Evidence map›Paper›PMID 38300771›Full record

ArticleIEEE transactions on bio-medical engineering2024

Accurate Reconstruction of Multiple Basis Images Directly From Dual Energy CT Data.

Buxin Chen, Zheng Zhang, Dan Xia, Emil Y Sidky, Xiaochuan Pan

Abstract read
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

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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

2 citing papers in PubMed.

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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

5 authors.

Buxin Chen
Zheng Zhang
Dan Xia
Emil Y Sidky
Xiaochuan Pan

Funding

Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01R01EB026282 · NIBIB · UNIVERSITY OF CHICAGO · PI PAN, XIAOCHUAN, SIDKY, EMIL Y · 2018 to 2021
$2.1M
Algorithm-Enabled Auto-Calibrating Quantitative Dual-Energy CTR21CA263660 · NCI · UNIVERSITY OF CHICAGO · PI PAN, XIAOCHUAN · 2022 to 2023
$414k
NCI NIH HHS R21 CA263660NIBIB NIH HHS R01 EB026282
6 · The paper itself

Abstract

objectiveWe develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT.

methodsIn medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array.

resultsWe conduct experimental studies using simulated and real DE data in CT, and evaluate basis images and VMIs obtained in terms of visual inspection and quantitative metrics. The study results reveal that the algorithm developed can accurately and robustly reconstruct multiple ( 2) basis images directly from DE data.

conclusionsThe developed algorithm can yield accurate multiple ( 2) basis images, VMIs, and physical quantities of interest from DE data in CT. SIGNIFICANCE: The work may provide insights into the development of practical procedures for reconstructing multiple basis images, VMIs, and physical quantities from DE data in applications. The work can be extended to reconstruct multiple basis images in multi-spectral or photon-counting CT.

Indexed as

AlgorithmsImage Processing, Computer-AssistedPhantoms, ImagingTomography, X-Ray ComputedHumansRadiography, Dual-Energy Scanned Projection

Identifiers

PMID38300771
PMCPMC11264342

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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.