Evidence map›Paper›PMID 33163986›Full record

ArticleConference proceedings. International Conference on Image Formation in X-Ray Computed Tomography2020

High-Resolution Model-based Material Decomposition for Multi-Layer Flat-Panel Detectors.

Yiqun Q Ma, Wenying Wang, Matt Tivnan, Junyuan Li, Minghui Lu, Jin Zhang, Josh Star-Lack, Richard E Colbeth, Wojciech Zbijewski, J Webster Stayman

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. 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

10 authors.

Yiqun Q MaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.
Wenying WangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.
Matt TivnanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.
Junyuan LiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.
Minghui LuVarex Imaging Corporation, 683 River Oaks Parkway, San Jose, CA 95134.
Jin ZhangVarex Imaging Corporation, 683 River Oaks Parkway, San Jose, CA 95134.
Josh Star-LackVarex Imaging Corporation, 683 River Oaks Parkway, San Jose, CA 95134.
Richard E ColbethVarex Imaging Corporation, 683 River Oaks Parkway, San Jose, CA 95134.
Wojciech ZbijewskiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.
J Webster StaymanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205.

Funding

BIOMEDICAL ENGINEERING TRAINING PROGRAMT32GM007057 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI KANOLD, PATRICK O, KARCHIN, RACHEL · 1985 to 2023
$10.4M
Monitoring of fractures with internal fixators using weight-bearing quantitative cone beam CTR01EB025470 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI STAYMAN, JOSEPH WEBSTER, ZBIJEWSKI, WOJCIECH BARTOSZ · 2018 to 2021
$1.5M
Spectral-spatial filtering for efficient multi-material decomposition in x-ray CTR21EB026849 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI STAYMAN, JOSEPH WEBSTER · 2018 to 2019
$450k
NIBIB NIH HHS R01 EB025470NIBIB NIH HHS R21 EB026849NIGMS NIH HHS T32 GM007057
6 · The paper itself

Abstract

In this work we compare a novel model-based material decomposition (MBMD) approach against a standard approach in high-resolution spectral CT using multi-layer flat-panel detectors. Physical experiments were conducted using a prototype dual-layer detector and a custom high-resolution iodine-enhanced line-pair phantom. Reconstructions were performed using three methods: traditional filtered back-projection (FBP) followed by image-domain decomposition, idealized MBMD with no blur modeling (iMBMD), and MBMD with system blur modeling (bMBMD). We find that both MBMD methods yielded higher resolution decompositions with lower noise than the FBP method, and that bMBMD further improves spatial resolution over iMBMD due to the additional blur modeling. These results demonstrate the advantages of MBMD in resolution performance and noise control over traditional methods for spectral CT. Model-based material decomposition hence has great potential in high-resolution spectral CT applications.

Indexed as

deblurred CTdual-layer FPDmodel-based iterative reconstructionone-step reconstruction and decompositionquantitative CTspectral CT

Identifiers

PMID33163986
PMCPMC7643886

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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.