Evidence map›Paper›PMID 28855745›Full record

ArticleInverse problems2017

SHAPE BASED IMAGE RECONSTRUCTION USING LINEARIZED DEFORMATIONS.

Ozan Öktem, Chong Chen, Nevzat Onur Domaniç, Pradeep Ravikumar, Chandrajit Bajaj

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Inverse problems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Ozan ÖktemDepartment of Mathematics, KTH - Royal Institute of Technology, 100 44 Stockholm, Sweden.
Chong ChenDepartment of Mathematics, KTH - Royal Institute of Technology, 100 44 Stockholm, Sweden and LSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Nevzat Onur DomaniçDepartment of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.
Pradeep RavikumarDepartment of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.
Chandrajit BajajDepartment of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.

Funding

Mathematical Chemical Imaging with Uncertainty Quantification R01GM117594 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI BAJAJ, CHANDRAJIT L · 2015 to 2018
$1.6M
Polar sampling and optimization of protein-ligand cocrystal structuresR41GM116300 · NIGMS · GFREE BIO, LLC · PI BAJAJ, CHANDRAJIT L, REYNOLDS, CHARLES H. · 2016 to 2016
$225k
NIGMS NIH HHS R01 GM117594NIGMS NIH HHS R41 GM116300
6 · The paper itself

Abstract

We introduce a reconstruction framework that can account for shape related a priori information in ill-posed linear inverse problems in imaging. It is a variational scheme that uses a shape functional defined using deformable templates machinery from shape theory. As proof of concept, we apply the proposed shape based reconstruction to 2D tomography with very sparse measurements, and demonstrate strong empirical results.

Indexed as

37C0544A1247A5254C5657N2557R2765F2265R3065R3292C5594A0894A12electron tomographyinverse problemsreconstructionregularizationshape analysisTomography

Identifiers

PMID28855745
PMCPMC5573282

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.