Application value of T2 fluid-attenuated inversion recovery sequence based on deep learning in static lacunar infarction

Abstract

Background Regular monitoring of static lacunar infarction (SLI) lesions plays an important role in preventing disease development and managing prognosis. Magnetic resonance imaging is one method used to monitor SLI lesions. Purpose To evaluate the image quality of the T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence using artificial intelligence-assisted compressed sensing (ACS) in detecting SLI lesions and assess its clinical applicability. Methods A total of 42 patients were prospectively enrolled and scanned by T2-FLAIR. Two independent readers reviewed the images acquired with accelerated modes 1D (acceleration factor 2) and ACS (acceleration factors 2, 3, and 4). The overall image quality and lesion image quality were analyzed, as were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and number of lesions between groups. Results The subjective assessment of overall brain image quality and lesion image quality was consistent between the two readers. The lesion display quality and the overall image quality were better with the traditional 1D acceleration method than with the ACS accelerated method. There was no significant difference in the SNR of the lacunar infarction in the images between the groups. The CNR of the images with the 1D acceleration mode was significantly lower than that of images with the ACS acceleration mode. Images with the 1D, ACS2, and ACS3 acceleration modes showed no significant differences in terms of detecting lesions but scan time can be reduced by 40% (1D vs. ACS3). Conclusion ACS acceleration mode can greatly reduce the scan time. In addition, the images have good SNR, high CNR, and strong SLI lesion detection ability.

Deep Learning Aided Neuroimaging and Brain Regulation

“…The utilization of deep learning in molecular diagnosis, prognosis, and treatment monitoring has resulted in the creation of a structured resource for radiogenomic analysis of brain or central nervous system diseases. Besides greatly reducing the scan time of neuroimaging methods like MRI and PET/CT [13], the deep learning aided medical images acquired better signal to noise ratio, higher contrast-to-noise ratio, and stronger brain or central nervous system disease lesion detection ability. Therefore, radiomics was thought to be the bridge between medical imaging and personalized medicine [14], which is a quantitative approach to medical imaging that involves the extraction and analysis of large amounts of quantitative data from medical images such as MRI and PET/CT.…”