {GPUs}},
  author={Johnson, Jeff and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
  journal={IEEE Transactions on Big Data},
  volume={7},
  number={3},
  pages={535--547},
  year={2019},
  publisher={IEEE}
}

@article{mercier_intersection-based_2025,
	title = {Intersection-based slice motion estimation for fetal brain imaging},
	volume = {190},
	issn = {0010-4825},
	url = {https://www.sciencedirect.com/science/article/pii/S0010482525003567},
	doi = {10.1016/j.compbiomed.2025.110005},
	abstract = {Fetal MRI offers a broad spectrum of applications, including the investigation of fetal brain development and facilitation of early diagnosis. However, image quality is often compromised by motion artifacts arising from both maternal and fetal movement. To mitigate these artifacts, fetal MRI typically employs ultrafast acquisition sequences. This results in the acquisition of three (or more) orthogonal stacks along different spatial axes. Nonetheless, inter-slice motion can still occur. If left uncorrected, such motion can introduce artifacts in the reconstructed 3D volume. Existing motion-correction approaches often rely on a two-step iterative process involving registration followed by reconstruction. They tend to detect and remove a large number of misaligned slices, resulting in poor reconstruction quality. This paper proposes a novel reconstruction-independent method for motion correction. Our approach benefits from the intersection of orthogonal slices and estimates motion for each slice by minimizing the difference between the intensity profiles along their intersections. To address potential misalignments, we present an innovative machine learning-based classifier for identifying misaligned slices. The parameters of these slices are then corrected using a multistart optimization approach. Quantitative evaluation on simulated datasets demonstrates very low registration errors. Qualitative analysis on real data further highlights the effectiveness of our approach compared to state-of-the-art methods.},
	urldate = {2025-10-07},
	journal = {Computers in Biology and Medicine},
	author = {Mercier, Chloe and Faisan, Sylvain and Pron, Alexandre and Girard, Nadine and Auzias, Guillaume and Chonavel, Thierry and Rousseau, François},
	month = may,
	year = {2025},
	keywords = {Fetal brain, Magnetic resonance imaging, Registration},
	pages = {110005},
	file = {ScienceDirect Full Text PDF:C\:\\Users\\jiast\\Zotero\\storage\\W4BL493M\\Mercier et al. - 2025 - Intersection-based slice motion estimation for fetal brain imaging.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\jiast\\Zotero\\storage\\J3SRFF2C\\S0010482525003567.html:text/html},
}


@article{girard_mr_2003,
	title = {{MR} imaging of acquired fetal brain disorders.},
	volume = {19},
	issn = {0256-7040},
	doi = {10.1007/s00381-003-0761-x},
	number = {7-8},
	journal = {Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery},
	author = {Girard, Nadine and others},
	month = aug,
	year = {2003},
	pmid = {12827415},
	pages = {490--500},
}

@article{loshchilov2017decoupled,
  title={Decoupled weight decay regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  journal={arXiv preprint arXiv:1711.05101},
  year={2017}
}
@article {Mihailov2024,
   author = {Mihailov, Angeline and others},
   title = {Burst of gyrification in the human brain after birth},
   journal = {Communications Biology},
   volume = {8},
   number = {1},
   pages = {1-13},
   ISSN = {2399-3642},
   year = {2025},
   type = {Journal Article}
}

@InProceedings{Rousseau2005,
  title={A novel approach to high resolution fetal brain MR imaging},
  author={Rousseau, Fran{\c{c}}ois and Glenn, Orit and Iordanova, Bistra and Rodriguez-Carranza, Claudia and Vigneron, Daniel and Barkovich, James and Studholme, Colin},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={548--555},
  year={2005},
  organization={Springer},
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  isbn="978-3-540-32094-4"
}

@inproceedings{rousseau2010super,
  title={On super-resolution for fetal brain MRI},
  author={Rousseau, Fran{\c{c}}ois and Kim, Kio and Studholme, Colin and Koob, Meriam and Dietemann, J-L},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={355--362},
  year={2010},
  organization={Springer}
}

@inproceedings{gholipour2010maximum,
  title={Maximum a posteriori estimation of isotropic high-resolution volumetric MRI from orthogonal thick-slice scans},
  author={Gholipour, Ali and Estroff, Judy A and Sahin, Mustafa and Prabhu, Sanjay P and Warfield, Simon K},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={109--116},
  year={2010},
  organization={Springer}
}

@misc{peng_three-dimensional_2025,
	title = {Three-{Dimensional} {MRI} {Reconstruction} with {Gaussian} {Representations}: {Tackling} the {Undersampling} {Problem}},
	shorttitle = {Three-{Dimensional} {MRI} {Reconstruction} with {Gaussian} {Representations}},
	url = {http://arxiv.org/abs/2502.06510},
	doi = {10.48550/arXiv.2502.06510},
	abstract = {Three-Dimensional Gaussian Splatting (3DGS) has shown substantial promise in the field of computer vision, but remains unexplored in the field of magnetic resonance imaging (MRI). This study explores its potential for the reconstruction of isotropic resolution 3D MRI from undersampled k-space data. We introduce a novel framework termed 3D Gaussian MRI (3DGSMR), which employs 3D Gaussian distributions as an explicit representation for MR volumes. Experimental evaluations indicate that this method can effectively reconstruct voxelized MR images, achieving a quality on par with that of wellestablished 3D MRI reconstruction techniques found in the literature. Notably, the 3DGSMR scheme operates under a selfsupervised framework, obviating the need for extensive training datasets or prior model training. This approach introduces significant innovations to the domain, notably the adaptation of 3DGS to MRI reconstruction and the novel application of the existing 3DGS methodology to decompose MR signals, which are presented in a complex-valued format.},
	language = {en},
	urldate = {2025-12-09},
	publisher = {arXiv},
	author = {Peng, Tengya and Zha, Ruyi and Li, Zhen and Liu, Xiaofeng and Zou, Qing},
	month = aug,
	year = {2025},
	note = {arXiv:2502.06510 [eess]},
	keywords = {Electrical Engineering and Systems Science - Image and Video Processing},
	file = {PDF:C\:\\Users\\jiast\\Zotero\\storage\\C4XLERP5\\Peng et al. - 2025 - Three-Dimensional MRI Reconstruction with Gaussian Representations Tackling the Undersampling Probl.pdf:application/pdf},
}





@Comment{jabref-meta: databaseType:bibtex;}
