Leveraging LU-Decomposition for Accelerated Tissue Chromophore Quantification in Diffuse Optical Imaging
Bibliographic record
Abstract
Accurate and fast quantification of the concentration of the main chromophores in tissue is crucial for cancer characterization.Previously, Photo-Magnetic Imaging (PMI) technique has been proposed to undergo this task with high resolution and quantitative accuracy.The standard iterative PMI method for chromophore quantification, while effective, can be computationally intensive, especially when dealing with large and complex geometries.In this paper, a non-iterative method is introduced for the fast and accurate determination of chromophore concentrations.By using linear algebraic methodology, particularly LU decomposition, the computational time is significantly reduced without degrading the accuracy of PMI.This new approach is evaluated on a numerical phantom with a tumor-like inclusion containing a mixture of two different dyes mimicking tissue chromophores.By using only two laser wavelengths, the chromophores concentrations were accurately resolved with an error as low as 7% using both methods.Nevertheless, the computational time was reduced 140-fold using the new approach.This accelerated method has the potential to revolutionize real-time monitoring and diagnostics, enabling faster analysis of tumor characteristics, treatment planning and response.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".