Micromechanics based modelling of in-vivo respiratory motion of the diaphragm muscle with the incorporation of optimized z-disks mechanics
Bibliographic record
Abstract
Lung cancer is by far the leading cause of cancer death among both men and women; according to the American Cancer Society, approximately 1 out of 4 cancer deaths are due to lung cancer. The primary treatment for the condition generally involves External Beam Radiation Therapy (EBRT). Lung cancer tumour motion is generally clinically significant and presents a major challenge for clinicians. With significant lung tumour motion (>;5mm) during respiration comes the requirement for motion compensation techniques1 . Ideally, continuous real-time tumour tracking allows for continuous radiation delivery such that the tumour receives sufficient radiation dose while minimizing dose to surrounding healthy lung tissue. Direct tumour tracking is often not possible in non-contrast images and a surrogate is required for tumour motion. Among surrogates for tumour tracking, the diaphragm muscle has shown to provide good correlation with tumour motion2 . Motion compensation techniques often require extensive 4D CT scans which is inherently dangerous. The diaphragm muscle, the major driver of respiratory motion, can also be incorporated into lung biomechanical models used to predict deformations in the lungs and surrounding organs during respiration3 . This research involves the development of a patient specific biomechanical model of the diaphragm muscle with both passive and active responses. Detailed anatomical, and geometric information, including the muscle micromechanics, is used to generate a Finite Element Model (FEM) of the diaphragm in order to predict its in vivo motion. Results from modelling a patient specific case revealed a good match between the simulated and actual contracted diaphragm surface with an average mean squared difference of 2.83 mm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".