Evidence map›Paper›PMID 28584976›Full record

ArticleMolecular imaging and biology2018

Sparse Reconstruction of Fluorescence Molecular Tomography Using Variable Splitting and Alternating Direction Scheme.

Jinzuo Ye, Yang Du, Yu An, Yamin Mao, Shixin Jiang, Wenting Shang, Kunshan He, Xin Yang, Kun Wang, Chongwei Chi and 1 more

Abstract read
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Article in Molecular imaging and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Recent methodology advances in fluorescence molecular tomography.Visual computing for industry, biomedicine, and art · 2018
    Review
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

11 authors.

Jinzuo YeCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China.
Yang DuCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China.
Yu AnThe Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing, 100044, China.
Yamin MaoCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China.
Shixin JiangThe Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing, 100044, China.
Wenting ShangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China.
Kunshan HeCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China.
Xin YangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China.
Kun WangCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China. kun.wang@ia.ac.cn.
Chongwei ChiCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China. chongwei.chi@ia.ac.cn.
Jie TianCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China. tian@ieee.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeFluorescence molecular tomography (FMT) is a novel imaging modality for three-dimensional preclinical research and has many potential applications for drug therapy evaluation and tumor diagnosis. However, FMT presents an ill-conditioned and ill-posed inverse problem, which is a challenge for its tomography reconstruction. Due to the importance of FMT reconstruction, it is valuable and necessary to develop further practical reconstruction methods for FMT. PROCEDURES: In this study, an efficient method using variable splitting strategy as well as alternating direction strategy (VSAD) was proposed for FMT reconstruction. In this method, the variable splitting strategy and the augmented Lagrangian function were first introduced to obtain an equivalent optimization formulation of the original problem. Then, the alternating direction scheme was used to solve the optimization problem and to accelerate its convergence. To examine the property of the VSAD method, three numerical simulation experiments (accuracy assessment experiment, robustness assessment experiment, and reconstruction speed assessment experiment) were performed and analyzed.

resultsThe results indicated that the reconstruction accuracy, the reconstruction robustness, and the reconstruction speed of FMT were satisfactory by using the proposed VSAD method. Two in vivo studies, which were conducted by using two nude mouse models, further confirmed the advantages of the proposed method.

conclusionsThe results indicated that the proposed VSAD algorithm is effective for FMT reconstruction. It was accurate, robust, and efficient for FMT imaging and was feasibly applied for in vivo FMT applications.

Indexed as

AlgorithmsImage Processing, Computer-AssistedTomographyAnimalsFluorescenceImaging, Three-DimensionalMice, NudeTomography, X-Ray ComputedAlternating direction schemeFluorescence molecular tomographyIn vivo imagingReconstructionVariable splitting

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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.