Implicit Neural Representation in Medical Imaging: A Comparative Survey <br> <span style="float: right">_{^{ICCV 2023 CVAMD Workshop}}</span>

:fire::fire: This is a collection of awesome articles about Implicit Neural Representation networks in medical imaging:fire::fire:

:loudspeaker: Our review paper published on arXiv: Implicit Neural Representation in Medical Imaging: A Comparative Survey :heart:

Citation

@inproceedings{molaei2023implicit,
  title={Implicit neural representation in medical imaging: A comparative survey},
  author={Molaei, Amirali and Aminimehr, Amirhossein and Tavakoli, Armin and Kazerouni, Amirhossein and Azad, Bobby and Azad, Reza and Merhof, Dorit},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2381--2391},
  year={2023}
}

Introduction

Implicitly representing image signals has gained popularity in recent years for a broad range of medical imaging applications. The most motivating reasons are the following:

• Memory efficiency: The amount of memory demanded to represent the signal is not restricted by the signal's resolution.
• Unlimited Resolution: They take values in the continuous domain, meaning they can generate values for coordinates in-between the pixel or voxel-wise grid
• Effective data usage: They can learn to handle reconstruction and synthesis tasks without high-cost external annotation.

Which all are significantly important for developing an automatic medical system.<br> With the aim of providing easier access for researchers, this repo contains a comprehensive paper list of Implicit Neural Representations in Medical Imaging, including papers, codes, and related websites.<br> We considered a sum of <u>86</u> research papers spanning from 2021 to 2023.

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papers

<img src="Figures/Taxonomy.png" alt="Taxonomy" width="816"> Here, we taxonomize studies that integrate implicit representations into building medical analysis models.<br>

<a name="return-to-list"></a>

• Image Reconstruction
  • Tomography and CT
  • MRI
  • Ultrasound
  • Dental and Maxillofacial
  • Miscellaneous
• Image Segmentation
  • Brain Structures and Lesions
  • Cardiac and Heart Structures
  • Retinal Blood Vessels
  • 3D Segmentation
  • Boundary Refinement
  • Patch Level Segmentation
• Image Registration
  • Deformable Registration
  • Diffeomorphic Registration
• Neural Rendering
  • Reconstruction from Limited or Sparse Views
    • Computed Tomography (CT)
    • Cone Beam Computed Tomography (CBCT)
    • Magnetic Resonance Imaging (MRI)
    • Digital Subtraction Angiography (DSA)
  • Surgical Scene Reconstruction
  • Dental and Oral Imaging
  • Pose Estimation
• Image Compression
• Image Synthesis

(Each section is ordered by the publication dates) <img src="Figures/Reconstruction.jpg" alt="reconstruction" width="1000" height="5"><br>

Image Reconstruction

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Tomography and CT

1. 📜 IntraTomo: Self-supervised Learning-based Tomography via Sinogram Synthesis and Prediction

   • 🗓️ Publication Date: 9th Feb. 2021
   • 📖 Proceedings: IEEE/CVF International Conference on Computer Vision, 2021
   • 🧑‍🔬 Authors: Guangming Zang, Ramzi Idoughi, Rui Li, Peter Wonka, Wolfgang Heidrich
   • 📄 PDF
   • 📌 Highlight: Uses coordinate-based neural representations for CT reconstructions, capturing details often overlooked by standard deep learning. It's self-supervised, using the scanned object's own projections as training data, and further refined with geometric techniques.

2. 📜 CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems

   • 🗓️ Publication Date: 9th Feb. 2021
   • 📖 Journal: IEEE Transactions on Computational Imaging, 2021
   • 🧑‍🔬 Authors: Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, Ulugbek S. Kamilov
   • 📄 PDF
   • 💻 GitHub
   • 📌 Highlight: Takes measurement coordinates, such as view angle θ and spatial location l in CT scans, as its input, then outputs the corresponding sensor responses for these coordinates, creating an implicit neural representation of the measurement field.

3. 📜 Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields

   • 🗓️ Publication Date: 23th Apr. 2021
   • 📖 Proceedings: IEEE/CVF International Conference on Computer Vision, 2021
   • 🧑‍🔬 Authors: Albert W. Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya
   • 📄 PDF
   • 📌 Highlight: Uses implicit neural representations (INRs) for 4D-CT reconstruction. Paired with a parametric motion field, they estimate evolving 3D objects. Using a differentiable Radon transform, reconstructions are synthesized and compared with x-ray data, improving reconstruciton quality without training data.

4. 📜 Neural Computed Tomography

   • 🗓️ Publication Date: 17th Jan. 2022
   • 📖 Preprint: arXiv, 2022
   • 🧑‍🔬 Authors: Kunal Gupta, Brendan Colvert, Francisco Contijoch
   • 📄 PDF
   • 💻 GitHub

5. 📜 Streak artifacts reduction algorithm using an implicit neural representation in sparse-view CT

   • 🗓️ Publication Date: 4th Apr. 2022
   • 📖 Conference: Medical Imaging 2022: Physics of Medical Imaging, 2022
   • 🧑‍🔬 Authors: Byeongjoon Kim, Hyunjung Shim, Jongduk Baek
   • 📄 PDF

6. 📜 Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography

   • 🗓️ Publication Date: 12th Sep. 2022
   • 📖 Journal: IEEE Transactions on Computational Imaging, 2023
   • 🧑‍🔬 Authors: Qing Wu, Ruimin Feng, Hongjiang Wei, Jingyi Yu, Yuyao Zhang
   • 📄 PDF
   • 💻 GitHub

7. 📜 OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

   • 🗓️ Publication Date: 23th Nov. 2022
   • 📖 Conference: CVPR, 2023
   • 🧑‍🔬 Authors: Haim Sawdayee, Amir Vaxman, Amit H. Bermano
   • 📄 PDF
   • 💻 GitHub

8. 📜 NeuRec: Incorporating Interpatient prior to Sparse-View Image Reconstruction for Neurorehabilitation

   • 🗓️ Publication Date: 21th Feb. 2022
   • 📖 Journal: BioMed Research International, 2022
   • 🧑‍🔬 Authors: Cong Liu, Qingbin Wang, Jing Zhang
   • 📄 PDF

9. 📜 MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images.

   • 🗓️ Publication Date: 25th Jun. 2023
   • 📖 Preprint: arXiv
   • 🧑‍🔬 Authors: Hong Wang, Minghao Zhou, Dong Wei, Yuexiang Li, Yefeng Zheng
   • 📄 PDF
   • 🖥️ GitHub

10. 📜 UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography

    • 🗓️ Publication Date: 3rd Jun. 2022
    • 📖 Authors: Francisca Vasconcelos, Bobby He, Nalini Singh, Yee Whye Teh
    • 📄 PDF
    • 💻 GitHub

11. 📜 Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction

    • 🗓️ Publication Date: 27th Jun. 2023
    • 📖 Preprint: arXiv
    • 🧑‍🔬 Authors: Qing Wu, Lixuan Chen, Ce Wang, Hongjiang Wei, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang
    • 📄 PDF

12. 📜 NAISR: A 3D Neural Additive Model for Interpretable Shape Representation

    • 🗓️ Publication Date: 16th Mar. 2023
    • 📖 Preprint: arXiv
    • 🧑‍🔬 Authors: Yining Jiao, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn
    • 📄 PDF
    • 💻 GitHub

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MRI

13. 📜 An Arbitrary Scale Super-Resolution Approach for 3-Dimensional Magnetic Resonance Image using Implicit Neural Representation

    • 🗓️ Publication Date: 29th Oct. 2021
    • 🧑‍🔬 Authors: Qing Wu, Yuwei Li, Yawen Sun, Yan Zhou, Hongjiang Wei, Jingyi Yu, Yuyao Zhang
    • 📄 PDF
    • 💻 GitHub

14. 📜 IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation

    • 🗓️ Publication Date: 29th Jun. 2021
    • 🧑‍🔬 Authors: Qing Wu, Yuwei Li, Lan Xu, Ruiming Feng, Hongjiang Wei, Qing Yang, Boliang Yu, Xiaozhao Liu, Jingyi Yu, Yuyao Zhang
    • 📄 PDF

15. 📜 MRI Super-Resolution using Implicit Neural Representation with Frequency Domain Enhancement

    • 🗓️ Publication Date: Aug. 2022
    • 🧑‍🔬 Authors: Shuangming Mao, Seiichiro Kamata
    • 📄 PDF

16. 📜 NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

    • 🗓️ Publication Date: IEEE TRANSACTIONS ON MEDICAL IMAGING, 2022
    • 🧑‍🔬 Authors: Junshen Xu, Daniel Moyer, Borjan Gagoski, Juan Eugenio Iglesias, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson
    • 📄 PDF
    • 💻 GitHub

17. 📜 Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction

    • 🗓️ Publication Date: 31th Dec. 2022
    • 🧑‍🔬 Authors: Jie Feng, Ruimin Feng, Qing Wu, Zhiyong Zhang, Yuyao Zhang, Hongjiang Wei
    • 📄 [PDF](Link to PDF)

18. 📜 Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging

    • 🗓️ Publication Date: 16th Dec. 2022
    • 📖 Conference: International Conference on Information Processing in Medical Imaging, 2023
    • 🧑‍🔬 Authors: Wenqi Huang, Hongwei Li, Jiazhen Pan, Gastao Cruz, Daniel Rueckert, Kerstin Hammernik
    • 📄 PDF

19. 📜 Continuous longitudinal fetus brain atlas construction via implicit neural representation

    • 🗓️ Publication Date: 14th Sep. 2022
    • 🧑‍🔬 Authors: Lixuan Chen, Jiangjie Wu, Qing Wu, Hongjiang Wei, Yuyao Zhang
    • 📄 PDF

20. 📜 Multi-contrast MRI Super-resolution via Implicit Neural Representations

    • 🗓️ Publication Date: 27th Mar. 2023
    • 📖 Conference: MICCAI, 2023
    • 🧑‍🔬 Authors: Julian McGinnis, Suprosanna Shit, Hongwei Bran Li, Vasiliki Sideri-Lampretsa, Robert Graf, Maik Dannecker, Jiazhen Pan, Nil Stolt Ansö, Mark Mühlau, Jan S. Kirschke, Daniel Rueckert, Benedikt Wiestler
    • 📄 PDF
    • 💻 GitHub

21. 📜 Streak artifacts reduction algorithm using an implicit neural representation in sparse-view CT.

    • 📅 Publication Date: 4th Apr., 2022

    • 📖 Journal: Medical Imaging 2022: Physics of Medical Imaging, 2022

    • 🧑‍🔬 Authors: Byeongjoon Kim, Hyunjung Shim, Jongduk Baek.

    • 📄 PDF

22. 📜 Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing

    • 🗓️ Publication Date: 23rd May. 2022
    • 🧑‍🔬 Authors: Xin Wang, Sheng Wang,