Koo So Jung, Yoo Roh-Eul, Choi Kyu Sung, Lee Kyung Hoon, Lee Han Byeol, Shin Dong-Joo, Yoo Hyunsuk, Choi Seung Hong
From the Department of Radiology (S.J.K., R.-E.Y., K.S.C., H.B.L., D.-J.S., H.Y., S.H.C.), Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
From the Department of Radiology (S.J.K., R.-E.Y., K.S.C., H.B.L., D.-J.S., H.Y., S.H.C.), Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
AJNR Am J Neuroradiol. 2025 Apr 2;46(4):750-757. doi: 10.3174/ajnr.A8567.
Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed to prospectively compare the image quality and diagnostic performance in evaluating cervical degenerative spine diseases and myelopathy between conventional cervical MRI and accelerated cervical MRI with a commercially available vendor-neutral DL-based reconstruction.
Fifty patients with degenerative cervical spine disease or myelopathy underwent both conventional cervical MRI and accelerated cervical MRI by using a DL-based reconstruction operating within the DICOM domain. The images were evaluated both quantitatively, based on SNR and contrast-to-noise ratio (CNR), and qualitatively, by using a 5-point scoring system for the overall image quality and clarity of anatomic structures on sagittal T1WI, sagittal contrast-enhanced (CE) T1WI, and axial/sagittal T2WI. Four radiologists assessed the sensitivity and specificity of the 2 protocols for detecting degenerative diseases and myelopathy.
The DL-based protocol reduced MRI acquisition time by 47%-48% compared with the conventional protocol. DL-reconstructed images demonstrated a higher SNR on sagittal T1WI ( = .046) and a higher CNR on sagittal T2WI ( = .03) than conventional images. The SNR on sagittal T2WI and the CNR on sagittal T1WI did not significantly differ ( > .05). DL-reconstructed images had better overall image quality on sagittal T1WI ( < .001), sagittal T2WI (Dixon in-phase or TSE) ( < .001), and sagittal T2WI (Dixon water-only) ( = .013) and similar image quality on axial T2WI and sagittal CE T1WI ( > .05). DL-reconstructed images had better clarity of anatomic structures ( values were < .001 for all structures, except for the neural foramen [ = .024]). DL-reconstructed images had a higher sensitivity for detecting neural foraminal stenosis ( = .005) and similar sensitivities for diagnosing other degenerative spinal diseases and myelopathy ( > .05). The specificities for diagnosing degenerative spinal diseases and myelopathy did not differ between the 2 images ( > .05).
The accelerated cervical MRI reconstructed with a vendor-neutral DL-based reconstruction algorithm did not compromise image quality and had higher or similar diagnostic performance for diagnosing cervical degenerative spine diseases and myelopathy compared with the conventional protocol.
基于深度学习（DL）的重建技术能够提高在短扫描时间内采集的磁共振成像（MRI）质量。我们旨在前瞻性地比较传统颈椎MRI与采用市售的基于供应商中立的DL重建技术的加速颈椎MRI在评估颈椎退行性脊柱疾病和脊髓病时的图像质量及诊断性能。
50例患有颈椎退行性疾病或脊髓病的患者同时接受了传统颈椎MRI和采用在DICOM域内运行的基于DL的重建技术的加速颈椎MRI检查。基于信噪比（SNR）和对比噪声比（CNR）对图像进行定量评估，并使用5分制评分系统对矢状面T1加权成像（T1WI）、矢状面对比增强（CE）T1WI以及轴位/矢状面T2WI上的整体图像质量和解剖结构清晰度进行定性评估。四位放射科医生评估了两种方案在检测退行性疾病和脊髓病方面的敏感性和特异性。
与传统方案相比，基于DL的方案将MRI采集时间缩短了47%-48%。DL重建图像在矢状面T1WI上显示出更高的SNR（P = 0.046），在矢状面T2WI上显示出更高的CNR（P = 0.03）。矢状面T2WI上的SNR和矢状面T1WI上的CNR差异无统计学意义（P > 0.05）。DL重建图像在矢状面T1WI（P < 0.001）、矢状面T2WI（Dixon同相位或快速自旋回波序列[TSE]）（P < 0.001）和矢状面T2WI（Dixon纯水图）（P = 0.013）上具有更好的整体图像质量，在轴位T2WI和矢状面CE T1WI上具有相似的图像质量（P > 0.05）。DL重建图像在解剖结构清晰度方面表现更好（除神经孔外，所有结构的P值均< 0.001，神经孔的P = 0.024）。DL重建图像在检测神经孔狭窄方面具有更高的敏感性（P = 0.005），在诊断其他退行性脊柱疾病和脊髓病方面具有相似的敏感性（P > 0.05）。两种图像在诊断退行性脊柱疾病和脊髓病方面的特异性无差异（P > 0.05）。
与传统方案相比，采用基于供应商中立的DL重建算法重建的加速颈椎MRI在诊断颈椎退行性脊柱疾病和脊髓病时，图像质量未受影响，且具有更高或相似的诊断性能。
