Development, Optimization, and Validation of a Cardiac Magnetic Resonance Imaging Protocol Under Exercise Stress Conditions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".