Objective. Dynamic positron emission tomography (PET) imaging, which can provide information on
dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer
treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited
counts received in individual frame, especially in ultra short frames. Recently, the unrolled modelbased
deep learning methods have shown inspiring results for low-count PET image reconstruction
with good interpretability. Nevertheless, the existing model-based deep learning methods mainly
focus on the spatial correlations while ignore the temporal domain. Approach. In this paper, inspired
by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network
(STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations
are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative
learning process of the network, which provides the physical constraints and enhances interpretability.
Main results. The experiments of both simulation data and real rat scan data have shown that the
proposed method can achieve substantial noise reduction in both temporal and spatial domains and
outperform the maximum likelihood expectation maximization, spatio-temporal kernel method,
LPD and FBPnet. Significance. Experimental results show STPDnet better reconstruction performance
in the low count situation, which makes the proposed method particularly suitable in whole-body
dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer
from high level of noise.
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This content will become publicly available on June 1, 2024
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
We propose a new method for count-based exploration in high-dimensional state spaces. Unlike
previous work which relies on density models, we show that counts can be derived by averaging samples from the Rademacher distribution (or coin flips). This insight is used to set up a simple supervised learning objective which, when optimized, yields a state’s visitation count. We show that our method is significantly more effective at deducing ground-truth visitation counts than previous work; when used as an exploration bonus for a model-free reinforcement learning algorithm, it outperforms existing approaches on most of 9 challenging exploration tasks, including the Atari game MONTEZUMA’S REVENGE.
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- NSF-PAR ID:
- 10467319
- Publisher / Repository:
- Proceedings of the 40th International Conference on Machine Learning
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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