Intravascular Functional Maps of Common Neurovascular Lesions Derived From Volumetric 4D CT Data
Bibliographic record
Abstract
PURPOSE: Current computed tomography angiography (CTA) postprocessing tools do not support quantitative assessment of intravascular physiology. Dynamic volumetric CT, acquired at a sufficiently high temporal resolution, is ideal for such analysis. Following preliminary experiments in flow phantoms, we examine the segmentation of blood vessels from 4D CT angiography by curve fit and encoding of functional blood flow information into the resulting functional intravascular maps. MATERIALS AND METHODS: Flow phantoms were constructed consisting of a single pipe input and 4 simultaneous outputs of varying flow rates. Two outflow pipe diameters were tested. Bolus transit time (TT), time to peak (TTP), and time of arrival (TOA) were analyzed using contrast bolus profiles generated from 4D volumetric CT examinations on a 320 detector scanner in regions of interest placed 10 cm apart in all outflow pipes. Six subjects with various neurovascular lesions were next examined using a volumetric contrast-enhanced 4D CT angiography protocol. Segmentation was performed by quadratic curve fit after comparative analysis and optimization of the segmentation technique using quadratic curves, the gamma variate function, and a simplified formulation of the gamma variate function. After segmentation, quantitative analysis of spatially congruent intravascular voxels including TTP, rise, TT, and slope of the contrast upstroke was employed to encode physiologic information into the segmentations and produce intravascular functional maps. Comparison was made in each case to the patient's routine imaging. RESULTS: Increasing volumetric flow rates correspond to reduction of bolus TT in flow phantoms. TT elongation was observed as the contrast bolus moved distally in all pipes, with greater elongation seen at slower flow rates and larger pipe diameters. A greater difference was observed between TTP proximally and distally in pipes compared with TOA, an effect most prominent at slower flow rates and larger pipe lumens, and thus TTP was chosen for functional encoding into segmentations of the clinical series. In vivo, the quadratic function demonstrated the lowest coefficient of variation when fit to intravascular time density series and outperformed 2 formulations of the gamma variate function. After segmentation with quadratic curves, Gaussian distributions were chosen over gamma variate functions to characterize contrast bolus profiles while neglecting recirculation and to calculate functional parameters for spatial encoding. Intravascular functional maps free of bone artifacts were created in every case that demonstrated all appropriate vessels and showed agreement with conventional imaging modalities in terms of vessel delineation and the diagnosis of vascular pathology. The most useful and interesting functional maps are discussed in each case. CONCLUSIONS: The above approach to quantitative CT angiography provides a method of evaluating dynamic CTA data by means of intravascular functional maps. The techniques are broadly applicable in the clinical assessment of a variety of vascular diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 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".