Recovery of missing data in partial geometry <scp>PET</scp> scanners: Compensation in projection space vs image space
Notice bibliographique
Résumé
PURPOSE: Robust and reliable reconstruction of images from noisy and incomplete projection data holds significant potential for proliferation of cost-effective medical imaging technologies. Since conventional reconstruction techniques can generate severe artifacts in the recovered images, a notable line of research constitutes development of appropriate algorithms to compensate for missing data and to reduce noise. In the present work, we investigate the effectiveness of state-of-the-art methodologies developed for image inpainting and noise reduction to preserve the quality of reconstructed images from undersampled PET data. We aimed to assess and ascertain whether missing data recovery is best performed in the projection space prior to reconstruction or adjoined with the reconstruction step in image space. METHODS: Different strategies for data recovery were investigated using realistic patient derived phantoms (brain and abdomen) in PET scanners with partial geometry (small and large gap structures). Specifically, gap filling strategies in projection space were compared with reconstruction based compensation in image space. The methods used for filling the gap structure in sinogram PET data include partial differential equation based techniques (PDE), total variation (TV) regularization, discrete cosine transform(DCT)-based penalized regression, and dictionary learning based inpainting (DLI). For compensation in image space, compressed sensing based image reconstruction methods were applied. These include the preconditioned alternating projection (PAPA) algorithm with first and higher order total variation (HOTV) regularization as well as dictionary learning based compressed sensing (DLCS). We additionally investigated the performance of the methods for recovery of missing data in the presence of simulated lesion. The impact of different noise levels in the undersampled sinograms on performance of the approaches were also evaluated. RESULTS: In our first study (brain imaging), DLI was shown to outperform other methods for small gap structure in terms of root mean square error (RMSE) and structural similarity (SSIM), though having relatively high computational cost. For large gap structure, HOTV-PAPA produces better results. In the second study (abdomen imaging), again the best performance belonged to DLI for small gap, and HOTV-PAPA for large gap. In our experiments for lesion simulation on patient brain phantom data, the best performance in term of contrast recovery coefficient (CRC) for small gap simulation belonged to DLI, while in the case of large gap simulation, HOTV-PAPA outperformed others. Our evaluation of the impact of noise on performance of approaches indicated that in case of low and medium noise levels, DLI still produces favorable results among inpainting approaches. However, for high noise levels, the performance of PDE4 (variant of PDE) and DLI are very competitive. CONCLUSIONS: Our results showed that estimation of missing data in projection space as a preprocessing step before reconstruction can improve the quality of recovered images especially for small gap structures. However, when large portions of data are missing, compressed sensing techniques adjoined with the reconstruction step in image space were the best strategy.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».