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Paper

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks

by Independent / Community 001a939e1ac64f7280fb08ec7c3e43936ae23f34

Free2AITools Nexus Index

67.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 80

P: Popularity 56

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation position and therefore degrades the spatial resolution. A way to address this issue is depth-of-interaction (DOI) estimation. In this work we propose two machine learning-based algorithms, a dense and a convolutional neural network (NN), as well as a mul...

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Semantic Scholar 22 Citations

Paper Information Summary
Entity Passport
Registry ID	001a939e1ac64f7280fb08ec7c3e43936ae23f34
License	ArXiv
Provider	semantic_scholar

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Academic & Research Attribution

BibTeX

@misc{001a939e1ac64f7280fb08ec7c3e43936ae23f34,
  author = {Unknown},
  title = {Improving depth-of-interaction resolution in pixellated PET detectors using neural networks Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/001a939e1ac64f7280fb08ec7c3e43936ae23f34}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Improving depth-of-interaction resolution in pixellated PET detectors using neural networks [Paper]. Free2AITools. https://api.semanticscholar.org/001a939e1ac64f7280fb08ec7c3e43936ae23f34

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 80

Popularity (P) 56

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Improving depth-of-interaction resolution in pixellated PET detectors using neural networks: Authority (A:80), Popularity (P:56), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation position and therefore degrades the spatial resolution. A way to address this issue is depth-of-interaction (DOI) estimation. In this work we propose two machine learning-based algorithms, a dense and a convolutional neural network (NN), as well as a mul..."

❝ Cite Node

@article{Unknown2026Improving,
  title={Improving depth-of-interaction resolution in pixellated PET detectors using neural networks},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈22CitationsSemantic Scholar

🏛️80AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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📦Data Source: semantic_scholar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
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citations: 22

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