Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting
Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting
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DOI：
10.1148/radiol.2020200723
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发表时间：
2021-01-01
期刊：
影响因子：
19.7
通讯作者：
Lebel, Marc R.
中科院分区：
文献类型：
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作者：
Kim, Minjae;Kim, Ho Sung;Lebel, Marc R.
背景：实现高空间分辨率垂体MRI是具有挑战性的，因为图像噪声和空间分辨率之间的权衡。基于深度学习的MRI重建能够实现图像去噪，边缘清晰，伪影减少，从而提高薄层MRI的图像质量。目的：评估1 mm层厚MRI与基于深度学习的重建（DLR）的诊断性能（以下称为1 mm MRI+DLR）与3 mm层厚MRI相比（以下称为3 mm MRI），用于在评价术后垂体瘤时识别残留肿瘤和海绵窦侵犯。材料和方法：这项单机构回顾性研究包括65名患者（平均年龄6标准差，54岁6 10; 26名女性）在2019年8月至10月期间接受了包括3 mm MRI和1 mm MRI+DLR在内的联合成像方案，用于垂体腺瘤的术后评价。通过使用所有可用的成像资源、临床病史、实验室检查结果、手术记录和病理报告，建立正确诊断的参考标准。3 mm、1 mm层厚MRI无DLR的诊断性能由两名阅片者对1 mm MRI（以下简称1 mm MRI）和1 mm MRI 1DLR用于识别残留肿瘤和海绵窦浸润进行评价，并在两种方案之间进行比较。1 mm MRI+DLR在识别残留肿瘤方面的性能与3 mm MRI相当（受试者工作特征曲线下面积[AUC]分别为0.89-0.92和0.85-0.89; P> 0.09）。在识别海绵窦浸润方面，1 mm MRI+DLR的诊断性能高于3 mm MRI（AUC分别为0.95-0.98 vs 0.83-0.87; P.38）。1 mm MRI+DLR诊断20例肿瘤残留，14例海绵窦受侵，3 mm MRI未诊断。在垂体腺瘤的术后评估中，1 mm层厚MRI和基于深度学习的重建显示出比3 mm层厚MRI更高的诊断性能。在海绵窦侵犯的识别中，层厚为2.0mm的MRI的诊断性能与层厚为3 mm的MRI在识别残留肿瘤方面的诊断性能相当。(C)RSNA，2020年。
Background: Achieving high-spatial-resolution pituitary MRI is challenging because of the trade-off between image noise and spatial resolution. Deep learning-based MRI reconstruction enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice MRI.Purpose: To assess the diagnostic performance of 1-mm slice thickness MRI with deep learning-based reconstruction (DLR) (hereafter, 1-mm MRI+DLR) compared with 3-mm slice thickness MRI (hereafter, 3-mm MRI) for identifying residual tumor and cavernous sinus invasion in the evaluation of postoperative pituitary adenoma.Materials and Methods: This single-institution retrospective study included 65 patients (mean age 6 standard deviation, 54 years 6 10; 26 women) who underwent a combined imaging protocol including 3-mm MRI and 1-mm MRI+DLR for postoperative evaluation of pituitary adenoma between August and October 2019. Reference standards for correct diagnosis were established by using all available imaging resources, clinical histories, laboratory findings, surgical records, and pathology reports. The diagnostic performances of 3-mm MRI, 1-mm slice thickness MRI without DLR (hereafter, 1-mm MRI), and 1-mm MRI1DLR for identifying residual tumor and cavernous sinus invasion were evaluated by two readers and compared between the protocols.Results: The performance of 1-mm MRI+DLR in the identification of residual tumor was comparable to that of 3-mm MRI (area under the receiver operating characteristic curve [AUC], 0.89-0.92 vs 0.85-0.89, respectively; P>.09). In the identification of cavernous sinus invasion, the diagnostic performance of 1-mm MRI+DLR was higher than that of 3-mm MRI (AUC, 0.95-0.98 vs 0.83-0.87, respectively; P.38). With 1-mm MRI+DLR, residual tumor was diagnosed in 20 patients and cavernous sinus invasion was diagnosed in 14 patients, in whom these diagnoses were not made with 3-mm MRI.Conclusion: In the postoperative evaluation of pituitary adenoma, 1-mm slice thickness MRI with deep learning-based reconstruction showed higher diagnostic performance than 3-mm slice thickness MRI in the identification of cavernous sinus invasion and comparable diagnostic performance to 3-mm slice thickness MRI in the identification of residual tumor. (C) RSNA, 2020.