Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease
Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease
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DOI：
10.1002/jmri.28593
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发表时间：
2023-01-16
影响因子：
4.4
通讯作者：
Prince, Martin R.
中科院分区：
文献类型：
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作者：
Dev, Hreedi;Zhu, Chenglin;Prince, Martin R.
背景：肾总体积(TKV)是评价肾功能的重要生物标志物，尤其是常染色体显性遗传性多囊肾病(ADPKD)。然而，从单个MRI脉冲序列测量TKV的重复性有限，+/-类似于5%，类似于ADPKD的年肾脏增长率。目的：通过扩展人工智能算法，在轴位和冠状面的T1加权、T2加权和稳态自由进动(SSFP)序列上自动分割肾脏，并对测量结果进行平均，以提高MRI上TKV测量的可重复性。研究类型：回溯性培训、前瞻性测试。研究对象：397例ADPKD患者(ADPKD356例，非ADPKD41例)，75%接受训练，25%接受验证，40例ADPKD患者接受测试，17例ADPKD患者进行重复性评估。场强/序列：1.5T和3T的T2加权单次激发快速自旋回波(T2)、SSFP和T1加权3D破坏梯度回波(T1)。评估：对所有序列的图像进行2D U-Net分割算法训练。在间隔1-3周的两次MRI检查中，五名观察者在T2轴上手动测量每个肾脏体积，并在所有序列和图像平面方向上使用模型辅助分割，以评估重复性。记录人工和模型辅助的分割次数。统计检验：Bland-Altman，Schapiro-Wilk(正态评估)，Pearson卡方(分类变量)；骰子相似系数，类间相关系数，和谐相关系数用于分析TKV的重复性。P值<；0.05被认为具有统计学意义。结果：在17例ADPKD患者中，模型辅助分割轴位T2图像的速度明显快于手动分割(2：49分钟比11：34分钟)，而TKV的绝对百分比在扫描1和扫描2之间没有显著差异(5.9%比5.3%，P=0.88)。对于其他序列，模型辅助分割的绝对百分比差异分别为5.5%(轴向T1)、4.5%(轴向SSFP)、4.1%(冠状SSFP)和3.2%(冠状T2)。所有五个模型辅助分割的平均测量值显著地将绝对百分比差异降低到2.5%，在排除异常值后进一步提高到2.1%。数据结论：利用深度学习模型辅助分割，在冠状面和轴面多个MRI脉冲序列上测量TKV是可行的，可以将ADPKD的TKV测量重复性提高2倍以上。
Background: Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, +/- similar to 5%, similar to ADPKD annual kidney growth rates. Purpose: To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements. Study Type: Retrospective training, prospective testing. Subjects: Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility. Field Strength/Sequence: T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T. Assessment: 2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded. Statistical Tests: Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant. Results: In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier. Data Conclusion: Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD.