Evidence map›Paper›PMID 31997391›Full record

ArticleMedical physics2020

GAN and dual-input two-compartment model-based training of a neural network for robust quantification of contrast uptake rate in gadoxetic acid-enhanced MRI.

Josiah Simeth, Yue Cao

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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 at 1 institution in 1 country.

Josiah SimethDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Yue CaoDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
University of Michigan–Ann Arbor · US

Funding

TREATMENT VERIFICATION AND TREATMENT PLAN REFINEMENTP01CA059827 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KESSLER, MARC LEWIS, LAWRENCE, THEODORE S · 1993 to 2018
$37.9M
Predicting Liver Function After IrradiationR01CA132834 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CAO, YUE · 2010 to 2015
$1.7M
NCI NIH HHS P01 CA059827NCI NIH HHS R01 CA132834NIH HHS P01 CA059827NIH HHS R01 CA132834
6 · The paper itself

Abstract

purposeGadoxetic acid uptake rate (k

methodsThirty Liver HTR DCE MRI exams were acquired in 22 patients with at least 16 min of postcontrast data sampled at least every 13 s. A simple neural network (NN) with four hidden layers was trained on voxel-wise LTR data to predict k

resultsFor acquisition lengths of 16 min NRMSE (Normalized Root-Mean-Squared Error) in k

conclusionsThe study indicates that data length is significant for LSITC analysis as applied to DCE data for standard temporal sampling, and that machine learning methods, such as the implemented NN, have potential for much greater resilience to shortened acquisition time than directly fitting to the LSITC model.

Indexed as

Magnetic Resonance ImagingNeural Networks, ComputerBiological TransportContrast MediaGadolinium DTPAImage Processing, Computer-AssistedLeast-Squares AnalysisContrast MediaGadolinium DTPAgadolinium ethoxybenzyl DTPAGANliver functionquantitative imaging

Identifiers

PMID31997391
PMCPMC7337040
OpenAlexW3003735556

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.