# Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior

arXiv: https://arxiv.org/abs/2106.10359 · HTML: https://mlsanity.com/paper/2106.10359/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 18 Jun 2021.

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

- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [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)
- [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, 13 of 13)

- [Early Stopping for Deep Image Prior](https://mlsanity.com/paper/2112.06074/) (2021)
- [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)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction](https://mlsanity.com/paper/2311.14473/) (2023)
- [Deep Kernel Representation for Image Reconstruction in PET](https://mlsanity.com/paper/2110.01174/) (2021)
- [Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction](https://mlsanity.com/paper/2201.01443/) (2022)
- [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)
- [Physics-driven Deep Learning for PET/MRI](https://mlsanity.com/paper/2206.06788/) (2022)
- [Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising](https://mlsanity.com/paper/2410.00184/) (2024)
- [A Novel Implicit Neural Representation for Volume Data](https://mlsanity.com/paper/2403.08566/) (2024)
- [Deep Phase Coded Image Prior](https://mlsanity.com/paper/2404.03906/) (2024)
- [UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization](https://mlsanity.com/paper/2508.17816/) (2025)

## Abstract (arXiv)

Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels can be acquired by extending the scanning time. However, this is not feasible for dynamic PET imaging, where the scanning time is already long enough. In this work, we proposed an unsupervised deep learning framework for direct parametric reconstruction from dynamic PET, which was tested on the Patlak model and the relative equilibrium Logan model. The patient's anatomical prior image, which is readily available from PET/CT or PET/MR scans, was supplied as the network input to provide a manifold constraint, and also utilized to construct a kernel layer to perform non-local feature denoising. The linear kinetic model was embedded in the network structure as a 1x1 convolution layer. The training objective function was based on the PET statistical model. Evaluations based on dynamic datasets of 18F-FDG and 11C-PiB tracers show that the proposed framework can outperform the traditional and the kernel method-based direct reconstruction methods.