# Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm

arXiv: https://arxiv.org/abs/2212.11844 · HTML: https://mlsanity.com/paper/2212.11844/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [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)
- [Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model](https://mlsanity.com/paper/2109.00768/) (2021)
- [Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance](https://mlsanity.com/paper/2109.00802/) (2021)
- [List-Mode PET Image Reconstruction Using Deep Image Prior](https://mlsanity.com/paper/2204.13404/) (2022)
- [Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement](https://mlsanity.com/paper/2107.13595/) (2021)
- [A Stochastic LBFGS Algorithm for Radio Interferometric Calibration](https://mlsanity.com/paper/1904.05619/) (2019)

## Built on by (most cited first, 6 of 6)

- [Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising](https://mlsanity.com/paper/2302.13546/) (2023)
- [PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction](https://mlsanity.com/paper/2605.08030/) (2026)
- [Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine](https://mlsanity.com/paper/2307.03298/) (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)
- [Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction](https://mlsanity.com/paper/2512.03962/) (2025)
- [Machine learning enables experimental access to photon-by-photon arrival times in scintillation detectors](https://mlsanity.com/paper/2605.27937/) (2026)

## Abstract (arXiv)

Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function. To implement a practical fully 3D PET image reconstruction, which could not be performed due to a graphics processing unit memory limitation, we modify the DIP optimization to block-iteration and sequentially learn an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term was added to the loss function to enhance the quantitative PET image accuracy. We evaluated our proposed method using Monte Carlo simulation with [18F]FDG PET data of a human brain and a preclinical study on monkey brain [18F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximum-a-posterior EM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that the proposed method improved the PET image quality by reducing statistical noise and preserved a contrast of brain structures and inserted tumor compared with other algorithms. In the preclinical experiment, finer structures and better contrast recovery were obtained by the proposed method. This indicated that the proposed method can produce high-quality images without a prior training dataset. Thus, the proposed method is a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.