# A Perspective on Deep Imaging

arXiv: https://arxiv.org/abs/1609.04375 · HTML: https://mlsanity.com/paper/1609.04375/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2016.

## Built on by (most cited first, 16 of 16)

- [Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data](https://mlsanity.com/paper/2201.09318/) (2022)
- [LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring](https://mlsanity.com/paper/2302.10630/) (2023)
- [DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging](https://mlsanity.com/paper/2311.04248/) (2023)
- [Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT](https://mlsanity.com/paper/2310.06949/) (2023)
- [An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2201.11440/) (2022)
- [Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT](https://mlsanity.com/paper/2303.12861/) (2023)
- [Low-dose CT Denoising with Language-engaged Dual-space Alignment](https://mlsanity.com/paper/2403.06128/) (2024)
- [Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup](https://mlsanity.com/paper/2209.15136/) (2022)
- [Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine](https://mlsanity.com/paper/2506.02149/) (2025)
- [LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models](https://mlsanity.com/paper/2507.06140/) (2025)
- [Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence](https://mlsanity.com/paper/2310.11641/) (2023)
- [Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Network](https://mlsanity.com/paper/2206.12344/) (2022)
- [Learned reconstruction methods for inverse problems: sample error estimates](https://mlsanity.com/paper/2312.14078/) (2023)
- [Hallucinations in medical devices](https://mlsanity.com/paper/2508.14118/) (2025)
- [InSituRes: A Physics-Informed Same-Grid Model for Enhanced Dynamic X-ray Micro-CT Reconstructions](https://mlsanity.com/paper/2508.17708/) (2025)
- [Deep learning Based Correction Algorithms for 3D Medical Reconstruction in Computed Tomography and Macroscopic Imaging](https://mlsanity.com/paper/2602.00220/) (2026)

## Abstract (arXiv)

The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction. The latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques. This direction might lead to intelligent utilization of domain knowledge from big data, innovative approaches for image reconstruction, and superior performance in clinical and preclinical applications. To realize the full impact of machine learning on medical imaging, major challenges must be addressed.