Evidence map›Paper›PMID 31655943›Full record

ArticleEuropean radiology experimental2019

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images.

Kyle A Hasenstab, Guilherme Moura Cunha, Atsushi Higaki, Shintaro Ichikawa, Kang Wang, Timo Delgado, Ryan L Brunsing, Alexandra Schlein, Leornado Kayat Bittencourt, Armin Schwartzman and 3 more

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology experimental, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 24% 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, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
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  7. Unsupervised deep learning registration model for multimodal brain images.Journal of applied clinical medical physics · 2023
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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

13 authors at 3 institutions in 2 countries.

Kyle A HasenstabLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Guilherme Moura CunhaLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA. gcunha@ucsd.edu.ORCID 0000-0001-6403-5155
Atsushi HigakiLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Shintaro IchikawaLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Kang WangLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Timo DelgadoLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Ryan L BrunsingDepartment of Radiology, Stanford University, Palo Alto, CA, USA.
Alexandra SchleinLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Leornado Kayat BittencourtAbdominal and Pelvic MRI, Radiology, CDPI Clinics, DASA Company, Fluminense Federal University (UFF), Rio de Janeiro, Brazil.
Armin SchwartzmanDepartment of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA.
Katie J FowlerLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Albert HsiaoAiDA Laboratory, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Claude B SirlinLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
University of California San Diego · USPalo Alto University · USUniversidade Federal Fluminense · BR

Funding

Traning Clinical Scientists in Radiological ImagingT32EB005970 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Eric Y Chang, Rebecca Ann Rakow-Penner · 2007 to 2026
$3.2M
NIBIB NIH HHS T32 EB005970
6 · The paper itself

Abstract

backgroundLiver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration.

methodsThree hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models.

resultsCompared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ≤ 0.020).

conclusionA fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration.

Indexed as

AlgorithmsNeural Networks, ComputerAdultAgedFemaleHumansLiverMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesGadolinium ethoxybenzyl DTPAImage processing (computer assisted)LiverMagnetic resonance imagingNeural networks (computer)

Identifiers

PMID31655943
PMCPMC6815316
OpenAlexW2981371998

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.