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Record W6981073264

Development, Optimization, and Validation of a Cardiac Magnetic Resonance Imaging Protocol Under Exercise Stress Conditions

2024· other· fr· W6981073264 on OpenAlexfundno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2024
Typeother
Languagefr
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsnot available
FundersCanada First Research Excellence FundPolytechnique Montréal
KeywordsCoronary heart diseaseElectrodiagnosisCardiac magnetic resonanceVentricular function
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Le remodelage cardiaque est un processus qui survient en réponse à une maladie cardiaque et qui engendre une cascade de changements dans la masse, la géométrie et la fonction du cœur. Toutefois, ce processus peut mener à des conditions cardiaques graves telles que l'insuffisance cardiaque, qui est une cause importante de morbidité et de décès. Malgré des progrès significatifs, la détection de changements anormaux après le remodelage cardiaque, reste un défi en raison du manque de techniques de caractérisation fiables et précises. L'un des principaux problèmes est que les techniques existantes telles que l'échocardiographie et l'imagerie par résonance magnétique (IRM) effectuées au repos, ne permettent pas la détection d’anomalies subtiles des maladies cardiaques. En conséquence, l’IRM à l’effort émerge comme un outil permettant une meilleure caractérisation. En effet, l’IRM à l’effort démontre un fort potentiel de caractériser précisément le cœur et de révéler des anomalies non apparentes au repos chez les patients cardiaques. L’IRM à l’effort a également démontré sa capacité à différencier le remodelage physiologique due à l’effort au remodelage pathologique avec une meilleure précision. Pour améliorer l'efficacité de l’IRM à l’effort, les chercheurs ont combiné cette technique avec des modèle computationnels, permettant la quantification d'autres paramètres cardiaques difficiles à mesurer pendant l'imagerie, telles que les pressions cardiaques et les propriétés mécaniques cardiaques. Malgré l'utilité des protocoles de l’IRM à l’effort, leurs applications cliniques demeurent limitées en raison des risques de compromettre la sécurité des patients et la qualité des images. En conséquence, le besoin de développer un nouveau protocole de l’IRM à l’effort plus efficace demeure. Cette technique pourrait permettre une caractérisation cardiaque plus précise et pourrait améliorer ainsi la détection précoce de maladies cardiaques. Pour développer un nouveau protocole d’IRM à l’effort, nous avons d'abord mené une étude pour mieux comprendre la relation entre les paramètres de l’IRM cardiaque et les propriétés mécaniques cardiaques des survivants du cancer de la leucémie lymphoblastique aiguë (LLA). Pour étudier la relation entre les propriétés mécaniques cardiaques et les temps de relaxation T1 et T2, ainsi que le coefficient de partition, nous avons analysé les résultats de 50 enfants survivants de la LLA ayant subi un examen d'IRM cardiaque sur un IRM Tesla 3T. ABSTRACT: Cardiac remodeling is a process that occurs in response to a cardiac disease and involves a cascade of changes in the heart’s mass, geometry, and function. Unfortunately, this process can lead to serious cardiac conditions such as heart failure, which is a significant cause of death and morbidity. Despite significant advances, detecting abnormal changes after cardiac remodeling is still a challenge due to the lack of precise and accurate techniques. Currently, one of the main problems is that existing techniques such as echocardiography and magnetic resonance imaging (MRI) when performed at rest, do not allow for the detection of subtle abnormalities in cardiac diseases. As a result, exercise cardiovascular magnetic resonance (Ex-CMR) has emerged as a valuable tool that allows for an accurate characterization. Indeed, Ex-CMR revealed a strong potential to characterize accurately the heart and to unmask abnormalities not seen at rest in cardiac patients. Ex-CMR has also demonstrated the ability to differentiate between normal cardiac remodeling and pathological cardiac remodeling with considerable accuracy. To improve the effectiveness of Ex-CMR, researchers have combined this technique with computational models, allowing the quantification of other cardiac parameters that are not easily measured during imaging, such as cardiac pressures, and cardiac mechanical properties. Despite the usefulness of Ex-CMR protocols, their clinical applications are still limited, due to certain limitations that may compromise the safety of participants of the quality of the images. Therefore, researchers have demonstrated that the need to develop a new effective Ex-CMR protocol, still exists. This technique may allow for a more accurate heart characterization and may improve the early detection of cardiac diseases. To develop a new Ex-CMR protocol, we first conducted a study to investigate the relationship between CMR parameters and cardiac mechanical properties in the survivors of acute lymphoblastic leukemia (ALL) cancer. To investigate the relationship between cardiac mechanical properties, T1 and T2 relaxation times, and the partition coefficient, we analyzed 50 childhood ALL survivors who underwent CMR exams on a 3T MRI system. These participants were initially classified into three risk groups: Standard Risk group (SR), High-Risk group (HR) and High Risk that took dexrazoxane as a cardioprotective agent (HR+DEX) group. After obtaining the cardiac parameters from Cine-images, we used the CircAdapt model to simulate participants’ cardiac mechanical performance (i.e. left ventricle stiffness (LVS), contractility (LVC) and pressure (Pminand Pmax), cardiac work efficiency (CWE) and ventricular arterial coupling (VAC). The results showed that cardiac properties can be predicted from the combination of CMR parameters and vice versa. For example, in the SR group, LVS was predicted from the combination of CMR parameters with (R2 = 94.8%, r = 0.97) and LVC was predicted with (R2 = 93.7%, r = 0.96). Partition coefficient was also predicted from the combination of cardiac mechanical properties by (R2 = 72.6%, r = 0.85) in the SR group. After analyses, we found that the high cumulative dose administered in the HR group limits the correlation between cardiac mechanical properties and CMR parameters in that group.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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