Evidence map›Paper›PMID 33792751›Full record

ReviewPediatric radiology2021

Pediatric skeletal diffusion-weighted magnetic resonance imaging: part 1 - technical considerations and optimization strategies.

Apeksha Chaturvedi

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pediatric radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 22% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
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

1 author at 1 institution in 1 country.

Apeksha ChaturvediDivision of Pediatric Radiology, Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave., Rochester, NY, 14642, USA. apeksha_chaturvedi@urmc.rochester.edu.ORCID http://orcid.org/0000-0002-1222-4795
University of Rochester Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffusion-weighted MRI, or DWI, is a fast, quantitative technique that is easily integrated into a morphological MR acquisition. The ability of DWI to aid in detecting multifocal skeletal pathology and in characterizing tissue cellularity to a level beyond that possible with other techniques makes it a niche component of multiparametric MR imaging of the skeleton. Besides its role in disease detection and establishing cellularity and character of osseous lesions, DWI continues to be examined as a surrogate biomarker for therapeutic response of several childhood bone tumors. There is increasing interest in harnessing DWI as a potential substitute to alternative modes of imaging evaluation that involve radiation or administration of intravenous contrast agent or radiopharmaceuticals, for example in early detection and diagnosis of capital femoral epiphyseal ischemia in cases of Legg-Calvé-Perthes disease, or diagnosis and staging of lymphoma. The expected evolution of skeletal diffusivity characteristics with maturation and the unique disease processes that affect the pediatric skeleton necessitate a pediatric-specific discussion. In this article, the author examines the developmentally appropriate normal appearances, technique, artifacts and pitfalls of pediatric skeletal DWI.

Indexed as

Diffusion Magnetic Resonance ImagingLegg-Calve-Perthes DiseaseChildEpiphysesHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyBone marrowChildrenDiagnosisDiffusion-weighted imagingMagnetic resonance imagingOptimizationSkeletonStaging

Identifiers

PMID33792751
OpenAlexW3150253290

What OpenQuestion holds

Textmetadata
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.