Value of T2 Flair sequence based on deep learning in improving image quality of white matter hyperintensities
Objective:To explore the application value of T2 fluid-attenuated inversion recovery(Flair)sequence based on deep learning reconstruction(DLR)algorithm in improving the image quality of white matter hyperintensities(WMH).Materials and Methods:Fifty patients with suspected cerebral ischemic disease were prospectively recruited.Both the conventional T2 FLAIR sequence and the high-resolution T2 Flair sequence,utilizing the DLR algorithm,were conducted on the patients.The DLR Flair sequence selected for this study retained the pre-processed images that have undergone conventional reconstruction algorithms without DLR processing(referred to as Pre-DLR).Subjective evaluations were performed on three groups of images using a 4-point scale to assess image sharpness,gray-white matter contrast,cerebrospinal fluid-choroid plexus contrast,WMH display,and overall image quality.Comparisons were made between the number of WMH detections,the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of WMH in three sets of images.Results:In the subjective evaluation,the DLR group of images scored higher than the conventional group and Pre-DLR group in terms of image sharpness,gray-white matter contrast,cerebrospinal fluid-choroid plexus contrast,WMH display,and overall image quality(all P＜0.05).In terms of WMH counting,the DLR group identified a higher number of WMHs than the conventional group(P＜0.05),while there was no statistical difference with the Pre-DLR group.In the objective evaluation,the DLR group showed higher SNR and CNR of WMH compared to the conventional group and Pre-DLR group(all P＜0.05).Conclusions:Compared to conventional sequences,the high-resolution T2 Flair sequence combined with the DLR algorithm can improve WMH image quality and detect more subtle WMH lesions without increasing scan time.
white matter hyperintensitiesmagnetic resonance imagingdeep learningreconstruction algorithmhigh resolutionimage quality
国家科技期刊平台
NSTL
万方数据
