Towards myocardial contraction force image reconstruction for heart disease assessment and intervention planning
Bibliographic record
Abstract
© 2015 SPIE. It is clinically vital to devise a technique to evaluate regional functionality of the myocardium in order to determine the extent and intensity of local damage to the cardiac tissue caused by ischemic injuries. Such a technique can potentially enable cardiologists to discriminate between reversible and irreversible ischemic injuries and to devise appropriate revascularization therapy in case of reversible lesions. The technique is founded on the premise that sufficient contraction force generated by the cardiac tissue can be regarded as a direct and reliable criterion for regional analysis of tissue healthy functionality. To this end, a number of imaging techniques have been developed and, to our knowledge, none of them assess regional cardiac functionality based on a straightforward mechanical measure such as local cardiac contraction forces. As such, a novel imaging technique is being developed on the basis of quantification and visualisation of local myocardial contraction forces. In this technique, cardiac contraction force distribution is attained through solving an inverse problem within an optimization framework which uses iterative forward mechanical modelling of the myocardium. Hence, a forward mechanical model of the myocardium which is computationally efficient, robust, and adaptable to diverse pathophysiological conditions is necessary for this development. As such, this paper is geared towards developing a novel mechanical model of the healthy and pathological myocardium which considers all aspects of the myocardial mechanics including hyperelasticity, anisotropy, and active contraction force. In this investigation, two major parts, including background tissue and reinforcement bars (fibers) have been considered for modelling the myocardium. The model was implemented using finite element (FE) approach and demonstrated very good performance in simulating normal and infarcted left ventricle (LV) contractile function.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".