Evidence map›Paper›PMID 35767986›Full record

ArticlePhysics in medicine and biology2022

Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels.

Rohan Nadkarni, Alex Allphin, Darin P Clark, Cristian T Badea

Open access · greenAbstract read
In one paragraph

Article in Physics in medicine and biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
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

10 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024
    Article
  6. Review
  7. Article
  8. Article
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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

4 authors at 1 institution in 1 country.

Rohan NadkarniQuantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC 27710, United States of America.
Alex AllphinQuantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC 27710, United States of America.
Darin P ClarkQuantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC 27710, United States of America.
Cristian T BadeaQuantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC 27710, United States of America.ORCID 0000-0002-1850-2522
Duke University · US

Funding

The Duke Preclinical Research Resources for Quantitative Imaging BiomarkersU24CA220245 · NCI · DUKE UNIVERSITY · PI BADEA, CRISTIAN T · 2017 to 2021
$2.8M
Development of a New Generation Micro-CT imaging for Functional and Molecular Imaging of CancerR01CA196667 · NCI · DUKE UNIVERSITY · PI BADEA, CRISTIAN T · 2015 to 2019
$2.3M
Cardiac photon counting CT and its application in studying interactions between Alzheimer's and heart diseaseRF1AG070149 · NIA · DUKE UNIVERSITY · PI BADEA, CRISTIAN T · 2021 to 2022
$2.1M
Multiorgan Photon Counting CT and Machine Learning to Elucidate Aging Mechanisms and InterventionsR01AG070149 · NIA · DUKE UNIVERSITY · PI CRISTIAN T BADEA, Alexandra Badea · 2024 to 2026
$2.1M
NCI NIH HHS R01 CA196667NCI NIH HHS U24 CA220245NIA NIH HHS R01 AG070149NIA NIH HHS RF1 AG070149
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

IodineTomography, X-Ray ComputedNeural Networks, ComputerPhantoms, ImagingPhotonsIodineCNNdeep learningmaterial decompositionphoton-counting CTtheranostics

Identifiers

PMID35767986
PMCPMC9969357
OpenAlexW4283721233

What OpenQuestion holds

Textmetadata
LicenceTDM
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.