SODL-IR-FISTA: sparse online dictionary learning with iterative reduction FISTA for cone-beam X-ray luminescence computed tomography.

Xin Cao, Wenlong Tang, Huimin Gao, Yifan Wang, Yi Chen, Chengyi Gao, Fengjun Zhao, Linzhi Su

Abstract read

In one paragraph

Article in Biomedical optics express, 2024. 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

8 authors.

Xin CaoSchool of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.ORCID https://orcid.org/0000-0003-3560-6523

Wenlong TangSchool of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.

Huimin GaoSchool of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.

Yifan WangSchool of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.

Yi ChenSchool of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide SA 5005, Australia.

Chengyi GaoDepartment of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.

Fengjun ZhaoSchool of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.

Linzhi SuSchool of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cone beam X-ray luminescence computed tomography (CB-XLCT) is an emerging imaging technique with potential for early 3D tumor detection. However, the reconstruction challenge due to low light absorption and high scattering in tissues makes it a difficult inverse problem. In this study, the online dictionary learning (ODL) method, combined with iterative reduction FISTA (IR-FISTA), has been utilized to achieve high-quality reconstruction. Our method integrates IR-FISTA for efficient and accurate sparse coding, followed by an online stochastic approximation for dictionary updates, effectively capturing the sparse features inherent to the problem. Additionally, a re-sparse step is introduced to enhance the sparsity of the solution, making it better suited for CB-XLCT reconstruction. Numerical simulations and in vivo experiments were conducted to assess the performance of the method. The SODL-IR-FISTA achieved the smallest location error of 0.325 mm in

Identifiers

PMID39296417

DOI10.1364/BOE.531828

PMCPMC11407256

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.