# Biomedical Image Reconstruction: A Survey

arXiv: https://arxiv.org/abs/2301.11813 · HTML: https://mlsanity.com/paper/2301.11813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 84 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images](https://mlsanity.com/paper/2004.00871/) (2020)
- [Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI](https://mlsanity.com/paper/2007.14450/) (2020)
- [Deep Learning for Biomedical Image Reconstruction: A Survey](https://mlsanity.com/paper/2002.12351/) (2020)
- [Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data](https://mlsanity.com/paper/2007.14979/) (2020)
- [Complex Fully Convolutional Neural Networks for MR Image Reconstruction](https://mlsanity.com/paper/1807.03343/) (2018)
- [Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction](https://mlsanity.com/paper/1908.09262/) (2019)
- [Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator](https://mlsanity.com/paper/1909.06763/) (2019)
- [Approximate k-space models and Deep Learning for fast photoacoustic reconstruction](https://mlsanity.com/paper/1807.03191/) (2018)
- [Stain Style Transfer using Transitive Adversarial Networks](https://mlsanity.com/paper/1910.10330/) (2019)
- [Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging](https://mlsanity.com/paper/1807.07416/) (2018)
- [Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging](https://mlsanity.com/paper/1908.10842/) (2019)
- [Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02743/) (2021)
- [Rethinking the optimization process for self-supervised model-driven MRI reconstruction](https://mlsanity.com/paper/2203.09724/) (2022)
- [Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging](https://mlsanity.com/paper/1808.04447/) (2018)
- [Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation](https://mlsanity.com/paper/1908.09515/) (2019)
- [APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network](https://mlsanity.com/paper/1909.09006/) (2019)
- [A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution](https://mlsanity.com/paper/2105.06887/) (2021)
- [Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging](https://mlsanity.com/paper/2208.00464/) (2022)

## Abstract (arXiv)

Biomedical image reconstruction research has been developed for more than five decades, giving rise to various techniques such as central and filtered back projection. With the rise of deep learning technology, biomedical image reconstruction field has undergone a massive paradigm shift from analytical and iterative methods to deep learning methods To drive scientific discussion on advanced deep learning techniques for biomedical image reconstruction, a workshop focusing on deep biomedical image reconstruction, MLMIR, is introduced and is being held yearly since 2018. This survey paper is aimed to provide basic knowledge in biomedical image reconstruction and the current research trend in biomedical image reconstruction based on the publications in MLMIR. This survey paper is intended for machine learning researchers to grasp a general understanding of the biomedical image reconstruction field and the current research trend in deep biomedical image reconstruction.