Evidence map›Paper›PMID 39830685›Full record

ArticleFrontiers in bioengineering and biotechnology2024

From MRI to FEM: an automated pipeline for biomechanical simulations of vertebrae and intervertebral discs.

Kati Nispel, Tanja Lerchl, Gabriel Gruber, Hendrik Moeller, Robert Graf, Veit Senner, Jan S Kirschke

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Kati NispelInstitute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Tanja LerchlInstitute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Gabriel GruberInstitute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Hendrik MoellerInstitute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Robert GrafInstitute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Veit SennerAssociate Professorship of Sport Equipment and Sport Materials, School of Engineering and Design, Technical University of Munich, Garching, Germany.
Jan S KirschkeInstitute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Biomechanical simulations can enhance our understanding of spinal disorders. Applied to large cohorts, they can reveal complex mechanisms beyond conventional imaging. Therefore, automating the patient-specific modeling process is essential. Methods: We developed an automated and robust pipeline that generates and simulates biofidelic vertebrae and intervertebral disc finite element method (FEM) models based on automated magnetic resonance imaging (MRI) segmentations. In a first step, anatomically-constrained smoothing approaches were implemented to ensure seamless contact surfaces between vertebrae and discs with shared nodes. Subsequently, surface meshes were filled isotropically with tetrahedral elements. Lastly, simulations were executed. The performance of our pipeline was evaluated using a set of 30 patients from an in-house dataset that comprised an overall of 637 vertebrae and 600 intervertebral discs. We rated mesh quality metrics and processing times. Results: With an average number of 21 vertebrae and 20 IVDs per subject, the average processing time was 4.4 min for a vertebra and 31 s for an IVD. The average percentage of poor quality elements stayed below 2% in all generated FEM models, measured by their aspect ratio. Ten vertebra and seven IVD FE simulations failed to converge. Discussion: The main goal of our work was to automate the modeling and FEM simulation of both patient-specific vertebrae and intervertebral discs with shared-node surfaces directly from MRI segmentations. The biofidelity, robustness and time-efficacy of our pipeline marks an important step towards investigating large patient cohorts for statistically relevant, biomechanical insight.

Indexed as

automatedFEMintervertebral discmeshingMRIpatient-specificspinevertebra

Identifiers

PMID39830685
PMCPMC11739884

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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.