# Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model

arXiv: https://arxiv.org/abs/2109.00768 · HTML: https://mlsanity.com/paper/2109.00768/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem](https://mlsanity.com/paper/1804.07851/) (2018)
- [Iterative PET Image Reconstruction Using Convolutional Neural Network Representation](https://mlsanity.com/paper/1710.03344/) (2017)
- [DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data](https://mlsanity.com/paper/1908.07516/) (2019)

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

- [Early Stopping for Deep Image Prior](https://mlsanity.com/paper/2112.06074/) (2021)
- [List-Mode PET Image Reconstruction Using Deep Image Prior](https://mlsanity.com/paper/2204.13404/) (2022)
- [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)
- [PET Image Reconstruction Using Deep Diffusion Image Prior](https://mlsanity.com/paper/2507.15078/) (2025)
- [Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising](https://mlsanity.com/paper/2302.13546/) (2023)
- [PIP: Positional-encoding Image Prior](https://mlsanity.com/paper/2211.14298/) (2022)
- [Implicit neural representations for end-to-end PET reconstruction](https://mlsanity.com/paper/2503.21825/) (2025)
- [DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction](https://mlsanity.com/paper/2303.04661/) (2023)
- [ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection](https://mlsanity.com/paper/2312.02494/) (2023)
- [List-Mode PET Image Reconstruction Using Dykstra-Like Splitting](https://mlsanity.com/paper/2403.00394/) (2024)
- [Deep Phase Coded Image Prior](https://mlsanity.com/paper/2404.03906/) (2024)
- [Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior](https://mlsanity.com/paper/2407.01926/) (2024)
- [Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction](https://mlsanity.com/paper/2409.06198/) (2024)
- [Exploiting network optimization stability for enhanced PET image denoising using deep image prior](https://mlsanity.com/paper/2502.11259/) (2025)
- [Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging](https://mlsanity.com/paper/2508.14364/) (2025)
- [Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning](https://mlsanity.com/paper/2604.16925/) (2026)
- [Machine learning enables experimental access to photon-by-photon arrival times in scintillation detectors](https://mlsanity.com/paper/2605.27937/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction. In particular, CNN-based direct PET image reconstruction, which directly generates the reconstructed image from the sinogram, has potential applicability to PET image enhancements because it does not require image reconstruction algorithms, which often produce some artifacts. However, these deep learning-based, direct PET image reconstruction algorithms have the disadvantage that they require a large number of high-quality training datasets. In this study, we propose an unsupervised direct PET image reconstruction method that incorporates a deep image prior framework. Our proposed method incorporates a forward projection model with a loss function to achieve unsupervised direct PET image reconstruction from sinograms. To compare our proposed direct reconstruction method with the filtered back projection (FBP) and maximum likelihood expectation maximization (ML-EM) algorithms, we evaluated using Monte Carlo simulation data of brain [18F]FDG PET scans. The results demonstrate that our proposed direct reconstruction quantitatively and qualitatively outperforms the FBP and ML-EM algorithms with respect to peak signal-to-noise ratio and structural similarity index.