ISSN 1004-4140
CN 11-3017/P

工业CT技术在地球科学中的应用

汤戈, 赵欣雨, 王宇翔, 冯鹏, 魏彪

汤戈, 赵欣雨, 王宇翔, 等. 工业CT技术在地球科学中的应用[J]. CT理论与应用研究(中英文), 2024, 33(1): 119-134. DOI: 10.15953/j.ctta.2023.091.
引用本文: 汤戈, 赵欣雨, 王宇翔, 等. 工业CT技术在地球科学中的应用[J]. CT理论与应用研究(中英文), 2024, 33(1): 119-134. DOI: 10.15953/j.ctta.2023.091.
TANG G, ZHAO X Y, WANG Y X, et al. Applications of Industrial Computed Tomography Technology in the Geosciences[J]. CT Theory and Applications, 2024, 33(1): 119-134. DOI: 10.15953/j.ctta.2023.091. (in Chinese).
Citation: TANG G, ZHAO X Y, WANG Y X, et al. Applications of Industrial Computed Tomography Technology in the Geosciences[J]. CT Theory and Applications, 2024, 33(1): 119-134. DOI: 10.15953/j.ctta.2023.091. (in Chinese).

工业CT技术在地球科学中的应用

基金项目: 科技部重点研发专项(重点锂、铍成矿带成矿规律与预测评价研究与综合(2019YFC0605203));国家自然科学基金青年基金(核辐射环境下硅双极型晶体管瞬态协同损伤机制研究(12205028));重庆市科委技术创新与应用发展专项(轨道交通智慧化车站研究及应用(cstc2021jscx-gksbX0056));成都理工大学2022年中青年骨干教师资助计划(10912-JXGG2022-08363)。
详细信息
    作者简介:

    汤戈: 男,成都理工大学核技术与自动化工程学院副教授、硕士生导师,主要从事核信号采集与数字化处理,E-mail:tangge_cqu@163.com

    通讯作者:

    冯鹏: 男,重庆大学光电工程学院副教授、博士生导师,主要从事CT理论与应用研究,E-mail:coe-fp@cqu.edu.cn

  • 中图分类号: P  631

Applications of Industrial Computed Tomography Technology in the Geosciences

  • 摘要:

    工业CT作为计算机断层成像(CT)发展至今的一个重要分支,得益于其分辨率高、可重复、探测范围广等优势,在航空航天、军事工业、地质分析等多个领域得到了广泛应用。本文在深入调研国内外工业CT技术研究现状的基础上,综述了在地球科学领域中的3种典型工业CT技术(地震波CT、电阻率CT、电磁波CT)以及多种物探方法组合而成的综合物探方法,重点介绍工业CT在孔隙研究、天然气水合物研究、构建数字岩心和二氧化碳地质利用与封存方面的最新应用。同时,总结工业CT在地球科学领域中的发展趋势。

    Abstract:

    As an important branch of computed tomography (CT), industrial CT is used widely in many fields, such as aerospace, military industry, and geological analysis fields, because of its advantages of high resolution, repeatability, and wide detection range. On the basis of thorough investigation and study, this paper summarizes three typical industrial CT technologies (i.e., seismic wave CT, resistivity CT, and electromagnetic wave CT) as well as the comprehensive geophysical exploration methods used in the geosciences. The current applications of industrial CT in pore structure studies, gas hydrate studies, digital core construction, and geological utilization and storage of carbon dioxide are introduced. The development trend of industrial CT in the geosciences is also discussed.

  • 源于滤泡细胞的甲状腺癌占甲状腺恶性肿瘤的95%,其中的80%~85%是甲状腺乳头状癌(papillary thyroid carcinoma,PTC)[1]。手术是治疗PTC的首选方法,然而部分PTC患者术后易复发和远处转移,导致预后不良,甚至死亡[2]。相当研究表明,颈部中央区淋巴结转移(central lymph node metastasis,CLNM)是PTC患者预后差的独立危险因素[3]。由于目前临床缺少术前预测CLNM的有效技术,术中冰冻切片证实为PTC的病例,常规执行预防性的中央区淋巴结清扫(prophylactic lymph node dissection,PLND)[4]。然而,过度的PLND不仅增加患者不必要的损伤,甚至引起严重的并发症[5]。因此,探索建立术前精准评价PTC患者颈部淋巴结状态的有效新方法,成为亟待解决的重要科学问题。

    目前,PTC患者颈部中央区淋巴结状态术前检查的方法主要包括体格检查、超声、CT、MRI和PET等,其中触诊的敏感性低于7%,超声和CT的敏感性为50%~60%[6-7],MRI和PET常因为呼吸道伪影的干扰,小的淋巴结不易显示[8]。甲状腺功能检查指标的水平与CLNM的发生的相关性也被广泛研究,但研究成果差异很大[9-11]。近年来,利用PTC原发灶的多参数特征构建模型术前预测CLNM逐渐成为研究热点,然而,大多数模型的建立涉及到术前不能明确或须经手术病理证实的特征,如甲状腺外侵犯、血管外侵犯、BRAFV600 E变异、桥本氏甲状腺炎(Hashimoto’s thyroiditis,HT)、TNM分期等[12],致使临床上应用受限。

    本研究针对上述问题,以PTC患者术前临床信息、超声及CT影像学数据联合甲状腺功能检查的多参数特征为基础,构建预测模型,精准预测PTC患者的颈部CLNM。其特点是所有的预测因素术前可得,同时考虑到原发病灶与CLNM确定的对映关系,我们选择单灶性PTC,以增加研究结果的可解释性。

    连续收集2019年1月至2023年12月在我院外科行甲状腺癌根治术患者的临床、CT、超声及实验室检查的资料。上海市金山区亭林医院伦理委员会批准此项研究,所有的患者免除签订知情同意书。

    纳入标准:患者甲状腺腺叶或全甲状腺切除术+颈部中央区淋巴结清扫,术后病理是单灶性PTC;术前两周在我院行甲状腺CT增强检查;术前两周在我院行甲状腺超声检查;术前一周在我院行甲状腺功能检查。

    排除标准:PTC最大径小于3 mm;既往有甲状腺或头颈部其他肿瘤手术史;Graves病;图像质量欠佳;临床信息不完整。

    按照纳入标准和排除标准,340例患者入组本研究。

    本研究方法包括变量的定义和分类;根据变量的性质选择不同的统计方法;以及选择用于模型构建的编程语言等。

    性别(男和女);年龄分组(≤55岁和>55岁);TSH(参考值,以下同:0.55~4.78 mIU/L);fT3(3~6.3 pmol/L);fT4(10.3~24.5 pmol/L);TT3(1.3~3.1 nmol/L);TT4(66~181 nmol/L);TG-Ab(0~60 IU/ml);TPO-Ab(0~60 IU/ml)。

    在未知CLNM病理结果的情况下,由两位具有10年以上头颈部放射诊断工作经验的医师(医师1和医师2)各自分析340例患者的CT图像特征。包括,CT值:正常甲状腺实质和同侧胸锁乳突肌的平扫CT值和差值,两者感兴趣区保持面积一致;PTC与甲状腺被膜接触程度:依据PTC与甲状腺包膜最大接触长度与肿瘤周长的比值,分为0、<25%、25%~50%和>50%;病灶位置分布:位置1(左叶、峡部、右叶)、位置2(上部、中部、下部);病灶实质构成:依据病灶内是否存在液性无强化区分为实性、囊实性及囊性。两位医师意见不一致时通过讨论或者咨询医师3(具有16年头颈部放射诊断工作能力)决定。

    在未知CLNM病理结果的情况下,两位具有8年以上甲状腺超声诊断工作经验的医师(医师4和医师5)分析340例患者的甲状腺超声图像特征。包括:病灶回声特点(低回声、等回声、高回声);形态特征(圆形或类圆形,边缘无明显分叶状或刺状定义为规则);肿瘤边界(肿瘤边缘与正常甲状腺实质界限可辨,称为清晰);肿瘤最大径(≤10 mm和>10 mm);肿瘤前后径(A)和左右径(T),并计算A/T比(≤1和>1);钙化特点(无钙化、直径≤3 mm和>3 mm);彩色多普勒血流显像(CDFI) (无血流、周围血流、中央血流、周围+中央血流)。

    单因素分析中,符合正态分布的计量资料用($\bar x \pm s $)表示,t检验分析,不符合正态分布的计量资料用Mann-Whitney U检验;计数资料用Pearson $ \chi^ 2 $检验(最小期望值≤5用Fisher精确$ \chi ^2 $检验);多因素logistic回归向前法筛选IRF;列线图的评价指标包括ROC曲线、calibration和DCA;医师之间的评判一致性用Kappa检验。所有的假设检验采用双尾检验,P<0.05表示差别有统计学意义,使用的软件包括SPSS19(SPSS/IBM,Chicago)和R2.15.3软件。

    本研究的340例PTC患者中,男性88例(25.9%),女性252例(74.1%),年龄范围19~83岁(50.60±13.243)。131例患者发生了CLNM(38.5%,男性50例,女性81例)。2个临床特征(年龄分组、性别)、4个CT影像特征(位置、内部构成、被膜接触、差值)、7个超声影像特征(回声、边缘、边界、最大径、A/T比、钙化、CDFI)以及甲状腺功能检查的7项指标被纳入研究。医师1、2和4、5之间各个征象评价一致性的Kappa检验系数>0.75。年龄≤55岁、男性性别以及肿瘤最大径>10 mm是CLNM的高危因素,PTC与被膜接触、边缘不规则生长、钙化以及病灶内发生囊变更容易发生CLNM,差异有统计学意义(表1表2)。

    表  1  CLNM分组的临床和实验室特征单因素分析
    Table  1.  Univariate analysis of clinical and laboratory characteristics in the CLNM group
    特征CLNM(n=131)Non-CLNM(n=209)T值P
    年龄46.27±13.6853.32±12.23-4.9330.000
    年龄分组(n%)11.2840.001
      ≤5593(71.0)110(52.6)
      >5538(29.0) 99(47.4)
    性别(n%)16.7680.000
      男50(38.2) 38(18.2)
      女81(61.8)171(81.8)
    TSH1.933±1.1172.086±1.173-1.130.259
    fT35.101±0.7814.975±0.6631.5840.114
    fT417.414±3.68217.382±2.9550.0860.932
    T31.768±0.3221.755±0.3160.3450.730
    T4111.3±25.346113.0±24.0161.2330.218
    TG-Ab2270135269-0.4230.673
    TPO-Ab22332.535673.5-0.0040.997
    下载: 导出CSV 
    | 显示表格
    表  2  CLNM分组的影像特征单因素分析
    Table  2.  Univariate analysis of imaging characteristics in the CLNM group
    特征CLNM(n=131)Non-CLNM(n=209)T值P
    边缘(n%)17.0440.000
      规则23(17.6)81(38.8)
      不规则108(82.4)128(61.2)
    边界(n%)0.1230.725
      清晰92(70.2)143(68.4)
      模糊39(29.8)66(31.6)
    位置1(n%)1.8610.394
      右叶59(45.0)103(49.3)
      峡部14(10.7)14(6.7)
      左叶39(29.8)92(44.0)
    位置2(n%)0.3950.821
      上32(24.4)45(21.5)
      中64(48.9)107(51.2)
      下35(26.7)57(27.3)
    实质构成(n%)7.830.005
      实性93(71.0)175(83.7)
      囊实性38(29.0)34(16.3)
    被膜接触(n%)34.5260.000
      08(6.1)51(24.4)
      <25%34(26.0)62(29.7)
      25%~50%35(26.7)61(29.2)
      >50%54(41.2)35(16.7)
    最大径(n%)32.5440.000
      ≤10 mm66(50.4)167(79.9)
      >10 mm65(49.6)42(20.1)
    A/T 比(n%)0.0030.959
      ≤183(63.4)133(63.6)
      >148(36.6)76(36.4)
    钙化(n%)9.2010.010
      无44(33.6)105(50.2)
      ≤3 mm72(55.0)84(40.2)
      >3 mm15(11.5)20(9.6)
    CDFI(n%)5.0730.167
      无60(45.8)118(56.5)
      周围24(18.3)38(18.2)
    中央40(30.5)47(22.5)
      周围+中央7(5.3)6(2.9)
    差值*33.09±16.45934.37±16.754-0.6930.489
    注:*甲状腺实质与同侧胸锁乳突肌CT值之差。
    下载: 导出CSV 
    | 显示表格

    对单因素分析中P值<0.05的特征(年龄分组、性别、肿瘤最大径、被膜接触、实质构成、边缘以及肿瘤内钙化)进行多因素logistics回归向前法分析,结果表明,年龄≤55岁、男性性别、肿瘤最大径>10 mm、被膜接触>0以及边缘不规则是CLNM的IRFs(表3)。

    表  3  340例PTC患者CLNM的多因素logistics回归分析
    Table  3.  Multivariate logistic regression analysis for CLNM in 340 patients with PTC
    变量估计值标准误Z值P
    截距-2.5960.688-3.7730.000
    年龄(>55)-0.7550.332-2.2720.023
    性别(女)-0.9930.362-2.7390.006
    边缘(不规则)1.1530.3902.8560.003
    最大径(>10 mm)1.0510.3622.8980.003
    构成(囊实性)0.3430.3970.8630.388
    被膜接触
      <25%1.7910.6222.8790.004
      25%~50%2.0050.6233.2170.001
      >50%2.5200.6323.9840.000
    钙化
      ≤3 mm0.0030.3500.0080.993
      >3 mm0.5150.5910.8710.384
    下载: 导出CSV 
    | 显示表格

    340名PTC患者以7∶3随机分成训练组(n=273)和验证组(n=103),IRFs在随机分组中的差异无统计学意义(表4)。依据IRFs在多因素logistic回归分析中的权重,在训练组构建列线图(nomogram)(图1),并在验证组验证。结果显示,Nomogram的ROC在训练组的曲线下面积(area under the curve,AUC)为0.815(95%CI:0.761~0.870),在验证组中的AUC值0.747(95%CI:0.646~0.848)(图2)。Nomogram预测个体患者CLNM发生概率的实例演示如图3

    表  4  340例PTC患者7:3比例随机分组中IRF的比较
    Table  4.  Comparison of IRFs in the training and validation cohorts where 340 PTC patients were randomly divided into at a 7:3 ratio
    特征训练组(n=237)验证组(n=103)T值P
    年龄分组(n%)0.0150.904
      ≤55141(59.5)62(60.2)
      >5596(40.5)41(39.8)
    性别(n%)0.2000.655
      男63(26.6)25(24.3)
      女174(73.4)78(75.7)
    边缘(n%)1.3250.250
      规则68(28.7)36(35,0)
      不规则169(71.3)67(65.0)
    被膜接触(n%)1.9030.593
      045(19.0)14(13.6)
      <25%68(28.7)28(27.2)
      25%~50%64(27.0)32(31.1)
      >50%60(25.3)29(28.2)
    最大径(n%)0.1620.687
      ≤10 mm164(69.2)69(67.0)
      >10 mm73(30.8)34(33.0)
    下载: 导出CSV 
    | 显示表格
    图  1  术前预测PTC患者CLNM的列线图
    注:Age:年龄分组,Gender:性别,Margin:边缘,Capsular:被膜接触,Tumor size:最大径。
    Figure  1.  Nomogram for preoperative prediction of CLNM in patients with PTC
    图  2  Nomogram预测CLNM的ROC曲线
    注:(a)和(b)中的ROC曲线的AUC值、灵敏性和特异性分别是0.815和0.747、0.759和0.781以及0.717和0.641。
    Figure  2.  ROC curves for the nomogram predicting CLNM
    图  3  Nomogram预测CLNM的性能演示(界值为0.303)
    注:患者1,男性,44岁,PTC病例,CLNM阳性。CT平扫示右叶中下部类圆形病灶,内见钙化,与甲状腺被膜接触约75%(a),增强后不均匀强化(b),超声显示低回声区,境界清晰,边缘光整,约20.6×16,5×18,5 mm(c),对照Nomogram:男性(38分)+年龄<55岁(28分)+被膜接触>50%(100分)+最大径>10 mm(45分)=211分,对应的CLNM预测概率0.73。患者2,女性,37岁,PTC病例,CLNM阴性。右叶内不规则低密度结节,边缘与被膜无接触(d),增强后轻度强化(e),超声显示境界清晰,边缘不光整的低回声区,最大径约8.5 mm(f),对照Nomogram:年龄<55岁(28分)+边缘不规则(48分)=76分,对应的CLNM预测概率0.10。
    Figure  3.  Performance of the nomogram for CLNM (cutoff=0.303)

    Nomogram的预测准确性和临床实用性用Calibration和DCA评价(图4图5),calibration显示预测概率和实际概率在训练训练组和验证组中一致性程度很高。DCA曲线表明,当个体患者预测概率在0.1到0.85范围内,Nomograms预测CLNM的净收益高于无预测组或全部干预组。

    图  4  列线图的calibration曲线
    注:图中的X轴表示预测概率,Y轴表示观察概率,虚线是参考线,表示预测概率和实际概率完全一致;红线和绿线是拟合线,绿线表示偏差校准前的曲线,红线表示偏差校准后的曲线。
    Figure  4.  Calibration curves of the nomogram
    图  5  列线图在训练组(a)和验证组(b)的DCA
    注:图中Y轴表示净收益,X轴表示概率阈值,红线表示Nomogram,粗线表示无干预组,细线表示全部干预组。阈值从0.1到0.85时,nomogram的净收益高于全部干预组或无干预组。
    Figure  5.  DCA of the nomogram in the training (a) and validation (b) cohorts

    术前精准评价PTC患者颈部淋巴结状态,对于制定临床手术方案、提升患者生存质量具有重要的意义。然而,目前临床对于甲状腺乳头状癌CLNM的术前精准预测方面一直面临巨大挑战。

    相当研究表明,年龄≤55岁、男性性别以及肿瘤最大径>10 mm是PTC患者发生CLNM的IRFs[13-14],这与本研究的结果发现相一致。分析认为,不健康的生活习性如抽烟、酗酒以及更高的基础代谢潜在地加速了肿瘤的扩散,可能是低龄男性PTC患者更易于发生CLNM的原因[13,15]。Huang等[16]研究发现常规PTC中,肿瘤直径越大越容易发生多个CLNM。

    被膜侵犯是预测甲状腺癌颈部淋巴结转移的重要因素[17],分析认为其机制可能与肿瘤组织突破甲状腺被膜,并侵犯被膜内的淋巴系统有关。本研究中,定义的被膜接触是指CT影像上瘤体与甲状腺被膜之间无甲状腺组织的直接接触,区别于病理上的被膜侵犯。本研究中的被膜接触可以理解为被膜侵犯一种影像征象。被膜接触在术前的CT图像上易于识别和准确定量,正常甲状腺实质高密度的背景下,PTC均表现为低密度,CT平扫期能够清晰地观察到被膜接触的长度。患有HT的患者,甲状腺实质的背景密度减低,可以结合增强动脉期或静脉期的影像比较观察。

    HT患者伴发PTC的几率增高,且高于其他类型的甲状腺癌[9]。我们比较了正常甲状腺实质与胸锁乳突肌的CT值差值(考虑到不同机型、不同扫描参数可能引起相同层面CT值的不同,但同一层面不同组织的CT差值可能不变)。研究结果显示差值越小,HT的发生率越高(22.33±15.54 vs 38,22±14.697,P<0.001),但与CLNM的发生无相关性。

    研究表明,PTC瘤体发生囊变的机制主要是由癌细胞产生的胶质被液化而形成,因此PTC囊变通常表明肿瘤具有更高的恶性度[18],产生更高的侵袭性和转移能力,因此,也更容易发生CLNM。甲状腺结节内微钙化的检出对甲状腺癌的诊断有高度特异性,特别是PTC[19]。本研究中,单因素分析显示瘤体内囊变和钙化与CLNM的发生都存在相关性,但多因素分析显示缺乏相关性,因此,它们与CLNM的相关性需要进一步研究。

    文献中有较多关于甲状腺功能检测指标预测CLNM的报道。Yu等[10]研究认为TPO-Ab和TG-Ab对预测CLNM都有价值,但作用互为相反;岳潇潇等[11]发现甲状腺功能检测指标的水平与PTC是否有淋巴结转移不相关。本研究对340例患者的血清TSH、FT3、FT4、T3、T4、TG-Ab、TPO-Ab表达水平行单因素分析(后两者为偏态分布资料,我们做了Wilcoxon秩和检验),但结果未能显示与CLNM之间的相关性,相关的数据还需要进一步研究证实。

    本研究的贡献之处在于,一旦术中冰冻切片确诊为PTC后,外科医生易于根据术前多参数资料计算CLNM的发生概率,辅助决定是否需要预防性的淋巴结清扫,尽可能避免扩大手术范围和不必要的损伤。

    本研究的局限性在于,首先,回顾性研究可能会引入一些选择偏倚;其次,单中心、小样本量以及外部验证的缺少,模型可能存在拟合优度过度的问题,产生的Nomogram泛化能力相对较弱;再次,本研究目的是精准分析术前单个PTC的预测能力,排除了多灶瘤及最大径小于3 mm的病例,须经术后病理确诊的特征也未被纳入,模型的预测效能不可避免地降低。尽管存在上述不利因素,我们的模型仍然展示了很好的预测能力和临床实用性,更优质的模型需要多中心、大样本量以及强大的外部验证进一步研究。

  • 图  1   CT技术的发展

    Figure  1.   Development of CT technology

    图  2   X射线穿过物质的示意图

    Figure  2.   Diagram of an X-ray passing through a substance

    图  3   跨孔电阻率CT装置类型

    Figure  3.   Cross-hole resistivity-type CT device

    图  4   重庆真测科技股份有限公司生产的CD−130BX/μCT微纳三维分析仪[56]

    Figure  4.   CD-130BX/μCT micro-nano 3D analyzer produced by Chongqing Zhence Science and Technology Co., Ltd.[56]

    图  5   课题组使用CD-130BX/μCT微纳三维分析仪对岩矿样品进行扫描

    Figure  5.   The research group used the CD-130BX/μCT micro-nano 3D analyzer to scan rock and ore samples

    图  6   X-CT检测水合物

    Figure  6.   Implementation of X-ray CT to detect gas hydrate

    表  1   工业CT总结

    Table  1   Summary of industrial CT

    CT种类理论方法传播速度km/s操作难度精度经济成本
    地震波CT射线理论5.5-7
    电磁波CT射线理论约3×105较高
    电阻率CT高密度电法
    下载: 导出CSV

    表  2   CT技术在研究不同材料孔隙结构中的应用

    Table  2   Application of CT technology to study the pore structure of various materials

    孔隙结构孔隙半径R/μm使用CT种类结论
    页岩孔隙结构4~40×10-3X-CT  岩心不同部位形成不同数量的孔隙空间
    煤岩孔隙结构0.1~100 X-CT  孔隙结构与煤岩的体积分形维数有关 
    黄土孔隙结构2~6   Micro-CT孔隙体的渗透率随孔隙度的增大而增大
    下载: 导出CSV
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  • 收稿日期:  2023-04-17
  • 修回日期:  2023-05-15
  • 录用日期:  2023-05-30
  • 网络出版日期:  2023-07-02
  • 刊出日期:  2024-01-09

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