Bayesian Regulators to Promote Sharp Image Edges in Limited-Angle Tomography by Combining

Gengsheng L Zeng

Abstract read

In one paragraph

Article in Journal of biotechnology and its applications, 2026. 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

1 author.

Gengsheng L ZengDepartment of Computer Science, Utah Valley University, Orem, USA.

Funding

Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction TechniquesR15EB024283 · NIBIB · WEBER STATE UNIVERSITY · PI ZENG, GENGSHENG · 2018 to 2022

$644k

NIBIB NIH HHS R15 EB024283

6 · The paper itself

Abstract

When the image reconstruction problem is severely ill-conditioned, for example, when the scanning angle is small, the analytic reconstruction algorithms produce images with too many artifacts to be useful in practice, while iterative Bayesian reconstruction algorithms can produce better images. The main purpose of Bayesian constraints is to regulate and stabilize the algorithm. Bayesian penalty minimization is a popular and effective method in regulating, denoising, and edge preserving. The main players for the Bayesian regulators are the total-variation (TV), Huber,

Indexed as

Bayesian objective functionEdge preservationL0L1L2Lq optimizationTotal variation

Identifiers

PMID42466200

PMCPMC13374723

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.