Evaluation of the Accuracy of Infer Read Software in Measuring the Volume of Pure Ground Glass Nodules in the Lung
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摘要: 目的:探讨人工智能(AI)肺结节检测软件对肺纯磨玻璃结节(pGGN)体积自动测量的准确性及测量误差的影响因素。方法:选择2021年1月1日至31日在本院行常规胸部CT扫描的患者90例,共计170个pGGN病灶。将CT扫描原始数据(含1 mm薄层图像)传送至AI服务器进行肺结节体积自动测量并记录其测量数据;由两名资深胸部影像诊断医师以手动进行pGGN逐层测量相加得出体积数值,并以3次测量平均值作为“金标准”数据与AI测量结果进行比较;并分析pGGN体积大小、位置、毗邻等因素对AI测量误差的影响。采用SPSS 26.0进行统计分析。结果:本研究90例患者中共计170个pGGN,右肺上叶者49个(28.82%),右肺中叶者21个(12.35%),右肺下叶者27个(15.89%),左肺上叶者49个(28.82%),左肺下叶者24个(14.12%)。在pGGN的毗邻关系中,完全位于肺实质内无毗邻关系者82个(48.24%),贴近血管者29个(17.06%),贴近胸膜者59个(34.70%)。①两名观察者之间、同一观察者不同时间点对pGGN手动测量的体积数值均无差异;②对相同pGGN体积的测量,使用AI自动测量与人工手动测量的结果亦无差异,且二者相关性很高(r=0.981),一致性也很高(ICC值为0.987);③pGGN的体积大小、发生位置、毗邻关系对AI体积测量的误差均无统计学意义。结论:InferRead肺结节检测软件对肺pGGN三维体积测量具有良好的准确性,可适用于临床肺结节诊断与相关研究。Abstract: Objective: To discuss the accuracy of the automatic measurement of the volume of pure ground glass nodules (pGGN) by the artificial intelligence pulmonary nodule detection software and the influencing factors of the measurement error. Methods: 170 pGGNs from 90 patients who underwent routine chest CT scan from January 1 to 31, 2021 in our hospital were selected in this retrospective study. The original CT scan data (including 1 mm thin-slice images) was sent to the AI server of Inference Technology to automatically measure the volume of lung nodules and record the measurement data. Two senior chest imaging diagnosticians manually carried out pGGNs layer-by-layer measurement and added the volume value, then took the average of three measurements as the "gold standard" data to compare with the AI measurement results, and then analyzed the influence of pGGN location, size, proximity and other factors on the AI measurement error. SPSS 26.0 was used for statistical analysis. Results: Among the total 170 pGGNs in the 90 patients in this study, 49 (28.82%) were in the right upper lobe, 21 (12.35%) were in the right middle lobe, 27 (15.88%) were in the right lower lobe, and left upper lobe 49 patients (28.82%) were involved, and 24 patients were in the lower lobe of the left lung (14.12%). Among the adjacent relationships of pGGN, 82 (48.24%) were completely located in the lung parenchyma without adjacency, 29 (17.06%) were close to the blood vessel, and 59 (34.70%) were close to the pleura. (1) There was no statistically significant difference in the volume values of pGGNs between two observers and the same observer at different time points. (2) For the measurement of the same pGGN, there was no statistically significant difference between the results of automatic measurement by AI and manual measurement and the correlation between the two was quite high (r=0.981), and the agreement was also very high (ICC value is 0.987). (3) The size, location, and adjacent relationship of pGGN lesions were not statistically significant for the error of AI volume measurement. Conclusions: The InferRead lung nodule measurement software shows high accuracy in the measurement of lung pGGN three-dimensional volume, which can be applied in clinical lung nodule diagnosis and related research. (2) For the measurement of the same pGGN volume, there was no difference between the results of automatic measurement by AI and manual measurement, and the correlation between them was quite high (r = 0.981), and the consistency was high (ICC value is 0.987). (3) The volume size, occurrence position and adjacent relationship of pGGN have no statistical significance on the error of AI volume measurement. Conclusion: The InferRead lung nodule detection software shows high accuracy in measuring the three-dimensional volume of lung pGGN, and can be applied to clinical diagnosis and related research of lung nodules.
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Keywords:
- artificial intelligence /
- ground-glass nodule /
- volume /
- accuracy
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西湖凹陷斜坡带平湖组沉积环境复杂,主要发育三角洲潮坪相,储层横向变化快;同时,目的层段0.5~2 m薄煤广泛发育,地震强反射特征明显[1-2],这对于地震信息驱动的储层反演易引起岩性假象风险,淹没砂岩真实地震响应,使得富煤环境下储层预测难度极大。
针对强反射屏蔽相邻储层信号问题,许多学者开展了相关研究和讨论,主要包括多子波分解与重构、匹配追踪及压缩感知等技术。张军华等[3]利用多子波分解重构方法对胜利探区5号桩油田T2强反射进行剥离,提高了储层预测精度;秦雪霏等[4]利用多子波分解与重构技术对大牛地气田三维地震资料开展分频段信号重组,凸显了储层有效反射信息;Wang[5-6]提出了基于自由多尺度的匹配追踪去强屏蔽方法,利用地震时频谱分解剥离强反射,随后,采用多道匹配追踪算法解决横向连续性问题;朱博华等[7]详细分析了匹配追踪算法中子波控制参数选取问题,提出利用能量增长率作为参数优选,并较好地刻画了河道边界及砂体展布;李海山等[8-9]利用匹配追踪算法分离了煤层强反射,在此基础上,通过叠前反演方法对含气层进行了有效识别;张在金等[10]将沿层信息引进匹配追踪算法,实现了强屏蔽剥离,并用低频伴影现象验证了去强屏蔽效果;何峰等[11]提出一种地震约束的井控匹配追踪去强反射技术,优化改进了匹配子波控制参数。压缩感知去强屏蔽上,张云银等[12]联合“钉型”子波与压缩感知技术有效地去除了渤海湾盆地济阳坳陷渤南地区强反射背景,突出了目的层段砂砾岩地震响应特征;张军华等[13]提出基于压缩感知的反射系数域沿层L2范数约束的去强屏蔽方法,一定程度上解决了去强屏蔽损失弱反射信号问题。
为了消除薄煤强反射对储层弱有效信号的影响,提高平湖组储层预测精度,本文提出一种基于AVO信息约束的匹配追踪去煤层强反射技术,通过截距减去梯度(P - G)精细定位煤层位置,并将该“先验信息”作为后续快速匹配追踪分解的原始信号,实现煤层精细定位及解耦,进而解决平湖组富煤环境储层精细刻画难问题。
1. 煤层识别
由于煤层与砂岩弹性阻抗差异大,在地震剖面上易引起强反射,这对于上下邻近储层影响较大,使得砂岩有效信息淹没于强反射之中;同时,平湖组埋深约4000 m,受地层压实等影响,高阻抗砂岩地震表现为强反射特征,给煤层识别带来一定干扰,进而影响富煤环境下储层精细刻画。
常见砂岩AVO类型主要包含3种:Ⅲ 类AVO低阻抗储层、Ⅱ类AVO近零阻抗差储层及Ⅰ类AVO高阻抗储层[14-15]。结合靶区实钻测井数据,对不同类型砂岩和煤层进行AVO曲线分析(上下围岩均为泥岩),不同岩性弹性参数见表1。
表 1 靶区不同岩性弹性参数Table 1. Elastic parameters of different lithologies in the target area岩性 Vp/(m/s) Vs/(m/s) 密度/(g/cm3) 纵波阻抗/
((m/s)·(g/cm3))泥岩 4150 2220 2.65 10998 Ⅲ 类砂岩 3500 2375 2.40 8400 Ⅱ类a型砂岩 4600 2750 2.49 11454 Ⅱ类b型砂岩 4300 2610 2.47 10621 Ⅰ类砂岩 5300 3250 2.55 13515 煤层 2900 1500 2.00 5800 图1所示为3种类型AVO砂岩与煤层的AVO曲线,分析可知,低阻抗、近零阻抗差及高阻抗砂岩的截距随着阻抗差异有正有负,但其AVO斜率均表现为负值,即不同类型砂岩顶界面均对应负梯度;与之相反,煤层AVO斜率表现为正值,即煤层顶界面的AVO梯度正值较大,且负截距特征明显。不同类型砂岩和煤层的截距、梯度值如表2所示。
表 2 靶区不同岩性截距P、梯度G及P–G值Table 2. Intercept P, gradient G, and P–G of different lithologies in the target area岩性 截距P 梯度G P-G Ⅲ 类砂岩 -0.135 -0.123 -0.012 Ⅱ类a型砂岩 0.020 -0.183 0.203 Ⅱ类b型砂岩 -0.017 -0.147 0.130 Ⅰ类砂岩 0.103 -0.357 0.460 煤层 -0.301 0.369 -0.670 表2分析可知,Ⅰ类和Ⅲ 类砂岩及煤层截距绝对值较大,分别为0.103、0.135和0.301,煤层截距绝对值最大;梯度上,Ⅰ类砂岩和煤层绝对值较大,分别为0.357和0.369。对比截距、梯度可知,煤层与Ⅰ类砂岩均表现为强振幅特征,由于地震数据频带的带限性,应重点辨别煤层与Ⅰ类砂岩差异,对此,结合不同岩性的截距、梯度特征,提出煤层地震敏感因子P - G,分析不同岩性P - G值可知,相比于单一截距、梯度,P - G属性一定程度上放大了煤层与非煤层差异,煤层的P - G绝对值最大,且极性为负,即振幅绝对值远大于非煤层,极性上与P - G值较大的Ⅰ类砂岩相反。基于此,利用P - G地震属性开展煤层精细定位,为后续匹配追踪去煤层强振幅提供原始信号。
2. 匹配追踪煤层解耦
2.1 基本原理
1993年,Mallat等[16]提出匹配追踪算法(matching pursuit),利用常见子波构建一个过完备时频原子库,将信号投影其中进行迭代自适应分解,表示为一系列时频原子的线性组合,其公式为:
$$ f = \Big\langle f,\,{g_{{\gamma _0}}} \Big\rangle {g_{{\gamma _0}}} + {R^1}f \text{,} $$ (1) 式中,
$ f $ 表示Hilbert空间的初始信号,$\Big\langle f,\,{g_{{\gamma _0}}} \Big\rangle$ 为初始信号与优选原子的内积,$ {R^1}f $ 为迭代产生的残差,其中,$ {R^1}f $ 和$ {g_{{\gamma _0}}} $ 满足正交性,即:$$ {\big{\|}}f{\big{\|}}{^2} = \big| \Big\langle f,\,{g_{{\gamma _0}}} \Big\rangle {\big|^2} + {\big{\|}}{R^1}f{\big{\|}}{^2} 。 $$ (2) 分析可知,
$\Big\langle f,\,{g_{{\gamma _0}}} \Big\rangle {g_{{\gamma _0}}}$ 为$ f $ 的最佳逼近,基于内积$\Big\langle f,\,{g_{{\gamma _0}}} \Big\rangle$ 尽可能大,信号残差尽可能小的原则,优选第一次最佳原子。假设第0次迭代残差为原始信号
$ {R^0}f = f $ ,则第$ n $ 次迭代剩余残差为$ {R^n}f $ ,然后利用内积最大的方式从原子库中优选原子$ {g_{{\gamma _n}}} $ 与上一步残差$ {R^n}f $ 进行最佳匹配,即:$$ {R^n}f = \Big\langle {R^n}f,\,{g_{{\gamma _n}}} \Big\rangle {g_{{\gamma _n}}} + {R^{n + 1}}f \text{,} $$ (3) 其中,
$ {R^{n + 1}}f $ 为$ n + 1 $ 次迭代处理后的残差,且$ {R^{n + 1}}f $ 和$ {g_{{\gamma _n}}} $ 正交。假设进行
$ m $ 次迭代后满足信号分解要求,则信号$ f $ 为:$$ f = \sum\limits_{n = 0}^{m - 1} { \Big\langle {R^n}} f,\,{g_{{\gamma _n}}} \Big\rangle {g_{{\gamma _n}}} + {R^m}f 。 $$ (4) 上述迭代停止原则分两种:一种是分解次数满足设定极大值,另一种为剩余残差符合设定的截止阈值。
2.2 去煤层技术流程
匹配追踪作为一种最优匹配、残差逐级递减的贪婪匹配算法[17],其第1次计算结果为富煤地震输入信号最优匹配,即第1次分解为煤层反射信号,残差信号
$ {R^1}f $ 为去煤层强反射结果,为了精细、高效完成煤层识别及解耦,本次研究利用煤层地震敏感因子P - G实现煤层精细定位,基于此“先验信息”作为匹配追踪初始信号,同时,为了解决算法计算量大、效率较低等问题,利用快速匹配追踪算法[18-19]实现局部最优搜索,最终实现煤层解耦的准确性与高效性。具体流程如下:(1)基于Shuey近似[20]计算地震角道集数据的截距P、梯度G,在此基础上得到煤层地震敏感因子P - G;
(2)分析已钻井煤层段与非煤层段对应的P - G值,确定煤层地震敏感因子阈值并将该阈值位置对应的地震数据作为后续匹配追踪去强振幅的目标处理信号;
(3)过完备原子库构建。常用的原子库有Ricker子波、Gabor子波及Morlet子波,本次研究选择更适用于靶区地震数据的Morlet子波,其函数如下:
$$ M(t) = \exp \Bigg(- \frac{{\ln (2)}}{{{\pi ^2}}}\frac{{w_m^2{{(t - u)}^2}}}{{{\sigma ^2}}}\Bigg)\exp \bigg(i\Big({w_m}(t - u) + \phi \Big)\bigg) \text{,} $$ (5) 其中,
$ w,u,\phi,\sigma $ 分别表示子波频率、时移量、相位及尺度因子。(4)输入地震数据S,计算总道数N及纵向采样点数n,利用Hilbert变换计算地震数据得瞬时振幅、频率及相位;
(5)依据步骤(2)和步骤(4)可以获取煤层初始时移时间
$ {u_0} $ ,初始频率$ {w_0} $ ,初始相位$ {\phi _0} $ 及尺度因子$ {\sigma _0} $ ,其中,尺度因子控制子波时频域宽度,值域范围为0~2,初始值$ {\sigma _0} = 1 $ ;(6)基于煤层4个参数,利用快速匹配追踪分解方法,在一定范围进行局部针对性高效匹配搜索
$ \Big\{{u}_{0}\pm \Delta u,\;{w}_{0}\pm \Delta w,\;{\varphi }_{0}\pm \Delta \varphi ,\;{\sigma }_{0}\pm \Delta \sigma \Big\} $ ,得到残差$ {R^1}f $ 最小时的最优子波参数;(7)将最优子波参数代入式(2)~式(5)小波函数,求取对应振幅,将其与子波相乘得到解耦煤层信号;
(8)对每一道地震中满足煤层P - G阈值的信号重复步骤(5)~步骤(7),完成输入地震数据S的煤层信号解耦,得到煤层响应信号S煤;
(9)地震数据S减去煤层响应数据S煤,得到煤层解耦后地震数据。
2.3 模型试算
为了验证该方法煤层识别、解耦的有效性,依据靶区实钻砂、泥岩及煤层岩石物理参数,设计图2(a)上覆砂岩、下覆泥岩背景下含煤层反射界面理论模型,其中(b)为理论反射系数模型、(c)为正演25 Hz零相位雷克子波、(d)为合成地震记录、(e)无煤层背景下上覆砂岩、下覆泥岩理论模型、(f)无煤层背景下理论反射系数、(g)为处理前后单道地震记录对比。图2(g)黑色曲线为有煤层背景下地震正演记录,蓝色曲线为去煤层反射地震记录,红线为无煤层背景下地震正演记录。分析可知,匹配追踪技术对煤层反射有很好分离效果,去煤层后反射地震记录与无煤层背景下地震记录基本一致。
为了进一步验证煤层强反射背景下匹配追踪算法的可靠性,以靶区N-1井富煤段进行论证分析。N-1井平湖组中下段发育13套煤层,厚度为0.5~3 m,多为1 m左右,含9套砂岩,背景为灰色泥岩,如图3所示,(a)为N-1井钻遇岩性、(b)为实际井旁道地震数据、(c)为本文匹配追踪去煤后地震记录、(d)为实钻数据剔除煤层影响后地震正演记录,分析可知,当多套砂煤耦合时,波形相互干涉,形成更加复杂的复合波,利用匹配追踪去煤层强振幅技术仍然有较好的效果,去煤层后的地震记录与理论无煤层记录匹配性较好,进一步验证该方法的可靠性。
3. 应用实例
通过理论模型及实钻数据正演论证了匹配追踪去煤层强振幅的可靠性,进一步针对靶区开发实际问题验证该方法的应用效果。W-1井P6b钻遇5.8 m优质气层,北侧较低部位部署开发井A-2,钻遇25 m气层且未见气水界面;钻前储层预测揭示南侧A-1设计井P6b砂体发育,实钻砂体落空,钻遇为泥煤反射段,如图4所示。
图5所示为过三口已钻井区叠加地震及Vp/Vs岩性反演剖面。分析可知,靶区P6~P8段薄煤层广泛发育,叠加地震剖面上表现为典型强振幅特征(图5(a)),Vp/Vs地震反演是一种基于叠前AVO地震信息的砂体预测技术,在地质模式不明确的情况下,储层反演低频建模很难约束煤层强振幅地震响应,进而导致富煤段反演为相对低Vp/Vs的砂岩假象特征。图5(b)所示为过三口井钻前Vp/Vs反演,钻前揭示W-1、A-1及A-2井P6b砂体均发育,实钻W-1井P6b砂体发育,但厚度比预测偏薄,实钻为5.8 m薄层,A-2井P6b层25 m砂岩预测较准确,但A-1井P6b砂体落空,实钻为泥煤反射段。
针对这一问题,首先分析煤层发育段,图6所示为过三口井煤层地震敏感因子P - G剖面,井上岩性黑色为煤层,白色为非煤层。分析可知,P - G属性与煤层发育段匹配性较高,能够较精细地反映煤层位置。
通过煤层地震敏感因子P - G定位煤层,在此基础上,以该位置先验信息作为匹配追踪地震信号,实现煤层解耦。图7所示为去煤层强振幅前后效果对比,其中图7(a)为过三口井叠加地震,图7(b)为匹配追踪去煤层强振幅后叠加地震,图7(c)为去强振幅前后的残差,即煤层响应信息。分析可知,受煤层强振幅影响,原始地震同相轴表现为横向稳定“大铁轨”特征,横向变化细节欠缺;而优化处理后叠加剖面较好地压制了煤层强振幅,横向细节变化更加明显,P6b砂顶波峰横向变化与已钻井更吻合,W-1和A-2井砂体发育,波峰特征明显,而A-1井砂体不发育,无明显波峰特征。
图8所示为去除煤层强振幅前后Vp/Vs岩性预测对比,针对原始Vp/Vs预测横向细节不够、煤层岩性假象等问题(图8(a)蓝色箭头),去煤后储层预测纵、横向精度有了较大提高,纵向上,W-1井P6b 5 m气层厚度匹配性更高,横向上,去煤后Vp/Vs反演与钻井吻合度更好,开发A-1井P6b显示砂岩不发育(图8(b)黑色箭头),与实钻认识相符。
进一步分析三口钻井P6b砂体空间展布,如图9所示,图中标出各井P6b平面位置。分析可知,去煤后Vp/Vs反演与已钻井匹配性更高,钻前揭示开发A-1井P6b砂体欠发育,与钻后认识相符;同时,去煤层后Vp/Vs反演对靶区P6b砂体空间刻画更精细,避免了平面上砂体“铺天盖地”特征。
4. 结论与认识
西湖凹陷中深层薄煤层富集,煤层强振幅制约了储层的纵、横向精细刻画,本文结合煤层4类AVO中负强截距、正强梯度特点,提出一种煤层地震敏感因子P-G,实现煤层地震响应的精细定位。
AVO信息约束的匹配追踪去强振幅技术较好地压制了煤层强反射引起的岩性假象,凸显储层真实、有效信号,提高了靶区P6b主力气层纵、横向刻画精度,为中深富煤层系储层精细刻画提供了一种技术思路。
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表 1 AI与人工测量值的差异性及相关性比较
Table 1 The differences and related comparisons between AI and manual measurements
性质 参数 AI测量 人工测量 P 差异性 P50(P25,P75) 264.59(158.08,544.77) 263.43(159.60,527.56) 0.703 相关性 相关系数r 1 0.981 0.000 注:AI与人工测量差异性比较采用Wilcoxon秩和检验,P=0.703>0.05,差异无统计学意义。二者相关性比较采用 Spearman分析,具有显著相关性;二者相关系数为0.981。 表 2 AI与人工测量一致性比较
Table 2 The consistency comparisons between AI and manual measurements
项目 同类相关性 95%置信区间 使用真值0的F检验 下限 上限 值 自由度1 自由度2 显著性 单个测量 0.987 0.983 0.99 154.651 169 169 0 平均测量 0.994 0.991 0.995 154.651 169 169 0 注:AI与“金标准”一致性比较采用组内相关系数(ICC)一致性检验,结果显示:二者ICC值为0.987,一致性较好。 表 3 不同因素对AI测量误差的影响
Table 3 The effects of different factors on AI measurement errors
变量 分组 测量误差 Z P 体积 体积≤523.6 mm3(n=125) -0.553(-8.313,9.327) -0.769 0.442 体积>523.6 mm3(n=45) 3.847(-32.743,77.457) 位置 右肺上叶(n=49) 1.208(-4.947,14.561) 5.575 0.233 右肺中叶(n=21) -8.990(-22.598,12.897) 右肺下叶(n=27) 1.203(-8.542,16.477) 左肺上叶(n=49) -0.253(-12.017,12.932) 左肺下叶(n=24) -2.240(-20.935,12.388) 毗邻 肺实质(n=82) -0.745(-11.519,14.643) 0.438 0.803 贴近血管(n=29) 0.010(-6.762,15.200) 胸膜下(n=59) -0.253(-10.637,9.273) -
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