Prediction and Reduction of Motion Artifacts in Free-Breathing Dynamic Contrast Enhanced CT Perfusion Imaging of Primary and Metastatic Intrahepatic Tumors
Notice bibliographique
Résumé
Rationale and ObjectiveTo develop and evaluate a method for predicting and reducing motion artifacts in free-breathing liver perfusion computed tomography (CT) scanning with couch shuttling and to compare tumor and liver parenchyma perfusion values.Materials and MethodsThirty patients (23 males, 7 females, median age of 74 years) with primary or metastatic intrahepatic tumors underwent dynamic contrast enhanced CT scans with axial shuttling. A semiautomatic respiratory motion correction algorithm was applied to align the acquired images along the z-axis. Perfusion maps were generated using the dual-input Johnson-Wilson model. Root mean squared deviation (RMSD) maps of the model fit to the pixel time-density curves were calculated.ResultsPrecorrection RMSD correlated positively with magnitude of change in functional values resulting from motion. Blood flow, arterial blood flow, and permeability surface product were significantly increased in tumor compared to normal tissue (P < .05), blood volume was significantly reduced in tumor compared to normal tissue (P < .05). In a subgroup of patients with high-amplitude motion significant difference was observed between uncorrected and motion correction blood flow maps.ConclusionsPatients can breathe freely during hepatic perfusion imaging if retrospective motion correction is applied to reduce motion artifacts. RMSD provides a regional assessment of motion induced artifacts in liver perfusion maps. To develop and evaluate a method for predicting and reducing motion artifacts in free-breathing liver perfusion computed tomography (CT) scanning with couch shuttling and to compare tumor and liver parenchyma perfusion values. Thirty patients (23 males, 7 females, median age of 74 years) with primary or metastatic intrahepatic tumors underwent dynamic contrast enhanced CT scans with axial shuttling. A semiautomatic respiratory motion correction algorithm was applied to align the acquired images along the z-axis. Perfusion maps were generated using the dual-input Johnson-Wilson model. Root mean squared deviation (RMSD) maps of the model fit to the pixel time-density curves were calculated. Precorrection RMSD correlated positively with magnitude of change in functional values resulting from motion. Blood flow, arterial blood flow, and permeability surface product were significantly increased in tumor compared to normal tissue (P < .05), blood volume was significantly reduced in tumor compared to normal tissue (P < .05). In a subgroup of patients with high-amplitude motion significant difference was observed between uncorrected and motion correction blood flow maps. Patients can breathe freely during hepatic perfusion imaging if retrospective motion correction is applied to reduce motion artifacts. RMSD provides a regional assessment of motion induced artifacts in liver perfusion maps.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».