Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions.

Jiaju Cheng, Peng Zhang, Fei Liu, Jie Liu, Hui Hui, Jie Tian, Jianwen Luo

Abstract read

In one paragraph

Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jiaju ChengDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0001-9351-0429

Peng ZhangDepartment of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.

Fei LiuBeijing Advanced Information and Industrial Technology Research Institute, Beijing Information Science and Technology University, Beijing 100192, China.

Jie LiuDepartment of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.

Hui HuiCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.ORCID https://orcid.org/0000-0002-6732-4232

Jie TianCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.ORCID https://orcid.org/0000-0003-0498-0432

Jianwen LuoDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.

Identifiers

PMID36187270

DOI10.1364/BOE.466349

PMCPMC9484427

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.