Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI


    Hyunsuk Yoo1, Roh-Eul Yoo1, Seung Hong Choi1, 2, 3, Inpyeong Hwang1, Ji Ye Lee1, June Young Seo1, Seok Young Koh1, Kyu Sung Choi1, Koung Mi Kang1 & Tae Jin Yun1


    1. Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
    2. Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea
    3. School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea



    European radiology (2023)



    To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)–based image reconstruction for degenerative lumbar spine diseases.

    Materials and Methods

    Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared.


    The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05).


    DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases.

    Clinical relevance statement

    The deep learning (DL)–based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging.

    Key Points


        • By using deep learning (DL)–based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol.

        • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures.

        • The average radiologist’s sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.