Respiratory Motion Alters Measurement of SPECT Myocardial Blood Flow
Notice bibliographique
Résumé
654 Background: Respiratory motion degrades image quality in nuclear medicine imaging. Motion during acquisition blurs the image, increasing partial volume effects, and can increase the spill-over contamination from hot extra-cardiac structures into the myocardial wall. Studies of static myocardial perfusion images have shown that there is substantial motion (>1cm) in up to 33% of patient studies. It is thus expected that there is respiratory motion within dynamic SPECT myocardial blood flow (MBF) studies. In addition, PET studies have shown that patient motion can have a significant impact on MBF measurements. The goal of our study was to estimate the prevalence and magnitude of respiratory motion in SPECT MBF studies and determine the impact on measured MBF values. Methods: Data from 25 patients were retrospectively analyzed. SPECT MBF was measured using a one-day rest/stress protocol with Tc99m-tetrofosmin on a pinhole cardiac SPECT camera. The imaging was repeated for each patient at an interval of 17 ± 13 days. Each of the 100 dynamic scans was divided into 19 frames of 9 x 10 sec, 6 x 15sec, 4 x 120sec. For each dynamic frame, the acquired count rate in each 100 msec interval was determined from the listmode data and data were sorted into 10 separate respiratory gates based on this count rate. The gates were reconstructed independently and the images registered using rigid-body translation to determine the motion vector between gates. The original projection data were then reconstructed offline with a 4D iterative algorithm that incorporated the motion vectors to generate a single respiratory-motion-corrected (RMC) image for each dynamic frame. A 3D version of the same algorithm was used to reconstruct images without motion correction (NMC). The maximum distance between any two gates, averaged over the last 4 frames (between 3 and 11 min after tracer injection), was used as a measure of patient motion. Three patients with large motion (≥1cm, mean 1.5cm) at both rest and stress, and 3 patients with minimal motion (≤ 3mm), were processed to determine MBF using 4DM reserve (INVIA) for both NMC and RMC reconstructions. Results: The average patient motion measured in the dynamic studies was 6.7mm ± 4.0 mm (maximum 25mm). Of the 100 scans, 17 had motion >10mm. The patient motion in the 1st study was not significantly different from that in the 2nd study (6.9mm vs 6.4mm, p=0.14) but was correlated between the 2 studies (r=0.86). The difference in motion between the two studies averaged -0.5mm ± 2.4mm (max difference = 9mm). Motion at stress was slightly, but significantly, larger than at rest (7.1mm vs 6.3mm, p=0.04) and correlated between rest and stress (r=0.76). For the 3 low-motion rest-stress studies, the average difference in global flow between NMC and RMC reconstructions was 10%, 14% and 8.5% for rest MBF, stress MBF, and myocardial flow reserve (MFR) respectively. The corresponding average regional values in the LAD, LCx, and RCA territories were 11%, 16%, and 11%. These differences are consistent with previously measured intra-user variation of ~15%. For the 3 high-motion rest-stress studies, the average difference in global flow between NMC and RMC reconstructions was 37%, 21% and 17% for rest MBF, stress MBF, and myocardial flow reserve (MFR) respectively. The corresponding average regional values in the LAD, LCx, and RCA territories were 39%, 23%, and 28%. Conclusions: Respiratory motion caused displacement of the heart by >10mm in 17% of dynamic scans. Respiratory motion can substantially alter SPECT MBF and MFR values.
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 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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 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 ».