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Craniocerebral Magnetic Resonance Imaging Features of Benign Paroxysmal Positional Vertigo under Artificial Intelligence Algorithm and the Correlation with Cerebrovascular Disease



. 2022 Apr 26;2022:8952355.


doi: 10.1155/2022/8952355.


eCollection 2022.

Affiliations

Item in Clipboard

Hailong Xue et al.


Contrast Media Mol Imaging.


.

Abstract

This research aimed to discuss the characteristics of benign paroxysmal positional vertigo (BPPV) and the correlation with cerebrovascular disease. An artificial intelligence algorithm under a parallel dual-domain concatenated convolutional neural network (PDDC-CNN) was proposed to process the images of magnetic resonance imaging (MRI). MRI, magnetic resonance angiography (MRA), and susceptibility weighted imaging (SWI) were performed on all 60 research objects with a 3.0 MRI scanner. The number of cases with cerebral microbleeds (CMBs), SWI image display of small veins, the number of lacunar infarctions, vertebral artery dominance, and vertebrobasilar morphology were observed in the two groups. The number of lacunar infarctions was 2.400 ± 3.358 and 0.672 ± 0.251, respectively, in the BBPV group with 30 cases and the control group with the other 30 cases. The positive rates of CMBs on SWI images were 48% and 27% in the BBPV group and the control group, respectively, and the average CMBs were counted as 1.670 ± 2.326 and 0.487 ± 0.865. CMBs were shown as round or oval lesions of conventional sequence deletion in the images with a diameter of less than 1.5 cm. SWI images of the BBPV group showed a significant increase in intracerebral small veins compared to those of the control group. The curvature of the vertebrobasilar artery in the BBPV group was significantly higher than that in the control group, and the curvature of the basilar artery was slightly higher than that in the control group. In conclusion, the MRI features of BPPV patients were related to their own microvascular lesions closely, and it was speculated that the cerebrovascular factors might play a dominant role in the early onset of BPPV.

Conflict of interest statement

The authors declare no conflicts of interest.

Figures



Figure 1

PDDC-CNN under deep learning and compressed sensing. The direction of the arrows indicated the direction of data flow.


Figure 2


Figure 2

MI and SWI images of CMBs. The arrows pointed out CMBs. (a) The MI image. (b) The SWI image showing low-density signals. (c) The SWI image with clearer intracranial veins processed by the minimum density projection technology.


Figure 3


Figure 3

Comparison of the number of lacunar infarctions in the BPPV group and the control group. The difference was statistically significant compared with the result of the BPPV group (P < 0.05).


Figure 4


Figure 4

Comparison of the positive rate of CMBs between the BPPV group and the control group.


Figure 5


Figure 5

Comparison of the detection rate of CMBs by conventional MRI and SWI sequence.


Figure 6


Figure 6

MI and SWI images of CMBs. (a–d) The image of T1WI, T2WI, T2-FLAIR, and DWI (b = 1000); these conventional MRI images showed no abnormality. (e) MI and (f) SWI image showed low-signal CMBs.


Figure 7


Figure 7

Comparative observation of the vertebral artery dominance in a total of 27 cases in the BPPV group (a) and the control group (b).


Figure 8


Figure 8

Morphological analyses of the vertebrobasilar artery.


Figure 9


Figure 9

Basilar artery morphology. (a–g) The C type, S type, reverse C type, normal vertebral artery dominance, normality, left side dominance, and right side dominance.

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