ArticleBiomedical optics express2024
Self-supervised hybrid neural network to achieve quantitative bioluminescence tomography for cancer research.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- A Novel Bioluminescence Tomography System Compatible with CBCT-guided Small Animal Irradiators for High-Precision Preclinical Radiation Research.Radiation research · 2026Article
- High precision fluorescence tomography system for pre-clinical radiation research: system design and validation.Physics in medicine and biology · 2025Article
- Diffuse Optical Spectroscopy: Technology and Applications: introduction to the feature issue.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Bioluminescence tomography (BLT) improves upon commonly-used 2D bioluminescence imaging by reconstructing 3D distributions of bioluminescence activity within biological tissue, allowing tumor localization and volume estimation-critical for cancer therapy development. Conventional model-based BLT is computationally challenging due to the ill-posed nature of the problem and data noise. We introduce a self-supervised hybrid neural network (SHyNN) that integrates the strengths of both conventional model-based methods and machine learning (ML) techniques to address these challenges. The network structure and converging path of SHyNN are designed to mitigate the effects of ill-posedness for achieving accurate and robust solutions. Through simulated and in vivo data on different disease sites, it is demonstrated to outperform the conventional reconstruction approach, particularly under high noise, in tumor localization, volume estimation, and multi-tumor differentiation, highlighting the potential towards quantitative BLT for cancer research.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.