Description and assessment of a registration-based approach to include bones for attenuation correction of whole-body PET/MRI
Notice bibliographique
Résumé
PURPOSE: Attenuation correction for whole-body PET/MRI is challenging. Most commercial systems compute the attenuation map from MRI using a four-tissue segmentation approach. Bones, the most electron-dense tissue, are neglected because they are difficult to segment. In this work, the authors build on this segmentation approach by adding bones using a registration technique and assessing its performance on human PET images. METHODS: Twelve oncology patients were imaged with FDG PET/CT and MRI using a Turbo-FLASH pulse sequence. A database of 121 attenuation correction quality CT scans was also collected. Each patient MRI was compared to the CT database via weighted heuristic measures to find the "most similar" CT in terms of body geometry. The similar CT was aligned to the MRI with a deformable registration method. Two MRI-based attenuation maps were computed. One was a standard four-tissue segmentation (air, lung, fat, and lean tissue) using basic image processing techniques. The other was identical, except the bones from the aligned CT were added. The PET data were reconstructed with the patient's CT-based attenuation map (the silver standard) and both MRI-based attenuation maps. The relative errors of the MRI-based attenuation corrections were computed in 14 standardized volumes of interest, in lesions, and over whole tissues. The squared Pearson correlation coefficient was also calculated over whole tissues. Statistical testing was done with ANOVAs and paired t-tests. RESULTS: The MRI-based attenuation correction ignoring bone had relative errors ranging from -37% to -8% in volumes of interest containing bone. By including bone, the magnitude of the relative error was reduced in all cases (p<0.001), ranging from -3% to 4%. Further, the relative error in volumes of interest adjacent to bone was improved from a mean of -7.5% to 2% (p<0.001). In the other seven volumes of interest, including bone reduced the magnitude of relative error in three cases (p<0.001), had no effect in three cases, and increased relative error in one case. There was no statistically significant difference in the relative error in lesions. Over whole tissues, including bone slightly increased relative error in lung from 7.7% to 8.0% (p=0.002), in fat from 8.5% to 9.2% (p<0.001), and in lean tissue from -2.1% to 2.6% (p<0.001), but reduced the magnitude of relative error in bone from -14.6% to 1.3% (p<0.001). The correlation coefficient was essentially unchanged in all tissues regardless of whether bone was included or not. CONCLUSIONS: The approach to include bones in MRI-based attenuation maps described in this work improves quantification of whole-body PET images in and around bony anatomy. The reduction in error is often large (tens of percents), and could alter image interpretation and subsequent patient care. Changes in other parts of the PET image are minimal and likely not of clinical significance.
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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 | 0,005 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».