Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise
  Reduction and Resolution Enhancement   High noise and low spatial resolution are two key confounding factors that
limit the qualitative and quantitative accuracy of PET images. AI models for
image denoising and deblurring are becoming increasingly popular for
post-reconstruction enhancement of PET images. We present here a detailed
review of recent efforts for AI-based PET image enhancement with a focus on
network architectures, data types, loss functions, and evaluation metrics. We
also highlight emerging areas in this field that are quickly gaining
popularity, identify barriers to large-scale adoption of AI models for PET
image enhancement, and discuss future directions.

34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn