Calculation of Intravascular Signal in Dynamic Contrast Enhanced-MRI Using Adaptive Complex Independent Component Analysis
Bibliographic record
Abstract
Assessing tumor response to therapy is a crucial step in personalized treatments. Pharmacokinetic (PK) modeling provides quantitative information about tumor perfusion and vascular permeability that are associated with prognostic factors. A fundamental step in most PK analyses is calculating the signal that is generated in the tumor vasculature. This signal is usually inseparable from the extravascular extracellular signal. It was shown previously using in vivo and phantom experiments that independent component analysis (ICA) is capable of calculating the intravascular time-intensity curve in dynamic contrast enhanced (DCE)-MRI. A novel adaptive complex independent component analysis (AC-ICA) technique is developed in this study to calculate the intravascular time-intensity curve and separate this signal from the DCE-MR images of tumors. The use of the complex-valued DCE-MRI images rather than the commonly used magnitude images satisfied the fundamental assumption of ICA, i.e., linear mixing of the sources. Using an adaptive cost function in ICA through estimating the probability distribution of the tumor vasculature at each iteration resulted in a more robust and accurate separation algorithm. The AC-ICA algorithm provided a better estimate for the intravascular time-intensity curve than the previous ICA-based method. A simulation study was also developed in this study to realistically simulate DCE-MRI data of a leaky tissue mimicking phantom. The passage of the MR contrast agent through the leaky phantom was modeled with finite element analysis using a diffusion model. Once the distribution of the contrast agent in the imaging field of view was calculated, DCE-MRI data was generated by solving the Bloch equation for each voxel at each time point. The intravascular time-intensity curve calculation results were compared to the previously proposed ICA-based intravascular time-intensity curve calculation method that applied ICA to the magnitude of the DCE-MRI data (Mag-ICA) using both simulated and experimental tissue mimicking phantoms. The AC-ICA demonstrated superior performance compared to the Mag-ICA method. AC-ICA provided more accurate estimate of intravascular time-intensity curve, having smaller error between the calculated and actual intravascular time-intensity curves compared to the Mag-ICA. Furthermore, it showed higher robustness in dealing with datasets with different resolution by providing smaller variation between the results of each datasets and having smaller difference between the intravascular time-intensity curves of various resolutions. Thus, AC-ICA has the potential to be used as the intravascular time-intensity curve calculation method in PK analysis and could lead to more accurate PK analysis for tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".