Clinical Application of Lumbar Spine MRI Based on Deep Learning Algorithm
Objective To compare the image quality of lumber fast spin echo sequence images and original images by deep learning reconstruction algorithm.Methods 130 patients with low back pain were analyzed retrospectively.Lumbar two dimensional(2D)fast spin echo(FSE)sequences was performed with 3.0T MRI,including T1-Weighted Image(T1WI),T2-Weighted Image(T2WI),T2-Weighted Fat Suppressed Image(T2WI-FS)sequence and transverse T2WI sequence.Once a scan is completed,the DL reconstruction algorithm engine will generate the original image(FSE0)and the image processed with DLR(FSEDL)according to the acceleration protocol.The overall image quality,clarity and anatomical structure of all sequence images were subjectively scored by two radiologists.And the consistency of the scores between the two physicians was tested.Objective quantitative image quality analysis was evaluated by calculating SNR and CNR of lumbar vertebrae and intervertebral discs respectively.Results The total scanning time was 3 minutes and 41 seconds.The SNR and CNR of lumbar vertebrae and intervertebral disc in all FSEDL images were higher than those in FSE0 images.And FSEDL images had higher overall image quality and sharpness,and the anatomical structure was more clearly displayed(P＜0.05).The excellent consistency between the two raters was between 0.754 and 0.923.Conclusion In conventional lumbar 2D FSE sequence imaging,using the deep learning reconstruction technique can improve the overall image quality while scanning within 4 minutes.
Lumbar SpineDeep Learning ReconstructionMagnetic Resonance ImagingSignal to Noise RatioContrast Noise Ratio
万方数据
维普
