Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook
Notice bibliographique
Résumé
PURPOSE: Partial volume effects (PVE) remain a major challenge in quantitative single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging, often compromising both accuracy and reproducibility. While numerous Partial Volume Correction (PVC) methods have been proposed, their clinical translation is still limited. This review provides a clinically oriented evaluation of PVC methods with a particular focus on state-of-the-art applications in neurology, cardiovascular imaging, oncology, and radiopharmaceutical therapy dosimetry, highlighting where these techniques offer the greatest added value. In addition, we outline which PVC techniques have the potential to be used in clinical practice and which remain primarily suited for research purposes, along with their suitability in each of the above-mentioned clinical domains. Finally, this review addresses the central question of whether PVC is essential in clinical practice or whether its impact is context dependent. METHODS: This review categorizes PVC approaches into three partially overlapping classes: reconstruction-based, post-reconstruction-based, and AI-driven or hybrid methods. Each class is further divided into anatomical and non-anatomical subcategories. We systematically compare their clinical applicability across key dimensions: quantitative accuracy, lesion detectability, robustness to noise and artifacts, anatomical dependence, generalizability across scanners and tracers, and clinical readiness. RESULTS: PVC techniques often improve quantitative accuracy in small structures and in regions affected by spill-over from adjacent high-uptake tissues. However, these benefits can come at the cost of increased noise or edge artifacts, which may limit their robustness for routine clinical use. Post-reconstruction methods are sensitive to segmentation errors, while AI-driven models, despite their promise, require further validation using clinical benchmarks, comparison to ground truth, and testing on diverse datasets. Issues, such as generalizability and interpretability remain significant barriers. CONCLUSION: This review emphasizes the importance of application-tailored PVC protocols for reliable quantitative imaging in neurology, cardiology, oncology, and radiopharmaceutical therapy dosimetry. Not all PVC methods are beneficial; some may even impair interpretation in certain contexts. We provide a practical overview of which PVC approaches are most beneficial for each clinical scenario, aiming to guide both researchers and clinicians in selecting appropriate techniques for future studies and routine practice, and also outline key areas requiring further development for broader integration into research and clinical workflows.
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 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 | 0,001 |
| 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 ».