Sinogram Interpolation Inspired by Single-Image Super Resolution.

Carolyn Christiansen, Gengsheng L Zeng

Abstract read

In one paragraph

Article in Journal of biotechnology and its applications, 2023. 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

2 authors.

Carolyn ChristiansenDepartment of Computer Science, Utah Valley University, Orem, Utah, USA.

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

Funding

Trade-offs in human observer performance, image quality metrics, and patient doseR01EB017095 · NIBIB · MAYO CLINIC ROCHESTER · PI Hao Gong, Cynthia H McCollough · 2012 to 2026

$7.0M

Critical resources to evaluate CT scan techniques and dose reduction approachesU01EB017185 · NIBIB · MAYO CLINIC ROCHESTER · PI MCCOLLOUGH, CYNTHIA H · 2013 to 2016

$3.6M

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 R01 EB017095NIBIB NIH HHS R15 EB024283NIBIB NIH HHS U01 EB017185

6 · The paper itself

Abstract

Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object. This results in a fair amount of radiation exposure for the patient, which increases the risk of cancer. Less radiation and fewer views, however, leads to inferior image reconstruction. To solve this sparse-view problem, a deep-learning model is created that takes as input a sparse sinogram and outputs a sinogram with interpolated data for additional views. The architecture of this model is based on the super-resolution convolutional neural network. The reconstruction of model-interpolated sinograms has less mean-squared error than the reconstruction of the sparse sinogram. It also has less mean-squared error than a reconstruction of a sinogram interpolated using the popular bilinear image-resizing algorithm. This model can be easily adapted to different image sizes, and its simplicity translates into efficiency in both time and memory requirements.

Indexed as

Deep learningLimited data imagingMachine learningMedical imagingTomography

Identifiers

PMID37323429

PMCPMC10270693

What OpenQuestion holds

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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.