Evidence map›Paper›PMID 31252162›Full record

ArticleMedical image analysis2019

Fast predictive simple geodesic regression.

Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer, Alzheimer’s Disease Neuroimaging Initiative

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image analysis, 2019. 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. Review
  2. Article
  3. Anomaly detection for the individual analysis of brain PET images.Journal of medical imaging (Bellingham, Wash.) · 2021
    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

7 authors.

Zhipeng DingDepartment of Computer Science, University of North Carolina at Chapel Hill, USA 201 S. Columbia St., Chapel Hill, NC 27599, USA. Electronic address: zp-ding@cs.unc.edu.
Greg FleishmanImaging Genetics Center, University of Southern California, USA 2001 N. Soto Street, SSB1-102, Los Angeles, CA 90032, USA; Department of Radiology, University of Pennsylvania, USA 3400 Civic Center Boulevard Atrium, Ground Floor, Philadelphia, PA 19104, USA. Electronic address: greg.nli10me@gmail.com.
Xiao YangDepartment of Computer Science, University of North Carolina at Chapel Hill, USA 201 S. Columbia St., Chapel Hill, NC 27599, USA. Electronic address: xy@cs.unc.edu.
Paul ThompsonImaging Genetics Center, University of Southern California, USA 2001 N. Soto Street, SSB1-102, Los Angeles, CA 90032, USA. Electronic address: pthomp@usc.edu.
Roland KwittDepartment of Computer Science, University of Salzburg, Austria Jakob Haringer Strasse 2, 5020 Salzburg, Austria. Electronic address: roland.kwitt@gmail.com.
Marc NiethammerDepartment of Computer Science, University of North Carolina at Chapel Hill, USA 201 S. Columbia St., Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA 125 Mason Farm Road, Chapel Hill, NC 27599, USA. Electronic address: mn@cs.unc.edu.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Large-scale automatic analysis of the OAI magnetic resonance image datasetR01AR072013 · NIAMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NIETHAMMER, MARC · 2017 to 2022
$2.2M
NIAMS NIH HHS R01 AR072013NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.

Indexed as

AlgorithmsImaging, Three-DimensionalMagnetic Resonance ImagingRegression AnalysisBrainDatasets as TopicImage Processing, Computer-AssistedModels, StatisticalReproducibility of ResultsADNI datasetFast predictionImage regressionLongitudinal data

Identifiers

PMID31252162
PMCPMC6661182

What OpenQuestion holds

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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.