Evidence map›Paper›PMID 29097881›Full record

ArticleSIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics2017

A LAGRANGIAN GAUSS-NEWTON-KRYLOV SOLVER FOR MASS- AND INTENSITY-PRESERVING DIFFEOMORPHIC IMAGE REGISTRATION.

Andreas Mang, Lars Ruthotto

Abstract read
In one paragraph

Article in SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. A machine learning framework for solving high-dimensional mean field game and mean field control problems.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  4. Coupling brain-tumor biophysical models and diffeomorphic image registration.Computer methods in applied mechanics and engineering · 2019
    Article
  5. CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2019
    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.

Andreas MangInstitute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA. (AM is now with the Department of Mathematics at the University of Houston).
Lars RuthottoDepartment of Mathematics and Computer Science, Emory University, Atlanta, Georgia, USA.

Funding

Statistical atlases of brain tumor MRI:do imaging phenotypes predict progression?R01NS042645 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2002 to 2024
$8.7M
NINDS NIH HHS R01 NS042645
6 · The paper itself

Abstract

We present an efficient solver for diffeomorphic image registration problems in the framework of

Indexed as

35Q9349J2065K1065M3268U1076D55Diffeomorphic Image RegistrationLagrangian MethodsLarge Deformation Diffeomorphic Metric MappingOptimal ControlPDE-Constrained Optimization

Identifiers

PMID29097881
PMCPMC5662028

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.