Evidence map›Paper›PMID 33162639›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2020

Prospective Prediction and Control of Image Properties in Model-based Material Decomposition for Spectral CT.

Wenying Wang, Matthew Tivnan, Grace J Gang, J Webster Stayman

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Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

4 authors.

Wenying WangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205.
Matthew TivnanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205.
Grace J GangDepartment 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

Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties such as spatial resolution, noise, and cross-basis response in the context of material decomposition are dependent on regularization, and high-dimensional exhaustive sweeping of regularization parameters is suboptimal. In this work, we proposed a set of prediction tools for generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, and noise correlation prospectively. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.

Identifiers

PMID33162639
PMCPMC7643888

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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.