# DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem

arXiv: https://arxiv.org/abs/1804.07851 · HTML: https://mlsanity.com/paper/1804.07851/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 30 of 40)

- [Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising](https://mlsanity.com/paper/2209.03300/) (2022)
- [Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction](https://mlsanity.com/paper/2308.10157/) (2023)
- [Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model](https://mlsanity.com/paper/2109.00768/) (2021)
- [Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior](https://mlsanity.com/paper/2106.10359/) (2021)
- [Multi-Grade Deep Learning](https://mlsanity.com/paper/2302.00150/) (2023)
- [Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance](https://mlsanity.com/paper/2109.00802/) (2021)
- [TSJNet: A Multi-modality Target and Semantic Awareness Joint-driven Image Fusion Network](https://mlsanity.com/paper/2402.01212/) (2024)
- [List-Mode PET Image Reconstruction Using Deep Image Prior](https://mlsanity.com/paper/2204.13404/) (2022)
- [Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction](https://mlsanity.com/paper/2412.04339/) (2024)
- [FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution](https://mlsanity.com/paper/2108.03920/) (2021)
- [Deep Kernel Representation for Image Reconstruction in PET](https://mlsanity.com/paper/2110.01174/) (2021)
- [Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm](https://mlsanity.com/paper/2212.11844/) (2022)
- [Supervise-assisted Multi-modality Fusion Diffusion Model for PET Restoration](https://mlsanity.com/paper/2602.11545/) (2026)
- [Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction](https://mlsanity.com/paper/2201.01443/) (2022)
- [Successive Affine Learning for Deep Neural Networks](https://mlsanity.com/paper/2305.07996/) (2023)
- [TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms](https://mlsanity.com/paper/2308.05365/) (2023)
- [Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization](https://mlsanity.com/paper/2412.18668/) (2024)
- [Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling](https://mlsanity.com/paper/2306.04664/) (2023)
- [PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network](https://mlsanity.com/paper/2310.15550/) (2023)
- [LegoPET: Hierarchical Feature Guided Conditional Diffusion for PET Image Reconstruction](https://mlsanity.com/paper/2411.16629/) (2024)
- [Survey of Deep Learning Methods for Inverse Problems](https://mlsanity.com/paper/2111.04731/) (2021)
- [FourierPET: Deep Fourier-based Unrolled Network for Low-count PET Reconstruction](https://mlsanity.com/paper/2601.11680/) (2026)
- [A Data-Driven Reconstruction Technique based on Newton's Method for Emission Tomography](https://mlsanity.com/paper/2110.11396/) (2021)
- [Unsupervised PET Reconstruction from a Bayesian Perspective](https://mlsanity.com/paper/2110.15568/) (2021)
- [Cutting-Splicing data augmentation: A novel technology for medical image segmentation](https://mlsanity.com/paper/2210.09099/) (2022)
- [Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators](https://mlsanity.com/paper/2211.01373/) (2022)
- [DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction](https://mlsanity.com/paper/2303.04661/) (2023)
- [STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction](https://mlsanity.com/paper/2303.04667/) (2023)
- [HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation](https://mlsanity.com/paper/2306.08783/) (2023)
- [Stretched sinograms for limited-angle tomographic reconstruction with neural networks](https://mlsanity.com/paper/2306.10201/) (2023)

## Abstract (arXiv)

Positron emission tomography (PET) is a cornerstone of modern radiology. The ability to detect cancer and metastases in whole body scans fundamentally changed cancer diagnosis and treatment. One of the main bottlenecks in the clinical application is the time it takes to reconstruct the anatomical image from the deluge of data in PET imaging. State-of-the art methods based on expectation maximization can take hours for a single patient and depend on manual fine-tuning. This results not only in financial burden for hospitals but more importantly leads to less efficient patient handling, evaluation, and ultimately diagnosis and treatment for patients. To overcome this problem we present a novel PET image reconstruction technique based on a deep convolutional encoder-decoder network, that takes PET sinogram data as input and directly outputs full PET images. Using realistic simulated data, we demonstrate that our network is able to reconstruct images >100 times faster, and with comparable image quality (in terms of root mean squared error) relative to conventional iterative reconstruction techniques.