RESUMO
Background: Magnetic resonance imaging (MRI) has the potential in assessing the inflammation of perivascular adipose tissue (PVAT) due to its excellent soft tissue contrast. However, evidence is lacking for the association between carotid PVAT measured by MRI and carotid vulnerable atherosclerotic plaques. This study aimed to investigate the association between signal intensity of PVAT and vulnerable plaques in carotid arteries using multi-contrast magnetic resonance (MR) vessel wall imaging. Methods: In this cross-sectional study, a total of 104 patients (mean age, 64.9±7.0 years; 86 men) with unilateral moderate-to-severe atherosclerotic stenosis referred to carotid endarterectomy (CEA) were recruited from April 2018 to December 2020 at Department of Neurosurgery of Peking University Third Hospital. All patients underwent multi-contrast MR vessel wall imaging including time-of-flight (ToF) MR angiography, black-blood T1-weighted (T1w) and T2-weighted (T2w) and simultaneous non-contrast angiography and intraplaque hemorrhage (IPH) imaging sequences. Patients with contraindications to endarterectomy or MRI examinations were excluded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of PVAT were measured on ToF images and vulnerable plaque characteristics including IPH, large lipid-rich necrotic core (LRNC), and fibrous cap rupture (FCR) were identified. The SNR and CNR of PVAT were compared between slices with and without vulnerable plaque features using Mann-Whitney U test and their associations were analyzed using the generalized linear mixed model (GLMM). Results: Carotid artery slices with IPH (30.93±14.56 vs. 27.34±10.02; P<0.001), FCR (30.35±13.82 vs. 27.53±10.37; P=0.006), and vulnerable plaque (29.15±12.52 vs. 27.32±10.05; P=0.016) had significantly higher value of SNR of PVAT compared to those without. After adjusting for clinical confounders, the SNR of PVAT was significantly associated with presence of IPH [odds ratio (OR) =0.627, 95% confidence interval (CI): 0.465-0.847, Puncorr=0.002, PFDR=0.016] and vulnerable plaque (OR =0.762, 95% CI: 0.629-0.924, Puncorr=0.006, PFDR=0.020). However, no significant association was found between the CNR of PVAT and presence of vulnerable plaque features (all P>0.05). Conclusions: The SNR of carotid artery PVAT measured by ToF MR angiography is independently associated with vulnerable atherosclerotic plaque features, suggesting that the signal intensity of PVAT might be an effective indicator for vulnerable plaque.
RESUMO
BACKGROUND: Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. RESULTS: Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. CONCLUSIONS: This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.
