Lung texture in serial thoracic CT scans: Assessment of change introduced by image registration
Lung texture in serial thoracic CT scans: Assessment of change introduced by image registration
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DOI：
10.1118/1.4730505
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发表时间：
2012-08-01
期刊：
影响因子：
3.8
通讯作者：
Armato, Samuel G., III
中科院分区：
文献类型：
--
作者：
Cunliffe, Alexandra R.;Al-Hallaq, Hania A.;Armato, Samuel G., III
目的：本研究的目的是量化的效果，四个图像配准方法对肺纹理特征提取的串行计算机断层扫描（CT）扫描从健康的人类subjects.Methods：两个胸部CT扫描采集在不同的时间点，回顾性地收集了27例患者。在自动肺分割之后，使用四种算法将每次随访CT扫描配准到基线扫描：（1）刚性，（2）仿射，（3）B样条变形，和（4）恶魔变形。通过测量150个已识别标志之间的欧几里得距离来评价每个扫描对的配准精度。平均而言，1432个空间匹配的32 × 32像素的感兴趣区域（ROT）对自动提取每个扫描对。在每个ROI中计算一阶、分形、傅立叶、Laws滤波器和灰度共生矩阵纹理特征，总共140个特征。通过Bland-Altman分析评估每个特征的基线和随访扫描ROT特征值之间的一致性;计算特征值差异一致性的95%限度所涵盖的范围，并通过平均特征值进行归一化，以获得归一化一致性范围（nRoA）。具有小nRoA的特征被认为是“配准稳定的”。“每个特征的归一化偏差是根据每个患者所有ROI的基线和随访扫描之间的特征值差异平均计算的。由于患者有“正常”的胸部CT扫描，预计扫描对之间的纹理特征值的变化最小，与小的偏差和狭窄的限制agrees.Results的期望：登记与恶魔减少了标志之间的欧氏距离，只有9%的地标分离>= 1毫米，与刚性（98%），仿射（95%），和B-样条（90%）。对于所有配准方法，所分析的140个特征中有99个（71%）的nRoA > 50%，表明大多数特征值在配准后受到干扰。19个特征（14%）在demons注册后nRoA < 15%，表明相对特征值稳定。学生的t检验表明，这19个功能的nRoA显着更大时，刚性，仿射，或B样条注册方法相比，恶魔注册。恶魔注册产生了更大的归一化偏差的特征值变化比B-样条注册，虽然这种差异并不显着（p = 0.15）。结论：恶魔注册提供了更高的空间精度之间的匹配解剖标志序列CT扫描比刚性，仿射，或B-样条算法。与所有其他算法相比，从连续CT扫描中计算的健康肺组织的纹理特征变化在恶魔配准后较小。尽管配准改变了大多数纹理特征的值，但19个特征在恶魔配准后保持相对稳定，表明它们在连续CT扫描中检测病理变化的潜力。使用demons和纹理分析的准确可变形配准的组合使用可以允许定量评估由于疾病进展或治疗反应引起的肺组织中的局部变化。(C)2012年美国医学物理学家协会。[http：//dx.doi.org/10.1118/1.4730505]
Purpose: The aim of this study was to quantify the effect of four image registration methods on lung texture features extracted from serial computed tomography (CT) scans obtained from healthy human subjects.Methods: Two chest CT scans acquired at different time points were collected retrospectively for each of 27 patients. Following automated lung segmentation, each follow-up CT scan was registered to the baseline scan using four algorithms: (1) rigid, (2) affine, (3) B-splines deformable, and (4) demons deformable. The registration accuracy for each scan pair was evaluated by measuring the Euclidean distance between 150 identified landmarks. On average, 1432 spatially matched 32 x 32-pixel region-of-interest (ROT) pairs were automatically extracted from each scan pair. First-order, fractal, Fourier, Laws' filter, and gray-level co-occurrence matrix texture features were calculated in each ROI, for a total of 140 features. Agreement between baseline and follow-up scan ROT feature values was assessed by Bland-Altman analysis for each feature; the range spanned by the 95% limits of agreement of feature value differences was calculated and normalized by the average feature value to obtain the normalized range of agreement (nRoA). Features with small nRoA were considered "registration-stable." The normalized bias for each feature was calculated from the feature value differences between baseline and follow-up scans averaged across all ROIs in every patient. Because patients had "normal" chest CT scans, minimal change in texture feature values between scan pairs was anticipated, with the expectation of small bias and narrow limits of agreement.Results: Registration with demons reduced the Euclidean distance between landmarks such that only 9% of landmarks were separated by >= 1 mm, compared with rigid (98%), affine (95%), and B-splines (90%). Ninety-nine of the 140 (71%) features analyzed yielded nRoA > 50% for all registration methods, indicating that the majority of feature values were perturbed following registration. Nineteen of the features (14%) had nRoA < 15% following demons registration, indicating relative feature value stability. Student's t-tests showed that the nRoA of these 19 features was significantly larger when rigid, affine, or B-splines registration methods were used compared with demons registration. Demons registration yielded greater normalized bias in feature value change than B-splines registration, though this difference was not significant (p = 0.15).Conclusions: Demons registration provided higher spatial accuracy between matched anatomic landmarks in serial CT scans than rigid, affine, or B-splines algorithms. Texture feature changes calculated in healthy lung tissue from serial CT scans were smaller following demons registration compared with all other algorithms. Though registration altered the values of the majority of texture features, 19 features remained relatively stable after demons registration, indicating their potential for detecting pathologic change in serial CT scans. Combined use of accurate deformable registration using demons and texture analysis may allow for quantitative evaluation of local changes in lung tissue due to disease progression or treatment response. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4730505]