Evidence map›Paper›PMID 36325254›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering

Cranial Meninges Reconstruction Based on Convolutional Networks and Deformable Models: Applications to Longitudinal Study of Normal Aging.

Peiyu Duan, Shuo Han, Lianrui Zuo, Yang An, Yihao Liu, Ahmed Alshareef, Junghoon Lee, Aaron Carass, Susan M Resnick, Jerry L Prince

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering. 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. RAPID BRAIN MENINGES SURFACE RECONSTRUCTION WITH LAYER TOPOLOGY GUARANTEE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023
    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

10 authors.

Peiyu DuanDepartment of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Shuo HanDepartment of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Lianrui ZuoDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Yang AnLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892.
Yihao LiuDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Ahmed AlshareefDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Junghoon LeeDepartment of Radiology, The Johns Hopkins School of Medicine, Baltimore, MD 21287.
Aaron CarassDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 20892.
Jerry L PrinceDepartment of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218.

Funding

Segmenting the subarachnoid space and dura from clinical MRIR21NS120286 · NINDS · JOHNS HOPKINS UNIVERSITY · PI PRINCE, JERRY L · 2020 to 2020
$454k
NINDS NIH HHS R21 NS120286
6 · The paper itself

Abstract

The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging.

Indexed as

convolutional networksdeformable modelsharmonizationlongitudinal studyMeningesMRI

Identifiers

PMID36325254
PMCPMC9623767

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.