@inproceedings{ResNet,
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Deep Residual Learning for Image Recognition}, 
  year={2016},
  pages={770-778},
  doi={10.1109/CVPR.2016.90},
  url={https://ieeexplore.ieee.org/document/7780459}
}

@article{SynthSR,
title = {Joint super-resolution and synthesis of 1mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast},
journal = {NeuroImage},
volume = {237},
pages = {118206},
year = {2021},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2021.118206},
url = {https://www.sciencedirect.com/science/article/pii/S1053811921004833},
author = {Juan Eugenio Iglesias and Benjamin Billot and Yaël Balbastre and Azadeh Tabari and John Conklin and R. {Gilberto González} and Daniel C. Alexander and Polina Golland and Brian L. Edlow and Bruce Fischl},
keywords = {Super-resolution, Clinical scans, Convolutional neural network, Public software}
}

@article{SynthSRulf,
author = {Iglesias, Juan Eugenio and Schleicher, Riana and Laguna, Sonia and Billot, Benjamin and Schaefer, Pamela and McKaig, Brenna and Goldstein, Joshua N. and Sheth, Kevin N. and Rosen, Matthew S. and Kimberly, W. Taylor},
title = {Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine Learning},
journal = {Radiology},
volume = {306},
number = {3},
pages = {e220522},
year = {2023},
doi = {10.1148/radiol.220522},
URL = { https://doi.org/10.1148/radiol.220522}
}

@article{LoHiResGAN,
title = "Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images",
author = "Islam, {Kh Tohidul} and Shenjun Zhong and Parisa Zakavi and Zhifeng Chen and Helen Kavnoudias and Shawna Farquharson and Gail Durbridge and Markus Barth and McMahon, {Katie L.} and Parizel, {Paul M.} and Andrew Dwyer and Egan, {Gary F.} and Meng Law and Zhaolin Chen",
year = "2023",
doi = "10.1038/s41598-023-48438-1",
volume = "13",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}

@inproceedings{CycleGAN,
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
  booktitle={2017 IEEE International Conference on Computer Vision (ICCV)}, 
  title={Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks}, 
  year={2017},
  pages={2242-2251},
  keywords={Training;Painting;Training data;Semantics;Extraterrestrial measurements;Graphics},
  doi={10.1109/ICCV.2017.244}
}

@inproceedings{Pix2Pix,
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A.},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Image-to-Image Translation with Conditional Adversarial Networks}, 
  year={2017},
  volume={},
  number={},
  pages={5967-5976},
  keywords={Gallium nitride;Generators;Training;Image edge detection;Force;Image resolution},
  doi={10.1109/CVPR.2017.632}
}

@inproceedings{pathakCVPR16context,
    Author = {Pathak, Deepak and Kr\"ahenb\"uhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei},
    Title = {Context Encoders: Feature Learning by Inpainting},
    Booktitle = {Computer Vision and Pattern Recognition ({CVPR})},
    Year = {2016}
}

@article{ResViT,
  author={Dalmaz, Onat and Yurt, Mahmut and Çukur, Tolga},
  journal={IEEE Transactions on Medical Imaging}, 
  title={ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis}, 
  year={2022},
  volume={41},
  number={10},
  pages={2598-2614},
  doi={10.1109/TMI.2022.3167808}
}

@article{I2I-Mamba,
  title={I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling}, 
  author={Omer F. Atli and Bilal Kabas and Fuat Arslan and Mahmut Yurt and Onat Dalmaz and Tolga Çukur},
  year={2024},
  journal={arXiv:2405.14022}
}

@inproceedings{MLPmixer,
title={{MLP}-Mixer: An all-{MLP} Architecture for Vision},
author={Ilya Tolstikhin and Neil Houlsby and Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Thomas Unterthiner and Jessica Yung and Andreas Peter Steiner and Daniel Keysers and Jakob Uszkoreit and Mario Lucic and Alexey Dosovitskiy},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=EI2KOXKdnP}
}

@inproceedings{s4,
  title={Efficiently Modeling Long Sequences with Structured State Spaces},
  author={Gu, Albert and Goel, Karan and R\'e, Christopher},
  booktitle={The International Conference on Learning Representations ({ICLR})},
  year={2022}
}

@article{mamba,
  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
  author={Gu, Albert and Dao, Tri},
  journal={arXiv preprint arXiv:2312.00752},
  year={2023}
}

@misc{mambasurvey,
      title={Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis}, 
      author={Moein Heidari and Sina Ghorbani Kolahi and Sanaz Karimijafarbigloo and Bobby Azad and Afshin Bozorgpour and Soheila Hatami and Reza Azad and Ali Diba and Ulas Bagci and Dorit Merhof and Ilker Hacihaliloglu},
      year={2024},
      eprint={2406.03430},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2406.03430}, 
}

@inproceedings{SwinUnetrV2,
  author={Yufan He and Vishwesh Nath and Dong Yang and Yucheng Tang and Andriy Myronenko and Daguang Xu},
  title={SwinUNETR-V2: Stronger Swin Transformers with Stagewise Convolutions for 3D Medical Image Segmentation},
  year={2023},
  cdate={1672531200000},
  pages={416-426},
  url={https://doi.org/10.1007/978-3-031-43901-8_40},
  booktitle={MICCAI (4)},
}

@article{U-Mamba,
    title={U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation},
    author={Ma, Jun and Li, Feifei and Wang, Bo},
    journal={arXiv preprint arXiv:2401.04722},
    year={2024}
}

@article{MambaScanning,
title = {Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral image classification},
journal = {Neurocomputing},
volume = {613},
pages = {128751},
year = {2025},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2024.128751},
url = {https://www.sciencedirect.com/science/article/pii/S0925231224015224},
author = {Weilian Zhou and Sei-ichiro Kamata and Haipeng Wang and Man Sing Wong and Huiying (Cynthia) Hou},
keywords = {Hyperspectral image classification, Remote sensing, Deep learning, Computer vision, State space model, Mamba},
}

@article{VoxelMamba,
  title={Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection},
  author={Zhang, Guowen and Fan, Lue and He, Chenhang and Lei, Zhen and Zhang, Zhaoxiang and Zhang, Lei},
  journal={arXiv preprint arXiv:2406.10700},
  year={2024}
}

@InProceedings{SFNet,
        author = { Tapp, Austin and Zhao, Can and Roth, Holger R. and Tanedo, Jeffrey and Anwar, Syed Muhammad and Bourke, Niall J. and Hajnal, Joseph V. and Nankabirwa, Victoria and Deoni, Sean and Lepore, Natasha and Linguraru, Marius George},
        title = { { Super-Field MRI Synthesis for Infant Brains Enhanced by Dual Channel Latent Diffusion } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15003},
        month = {October},
        page = {444 -- 454}
}

@article{CNNreview,
title = {Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020},
journal = {European Journal of Radiology},
volume = {146},
pages = {110069},
year = {2022},
issn = {0720-048X},
doi = {https://doi.org/10.1016/j.ejrad.2021.110069},
url = {https://www.sciencedirect.com/science/article/pii/S0720048X21005507},
author = {Lu Wang and Hairui Wang and Yingna Huang and Baihui Yan and Zhihui Chang and Zhaoyu Liu and Mingfang Zhao and Lei Cui and Jiangdian Song and Fan Li},
keywords = {Bibliometrics, Deep learning, Diagnostic imaging, Radiology},
}

@article{MedGAN,
title = {MedGAN: Medical image translation using GANs},
journal = {Computerized Medical Imaging and Graphics},
volume = {79},
pages = {101684},
year = {2020},
issn = {0895-6111},
doi = {https://doi.org/10.1016/j.compmedimag.2019.101684},
url = {https://www.sciencedirect.com/science/article/pii/S0895611119300990},
author = {Karim Armanious and Chenming Jiang and Marc Fischer and Thomas Küstner and Tobias Hepp and Konstantin Nikolaou and Sergios Gatidis and Bin Yang},
keywords = {Generative adversarial networks, Deep neural networks, Image translation, PET attenuation correction, MR motion correction},
}

@inproceedings{
CNNinductivebias,
title={Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks},
author={Zihao Wang and Lei Wu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=N0KwVdaaaJ}
}

@inproceedings{
ViT,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=YicbFdNTTy}
}

@article{https://doi.org/10.1002/jmri.28408,
author = {Arnold, Thomas Campbell and Freeman, Colbey W. and Litt, Brian and Stein, Joel M.},
title = {Low-field MRI: Clinical promise and challenges},
journal = {Journal of Magnetic Resonance Imaging},
volume = {57},
number = {1},
pages = {25-44},
keywords = {low-field MRI, portable MRI, point-of-care MRI, MRI accessibility, healthcare cost, clinical low-field MRI},
doi = {https://doi.org/10.1002/jmri.28408},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.28408},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.28408},
year = {2023}
}

@article{https://doi.org/10.1002/nbm.5022,
author = {Jalloul, Mohammad and Miranda-Schaeubinger, Monica and Noor, Abass M. and Stein, Joel M. and Amiruddin, Raisa and Derbew, Hermon Miliard and Mango, Victoria L. and Akinola, Adeyanju and Hart, Kelly and Weygand, Joseph and Pollack, Erica and Mohammed, Sharon and Scheel, John R. and Shell, Jessica and Dako, Farouk and Mhatre, Pradnya and Kulinski, Lauren and Otero, Hansel J. and Mollura, Daniel J.},
title = {MRI scarcity in low- and middle-income countries},
journal = {NMR in Biomedicine},
volume = {36},
number = {12},
pages = {e5022},
keywords = {low- and middle-income countries, MRI, MRI accessibility, MRI education, MRI safety, RAD-AID},
doi = {https://doi.org/10.1002/nbm.5022},
url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.5022},
eprint = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.5022},
year = {2023}
}

@article{Hilbert1891UeberDS,
  title={Ueber die stetige Abbildung einer Line auf ein Fl{\"a}chenst{\"u}ck},
  author={David R. Hilbert},
  journal={Mathematische Annalen},
  year={1891},
  volume={38},
  pages={459-460},
  url={https://api.semanticscholar.org/CorpusID:123643081}
}

@article{908985,
  author={Moon, B. and Jagadish, H.V. and Faloutsos, C. and Saltz, J.H.},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Analysis of the clustering properties of the Hilbert space-filling curve}, 
  year={2001},
  volume={13},
  number={1},
  pages={124-141},
  keywords={Hilbert space;Multidimensional systems;Image coding;Image databases;Geographic Information Systems;Moon;Shape;Time measurement;Fractals;Helium},
  doi={10.1109/69.908985}}

@misc{gilbert,
  author={Jakub Červený},  
  title = {Gilbert},
  howpublished = {\url{https://github.com/jakubcerveny/gilbert}},
  year = {2019},
  note = {Accessed: 2024-12-01}
}

@inproceedings{HilbertNet,
  author={Chen, Wanli and Zhu, Xinge and Chen, Guojin and Yu, Bei},
  booktitle={2022 European Conference on Computer Vision (ECCV)}, 
  title={Efficient point cloud analysis using hilbert curve}, 
  year={2022},
  pages={5428–5437},
  doi={https://doi.org/10.1007/978-3-031-20086-1_42}
}

@article{Khula,
author = { Zieff, MR and Miles, M and Mbale, E and Eastman, E and Ginnell, L and Williams, SCR and Jones, DK and Alexander, DC and Wijeratne, PA and Gabard-Durnam, LJ and Klepac-Ceraj, V and Bonham, KS and Pini, N and Sania, A and Lucchini, M and Deoni, S and Fifer, WP and Gladstone, M and Amso, D and Donald, KA},
title = {Characterizing developing executive functions in the first 1000 days in South Africa and Malawi: The Khula Study},
journal = {Wellcome Open Research},
volume = {9},
year = {2024},
number = {157},
doi = {10.12688/wellcomeopenres.19638.1}
}

@article {MiNE,
author = {Surani, Zoya and Parkar, Sadia and Afshan, Gul and Elahi, Kinza Naseem and Hoodbhoy, Zahra and Hilal, Kiran and Jafri, Sidra Kaleem},
title = {Maternal and environmental Impact assessment on Neurodevelopment in Early childhood years (MINE): a prospective cohort study protocol from a low, middle-income country},
volume = {13},
number = {7},
year = {2023},
doi = {10.1136/bmjopen-2022-070283},
publisher = {British Medical Journal Publishing Group},
issn = {2044-6055},
URL = {https://bmjopen.bmj.com/content/13/7/e070283},
eprint = {https://bmjopen.bmj.com/content/13/7/e070283.full.pdf},
journal = {BMJ Open}
}

@article{doi:10.1148/radiol.2019190452,
author = {Campbell-Washburn, Adrienne E. and Ramasawmy, Rajiv and Restivo, Matthew C. and Bhattacharya, Ipshita and Basar, Burcu and Herzka, Daniel A. and Hansen, Michael S. and Rogers, Toby and Bandettini, W. Patricia and McGuirt, Delaney R. and Mancini, Christine and Grodzki, David and Schneider, Rainer and Majeed, Waqas and Bhat, Himanshu and Xue, Hui and Moss, Joel and Malayeri, Ashkan A. and Jones, Elizabeth C. and Koretsky, Alan P. and Kellman, Peter and Chen, Marcus Y. and Lederman, Robert J. and Balaban, Robert S.},
title = {Opportunities in Interventional and Diagnostic Imaging by Using High-Performance Low-Field-Strength MRI},
journal = {Radiology},
volume = {293},
number = {2},
pages = {384-393},
year = {2019},
doi = {10.1148/radiol.2019190452},
note ={PMID: 31573398},
URL = {https://doi.org/10.1148/radiol.2019190452},
eprint = {https://doi.org/10.1148/radiol.2019190452}
}

@article{https://doi.org/10.1002/mrm.29509,
author = {Padormo, Francesco and Cawley, Paul and Dillon, Louise and Hughes, Emer and Almalbis, Jennifer and Robinson, Joanna and Maggioni, Alessandra and Botella, Miguel De La Fuente and Cromb, Dan and Price, Anthony and Arlinghaus, Lori and Pitts, John and Luo, Tianrui and Zhang, Dingtian and Deoni, Sean C. L. and Williams, Steve and Malik, Shaihan and O'Muircheartaigh, Jonathan and Counsell, Serena J. and Rutherford, Mary and Arichi, Tomoki and Edwards, A. David and Hajnal, Joseph V.},
title = {In vivo T mapping of neonatal brain tissue at 64 mT},
journal = {Magnetic Resonance in Medicine},
volume = {89},
number = {3},
pages = {1016-1025},
keywords = {gray matter, neonatal, relaxometry, ultralow-field MRI, white matter},
doi = {https://doi.org/10.1002/mrm.29509},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.29509},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29509},
year = {2023}
}

@article{ANTs,
title = {Advanced {Normalization} {Tools} ({ANTS})},
volume = {2},
url ={https://psychiatry.ucsd.edu/research/programs-centers/snl/_files/ants2.pdf},
abstract = {We provide examples and highlights of Advanced Normalization Tools (ANTs), versions 2.x, that address practical problems in real data.},
language = {en},
number = {365},
journal = {Insight j},
author = {Avants, Brian B and Tustison, Nick and Johnson, Hans},
pages = {1--35},
file = {Avants et al. - Advanced Normalization Tools (ANTS).pdf:/Users/frantisek/Zotero/storage/GUG8RHJ6/Avants et al. - Advanced Normalization Tools (ANTS).pdf:application/pdf},
year = {2009},
}

@article{FreeSurfer,
title = {FreeSurfer},
journal = {NeuroImage},
volume = {62},
number = {2},
pages = {774-781},
year = {2012},
note = {20 YEARS OF fMRI},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2012.01.021},
url = {https://www.sciencedirect.com/science/article/pii/S1053811912000389},
author = {Bruce Fischl},
}

@article{SynthSeg,
title = {SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining},
journal = {Medical Image Analysis},
volume = {86},
pages = {102789},
year = {2023},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.102789},
url = {https://www.sciencedirect.com/science/article/pii/S1361841523000506},
author = {Benjamin Billot and Douglas N. Greve and Oula Puonti and Axel Thielscher and Koen {Van Leemput} and Bruce Fischl and Adrian V. Dalca and Juan Eugenio Iglesias},
keywords = {Domain randomisation, Contrast and resolution invariance, Segmentation, CNN},
}

@article{Myelination,
author = {Dubois, Jessica and Alison, Marianne and Counsell, Serena J. and Hertz-Pannier, Lucie and Hüppi, Petra S. and Benders, Manon J.N.L.},
title = {MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances},
journal = {Journal of Magnetic Resonance Imaging},
volume = {53},
number = {5},
pages = {1318-1343},
keywords = {brain development, newborns, infants, anatomical MRI, diffusion MRI, quantitative MRI, functional MRI},
doi = {https://doi.org/10.1002/jmri.27192},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.27192},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.27192},
year = {2021}
}

@article{Myelination2,
  title={6-Month Infant Brain Mri Segmentation Guided by 24-Month Data Using Cycle-Consistent Adversarial Networks},
  author={Toan Duc Bui and Li Wang and Weili Lin and Gang Li and Dinggang Shen},
  journal={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  year={2020},
  pages={359-362},
  url={https://api.semanticscholar.org/CorpusID:218893283}
}

@misc{MONAI,
      title={MONAI: An open-source framework for deep learning in healthcare}, 
      author={M. Jorge Cardoso and Wenqi Li and Richard Brown and Nic Ma and Eric Kerfoot and Yiheng Wang and Benjamin Murrey and Andriy Myronenko and Can Zhao and Dong Yang and Vishwesh Nath and Yufan He and Ziyue Xu and Ali Hatamizadeh and Andriy Myronenko and Wentao Zhu and Yun Liu and Mingxin Zheng and Yucheng Tang and Isaac Yang and Michael Zephyr and Behrooz Hashemian and Sachidanand Alle and Mohammad Zalbagi Darestani and Charlie Budd and Marc Modat and Tom Vercauteren and Guotai Wang and Yiwen Li and Yipeng Hu and Yunguan Fu and Benjamin Gorman and Hans Johnson and Brad Genereaux and Barbaros S. Erdal and Vikash Gupta and Andres Diaz-Pinto and Andre Dourson and Lena Maier-Hein and Paul F. Jaeger and Michael Baumgartner and Jayashree Kalpathy-Cramer and Mona Flores and Justin Kirby and Lee A. D. Cooper and Holger R. Roth and Daguang Xu and David Bericat and Ralf Floca and S. Kevin Zhou and Haris Shuaib and Keyvan Farahani and Klaus H. Maier-Hein and Stephen Aylward and Prerna Dogra and Sebastien Ourselin and Andrew Feng},
      year={2022},
      eprint={2211.02701},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2211.02701}, 
}

@article{nnU-Net,
  author       = {Fabian Isensee and
                  Jens Petersen and
                  Andr{\'{e}} Klein and
                  David Zimmerer and
                  Paul F. Jaeger and
                  Simon Kohl and
                  Jakob Wasserthal and
                  Gregor K{\"{o}}hler and
                  Tobias Norajitra and
                  Sebastian J. Wirkert and
                  Klaus H. Maier{-}Hein},
  title        = {nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation},
  journal      = {CoRR},
  volume       = {abs/1809.10486},
  year         = {2018},
  url          = {http://arxiv.org/abs/1809.10486},
  eprinttype    = {arXiv},
  eprint       = {1809.10486},
  timestamp    = {Tue, 15 Jun 2021 14:13:20 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1809-10486.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{10.1145/16856.16886,
author = {Orenstein, Jack A.},
title = {Spatial query processing in an object-oriented database system},
year = {1986},
issue_date = {June 1986},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {15},
number = {2},
issn = {0163-5808},
url = {https://doi.org/10.1145/16856.16886},
doi = {10.1145/16856.16886},
abstract = {DBMSs must offer spatial query processing capabilities to meet the needs of applications such as cartography, geographic information processing and CAD. Many data structures and algorithms that process grid representations of spatial data have appeared in the literature. We unify much of this work by identifying common principles and distilling them into a small set of constructs. (Published data structures and algorithms can be derived as special cases.) We show how these constructs can be supported with only minor modifications to current DBMS implementations. The ideas are demonstrated in the context of the range query problem. Analytical and experimental evidence indicates that performance of the derived solution is very good (e.g., comparable to performance of the kd tree.)},
journal = {SIGMOD Rec.},
month = jun,
pages = {326–336},
numpages = {11}
}


@inproceedings{z-order,
author = {Orenstein, Jack A.},
title = {Spatial query processing in an object-oriented database system},
year = {1986},
isbn = {0897911911},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/16894.16886},
doi = {10.1145/16894.16886},
booktitle = {Proceedings of the 1986 ACM SIGMOD International Conference on Management of Data},
pages = {326–336},
numpages = {11},
location = {Washington, D.C., USA},
series = {SIGMOD '86}
}

@inproceedings{hilbertzorder,
author = {Warren, M. S. and Salmon, J. K.},
title = {A parallel hashed Oct-Tree N-body algorithm},
year = {1993},
isbn = {0818643404},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/169627.169640},
doi = {10.1145/169627.169640},
booktitle = {Proceedings of the 1993 ACM/IEEE Conference on Supercomputing},
pages = {12–21},
numpages = {10},
location = {Portland, Oregon, USA},
series = {Supercomputing '93}
}