Intravascular Functional Maps of Common Neurovascular Lesions Derived From Volumetric 4D CT Data
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».