Deconvolution of dynamic dual photon microscopy images of cerebral microvasculature to assess the hemodynamic status of the brain
Bibliographic record
Abstract
Assessing the hemodynamic status of the brain and its variations in response to stimulations is required to understand the local cerebral circulatory mechanisms. Dynamic contrast enhanced imaging of cerebral microvasculature provides information that can be used in understanding physiology of cerebral diseases. Bolus tracking is used to extract characteristic parameters that quantify local cerebral blood flow. However, post-processing of the data is needed to segment the field of view (FOV) and to perform deconvolution to remove the effects of input bolus profile and the path it travels to reach the imaging window. Finding the arterial input function (AIF) and dealing with the ill-posedness of deconvolution system make this process are the main challenges. We propose using ICA to segment the FOV and to extract a local AIF as well as the venous output function that is required for deconvolution. This also helps to stabilize the system as ICA suppresses noise efficiently. Tikhoniv regularization (with L-curve analysis to find the best regularization parameter) is used to make the system stable. In-vivo dynamic 2PLSM images of a rat brain in two conditions (when the animal is at rest and when it is stimulated) are used in this study. The experimental along with the simulation studies provided promising results that demonstrate the feasibility and importance of performing deconvolution.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".