Improved Cerebral Blood Flow Measurement with Multiexposure Speckle Imaging
Notice bibliographique
Résumé
Laser speckle contrast imaging (LSCI) is a powerful tool for in vivo imaging of blood flow dynamics. Based on the blurring of the random interference pattern (‘speckle') produced at the camera by laser light reflecting off a surface, LSCI has been rapidly adopted because of its high spatial and temporal resolution coupled with the relative simplicity and low cost of assembling a LSCI instrument. Wide-field maps of cerebral blood flow acquired via LSCI have made significant contributions to our understanding of cerebral hemodynamics in the healthy and ischemic brain in animal models.1, 2, 3, 4 More recently, LSCI has been applied in studies of human patients.5, 6 While the high-resolution maps of relative changes in blood flow acquired during LSCI have been well validated, flow estimates from LSCI are affected by a number of parameters unrelated to blood flow. As such, the exact quantitative relationship between speckle contrast and blood flow velocity has not been precisely defined.7, 8 The sensitivity of LSCI to nonflow parameters is particularly problematic in long-term studies where nonflow parameters are more likely to vary between imaging sessions. Recently, traditional single-exposure LSCI was extended with a multiexposure speckle imaging (MESI) protocol that improved quantitative accuracy in microfluidic flow simulations and in vivo imaging in rodents.9, 10 Using improved mathematical models and instrumentation to acquire speckle contrast images at multiple, defined exposures, MESI accurately estimates flow changes associated during acute ischemic stroke, contrary to the underestimates of flow changes associated with traditional single-exposure LSCI.10 However, the utility of MESI in long-term, repeated-imaging studies has not been validated. In the current issue, Kazmi et al11 perform MESI in mice implanted with chronic cranial imaging windows. Multiexposure speckle imaging data are compared with quantitative measurements of absolute blood flow acquired via high-frame rate red blood cell photography (RBC tracking). In healthy mice and mice with targeted ischemic strokes imaged over multiple days, MESI consistently quantified blood flow more accurately than single-exposure LSCI. Notably, by testing multiple exposure times, Kazmi et al demonstrated that no single exposure time was optimal in all animals and imaging sessions. By incorporating multiple exposure times and improved mathematical modeling, MESI better accounted for variations in imaging conditions and more reliably quantified the blood flow during chronic imaging. Improved quantitative accuracy is demonstrated by reduced deviation between flow measurements derived from MESI and RBC tracking (∼10%) relative to single-exposure LSCI and RBC tracking (∼24%). While the reliability of quantitative accuracy between animal comparisons remains to be confirmed, MESI significantly improves the quantitative accuracy of LSCI blood flow measurements in both acute and chronic imaging paradigms. This improved quantitative accuracy extends LSCI beyond qualitative descriptions of changes in flow, an important contribution to preclinical and clinical studies of stroke hemodynamics and flow restoration therapies.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».