诊断学理论与实践 ›› 2024, Vol. 23 ›› Issue (06): 594-601.doi: 10.16150/j.1671-2870.2024.06.006

• 论著 • 上一篇    下一篇

深度学习重建技术在改善磁共振冠状位T1WI显示垂体神经内分泌肿瘤图像质量中的研究

张慧慧, 方姝, 吴梦雄, 刘方韬, 贺娜英, 董海鹏(), 严福华   

  1. 上海交通大学医学院附属瑞金医院放射科,上海 200025
  • 收稿日期:2023-02-22 出版日期:2024-12-25 发布日期:2024-12-25
  • 通讯作者: 董海鹏 E-mail:dhp40427@rjh.com.cn

Study on deep learning reconstruction technology in improving image quality of pituitary neuroendocrine tumors in coronal T1WI magnetic resonance image

ZHANG Huihui, FANG Shu, WU Mengxiong, LIU Fangtao, HE Naying, DONG Haipeng(), YAN Fuhua   

  1. Department of Radiology, Ruijin Hospital, Shanghai Jiao tong University School of Medicine, Shanghai 200025, China
  • Received:2023-02-22 Published:2024-12-25 Online:2024-12-25

摘要:

目的: 探讨基于深度学习(deep learning, DL)重建技术的薄层垂体T1WI脂肪抑制(fat suppression, FS)冠状位增强序列,在改善显示垂体神经内分泌肿瘤图像质量中的作用。方法: 前瞻性连续纳入2023年6月至2024年6月诊断或疑似垂体病变患者46例,共有垂体神经内分泌肿瘤病灶40个。所有患者均行薄层垂体DL T1WI FS冠状位增强扫描,并保留未应用DL的原始重建(origin reconstruction, OR)图像,根据重建方式将图像分为DL组和OR组。由2名神经放射诊断医师采用双盲法,分别对2组的图像质量(均匀度、锐利度、伪影、垂体结构辨识度、病灶辨识度、整体质量6个方面)进行主观评估(采用李克特五分量表法),客观评价包括测量并计算垂体神经内分泌肿瘤瘤体和垂体无病灶区的信噪比(signal noise ratio,SNR)和对比噪声比(contrast noise,CNR)。2组图像质量评分差异比较采用Wilcoxon秩和检验,采用组内相关系数ICC(intra-class correlation coefficient,ICC)分别评估2名医师主客观图像测量结果的一致性。结果: DL和OR 2组图像主、客观质量评分的观察者间ICC值均大于0.81,呈极高度一致。在主观图像质量评价方面,DL组和OR组图像的图像均匀度评分分别为4.33(3,5)分、3.73(3,4)分,锐利度评分分别为4.25(3,5)分、3.50(3,4)分,伪影评分分别为4.35(4,5)分、2.95(2,4)分,垂体结构辨识度评分分别为4.38(3,5)分、3.35(2,5)分,病灶辨识度评分分别为4.6(3,5)分、3.15(2,4)分,整体质量评分分别为4.30(4,5)分、2.63(2,3)分,DL组均高于OR组,差异有统计学意义(P均<0.001)。客观图像质量方面,DL组和OR组垂体瘤的SNR分别为26.96(18.10,34.15)、16.51(11.24,20.65),CNR分别为11.30(6.74,19.72)、4.34(3.07,6.00),垂体的SNR分别为38.36(31.93,47.03)、17.02(15.49,20.51),CNR分别29.89(23.28,39.75)、18.44(16.61,24.56),DL组均高于OR组,差异有统计学意义(P均<0.001)。2名医师评估所得的主客观指标一致性较好。结论: 基于DL的T1WI FS冠状位增强序列,在确保图像空间分辨率的情况下,可明显改善垂体神经内分泌肿瘤图像质量,提升图像SNR及CNR,为临床诊疗提供精准的影像学依据。

关键词: 垂体, 神经内分泌肿瘤, 深度学习, 图像重建, 磁共振成像

Abstract:

Objective To investigate the role of thin-layer pituitary T1WI fat suppression (FS) coronal enhanced sequences based on deep learning (DL) reconstruction technology in improving image quality for pituitary neuroendocrine tumors. Methods From June 2023 to June 2024, 46 patients diagnosed or suspected of having pituitary lesions were prospectively and consecutively enrolled, with a total of 40 pituitary neuroendocrine tumor lesions identified. All patients underwent thin-layer pituitary DL T1WI FS coronal enhanced scanning. Original reconstruction (OR) images without DL application were retained, and the images were divided into DL and OR groups according to the reconstruction method. Two neuroradiologists, using a double-blind method, subjectively evaluated (using the five-point Likert scale) the image quality of the two groups in six aspects: uniformity, sharpness, artifact, pituitary structure identification, lesion identification, and overall quality. Objective evaluation included measuring and calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pituitary neuroendocrine tumor and tumor-free area. The differences in image quality scores between the two groups were compared using the Wilcoxon rank-sum test. The intra-class correlation coefficient (ICC) was used to assess the consistency of subjective and objective image measurement results of both doctors. Results The ICC values of subjective and objective image quality scores between the DL and OR groups were all greater than 0.81, indicating extremely high consistency. In terms of subjective image quality evaluation, the image uniformity of the DL and OR groups was 4.33 (3, 5) and 3.73 (3, 4), respectively. Sharpness was 4.25 (3, 5) and 3.50 (3, 4), artifact was 4.35 (4, 5) and 2.95 (2, 4), pituitary structure identification was 4.38 (3, 5) and 3.35 (2, 5), lesion identification was 4.6 (3, 5) and 3.15 (2, 4), and overall quality was 4.30 (4, 5) and 2.63 (2, 3), respectively. The DL group showed higher scores than the OR group, and the differences were statistically significant (all P<0.001). For objective image quality evaluation, the SNRs of pituitary tumors in the DL and OR groups were 26.96 (18.10, 34.15) and 16.51 (11.24, 20.65), respectively, while the CNRs were 11.30 (6.74, 19.72) and 4.34 (3.07, 6.00), respectively. The SNRs of the pituitary in the DL and OR groups were 38.36 (31.93, 47.03) and 17.02 (15.49, 20.51), while the CNRs were 29.89 (23.28, 39.75) and 18.44 (16.61, 24.56), respectively. The DL group had higher scores than the OR group, and the differences were statistically significant (all P<0.001). The subjective and objective indicators evaluated by the two doctors showed good consistency. Conclusions The T1WI FS coronal enhanced sequences based on DL can significantly improve the image quality of pituitary neuroendocrine tumors and enhance the SNR and CNR of the images while ensuring the spatial resolution of the images, providing accurate imaging support for clinical diagnosis and treatment.

Key words: Pituitary, Neuroendocrine tumor, Deep learning, Image reconstruction, Magnetic resonance imaging

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