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Record W2007600300 · doi:10.1081/bio-120028672

Can Magnetic Resonance Imaging Be the Key Technique to Visualize and Investigate Endovascular Biomaterials?

2004· article· en· W2007600300 on OpenAlexaff
Robert Guidoin, François Langevin, M.F. Baslé, C. Alarcone, A. P. Legrand, Ze Zhang, B. Basse‐Cathalinat, Jean‐Michel Franconi, Yvan Douville, C. Baquey

Bibliographic record

VenueArtificial Cells Blood Substitutes and Biotechnology · 2004
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMagnetic resonance imagingMaterials scienceBiomedical engineeringModality (human–computer interaction)Scanning electron microscopeRadiologyComputer scienceMedicineArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

OBJECTIVE: Magnetic resonance imaging (MRI) is an established modality in clinical use but may be potentially underutilized to visualize and investigate biomaterials. As its use is totally contraindicated only for ferromagnetic devices, it was employed to visualize deployment, biofonctionality, healing, and biodurability of a commercially available endovascular device, namely the Medtronic-AVE AneuRx. The quality of the observations coupled with the absence of ionizing radiations are likely to make this technique an attractive imaging modality in the future. METHOD: The potential benefits of the MRI technique were investigated in a GE Vectra-MR 0.5T MRI for the Medtronic-AVE AneuRx endovascular prosthesis, under different conditions: undeployed i.e., inserted in the delivery cartridge as received from the manufacturer (step 1), deployed in a mock glass-aneurysm tube (step 2), and as a pathological explant harvested at the autopsy of a patient (step 3). The device was submitted to X-rays for examination in addition to MRI. At step 3, the device was further investigated with light microscopy and scanning electron microscopy (SEM) together with X-ray diffraction. RESULTS: The device which was inserted and pleated in the delivery cartridge did not demonstrate any significant observation either in MRI or in X-rays. When it was deployed in the mock aneurysmal glass tube, light artefacts were associated with the T2 weighed FSE images around the Nitinol whereas X-rays gave images of indisputable interest. Similar results were noted using the explanted device. Very high contrasts were obtained with T1 whereas T2 images were almost defect free. The X-rays allowed to accurate imaging of the Nitinol skeleton but were poor to discriminate between the different tissues. Pathology observations using light microscopy were not really challenged, as the magnetic resonance imaging was performed using a 0.5T machine. DISCUSSION: The benefits of magnetic resonance imaging as a quality control technique to examine an endovascular device within its cartridge remains ill defined. Similarly, the role of conventional X-rays is unknown. The observation of devices fully deployed in a mock aneurysmal glass-tube under MRI are potentially useful but X-rays images allowed better definition. The MRI examination of the explanted device does permit observations related to the healing of the device that might be obtained in vivo and, thus offers new avenues for the follow-up of implanted devices. The pathological investigations brought additional informations about the tissues and the corrosion of the Nitinol. However, it is unlikely that MRI will permit detailed analysis of the biomaterials and in particular the corrosion process of the stents. CONCLUSION: These early observations of the follow-up of devices using MRI warrant further investigation. The absence of ionizing radiation with MRI makes this technique particularly attractive. As there is no emission of ionizing radiation associated with magnetic resonance, it is recommended that further investigation using this environment friendly technique for the follow-up of devices made of biomaterials that are MRI compatible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.238
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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