# A Review on Deep Learning in Medical Image Reconstruction

arXiv: https://arxiv.org/abs/1906.10643 · HTML: https://mlsanity.com/paper/1906.10643/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [Diffeomorphic Image Registration with Neural Velocity Field](https://mlsanity.com/paper/2202.12498/) (2022)
- [Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation](https://mlsanity.com/paper/2103.05255/) (2021)
- [NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problems](https://mlsanity.com/paper/2203.00804/) (2022)
- [Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics](https://mlsanity.com/paper/2405.01822/) (2024)
- [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://mlsanity.com/paper/2105.01800/) (2021)
- [DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration](https://mlsanity.com/paper/2507.17892/) (2025)
- [Towards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport](https://mlsanity.com/paper/2505.17644/) (2025)
- [3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices](https://mlsanity.com/paper/2507.02411/) (2025)
- [3D Reconstruction via Incremental Structure From Motion](https://mlsanity.com/paper/2508.01019/) (2025)
- [Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction](https://mlsanity.com/paper/2601.15098/) (2026)
- [MORE: Multi-Organ Medical Image REconstruction Dataset](https://mlsanity.com/paper/2510.26759/) (2025)
- [Performance Evaluation of Transfer Learning Based Medical Image Classification Techniques for Disease Detection](https://mlsanity.com/paper/2512.04397/) (2025)
- [A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline](https://mlsanity.com/paper/2605.08282/) (2026)
- [Latent Anchor-Driven Test Generation for Deep Neural Networks](https://mlsanity.com/paper/2606.04310/) (2026)

## Abstract (arXiv)

Medical imaging is crucial in modern clinics to guide the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision, have been playing a prominent role. Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed, and we shall call these models handcrafted models. Later, handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs, while part of the model is learned from the observed data. More recently, as more data and computation resources are made available, deep learning based models (or deep models) pushed data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs. Both handcrafted and data-driven modeling have their own advantages and disadvantages. One of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches. The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint. This viewpoint stimulates new designs of neural network architectures with inspiration from optimization algorithms and numerical differential equations. Given the popularity of deep modeling, there are still vast remaining challenges in the field, as well as opportunities which we shall discuss at the end of this article.