Volumetric Measurements Of Pulmonary Nodules Research Articles

Computed tomography quantitative analysis of components: a new method monitoring the growth of pulmonary nodule

The estimation of the growth of solitary pulmonary nodules by using non-invasive methods is increasingly gaining clinical importance for performing the timely adequate treatment of these nodules. To evaluate the application value of computed tomography (CT) quantitative analysis of components for dynamic assessment of the growth of solitary pulmonary nodules, and compare this approach with three-dimensional (3D) volumetric measurement of pulmonary nodules. The imaging data of 21 patients who had undergone multiple follow-up CT scans for solitary pulmonary nodules were retrospectively analyzed, and the total volume of pulmonary nodules and the percentage change in the total volume of pulmonary nodules after multiple follow-up CT scans were measured using 3D volume measurement software. The volume of solid components in pulmonary nodules was measured using CT quantitative analysis; the percentage change in the volume of solid components across examinations was calculated; and the percentage change in the total volume of pulmonary nodules was compared and contrasted with the percentage change in the volume of solid components in the pulmonary nodules. All 21 cases were malignant adenocarcinomas. In the 21 cases of malignant nodules, the 3D volumes of the nodules and solid components were both increased, with the percentage change in the volume of the solid components (115.78-418.91%, 130.45 ± 119.48) significantly different from the percentage change in the total volume of the nodules (78.56-105.73% , 42.34 ± 32.17) (P = 0.02). By measuring volume changes in solid components in the nodules, CT quantitative analysis offers more sensitive and earlier evaluation of the dynamic growth of the nodules than measurement of volume changes in the nodules alone.

Effects of different reconstruction parameters on CT volumetric measurement of pulmonary nodules

背景与目的已有的研究证明：CT肺结节容积测量能够敏感反映结节容积的微小变化，在未定性肺结节的随访观察及良恶性鉴别方面具有重要应用前景。本研究旨在评估不同重建算法和层厚对CT肺结节容积测量的影响。方法对2009年12月-2011年8月在天津医科大学总医院行未定性肺结节CT随访研究的30例患者的CT图像进行后处理分析。共获取52个肺结节，应用3种不同的算法（骨密度算法、标准算法及肺算法）及层厚（0.625 mm、1.25 mm、2.5 mm）进行9次重建。由一位放射科医师应用肺结节容积分析软件进行容积测量。应用重复测量多元方差分析、相关分析及Bland-Altman法评价结节直径、算法、层厚对容积测量的影响。结果不同重建算法（F=13.6, P＜0.001）、层厚（F=4.4, P=0.02）条件下测量所得的结节容积之间具有统计学差异。肺结节9次测量所得容积的变异系数和结节直径之间呈负相关（r=-0.814, P＜0.001）。应用2.5 mm层厚时，结节容积比1.25 mm及0.625 mm层厚的一致性差，1.25 mm和0.625 mm层厚在采用骨算法时一致性最好。结论不同重建算法及层厚对肺结节容积测量有影响，尤其是直径较小的结节。在未定性肺结节尤其是肺小结节的随访过程中建议应用相同的重建参数。

Volumetric measurement of pulmonary nodules at low-dose chest CT: effect of reconstruction setting on measurement variability

ObjectiveTo assess volumetric measurement variability in pulmonary nodules detected at low-dose chest CT with three reconstruction settings.MethodsThe volume of 200 solid pulmonary nodules was measured three times using commercially available semi-automated software of low-dose chest CT data-sets reconstructed with 1 mm section thickness and a soft kernel (A), 2 mm and a soft kernel (B), and 2 mm and a sharp kernel (C), respectively. Repeatability coefficients of the three measurements within each setting were calculated by the Bland and Altman method. A three-level model was applied to test the impact of reconstruction setting on the measured volume.ResultsThe repeatability coefficients were 8.9, 22.5 and 37.5% for settings A, B and C. Three-level analysis showed that settings A and C yielded a 1.29 times higher estimate of nodule volume compared with setting B (P = 0.03). The significant interaction among setting, nodule location and morphology demonstrated that the effect of the reconstruction setting was different for different types of nodules. Low-dose CT reconstructed with 1 mm section thickness and a soft kernel provided the most repeatable volume measurement.ConclusionA wide, nodule-type-dependent range of agreement between volume measurements with different reconstruction settings suggests strict consistency is required for serial CT studies.

Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study

The purpose of this study is to investigate the effects of CT scanning and reconstruction parameters on automated segmentation and volumetric measurements of nodules in CT images. Phantom nodules of known sizes were used so that segmentation accuracy could be quantified in comparison to ground-truth volumes. Spherical nodules having 4.8, 9.5 and 16 mm diameters and 50 and 100 mg cc−1 calcium contents were embedded in lung-tissue-simulating foam which was inserted in the thoracic cavity of a chest section phantom. CT scans of the phantom were acquired with a 16-slice scanner at various tube currents, pitches, fields-of-view and slice thicknesses. Scans were also taken using identical techniques either within the same day or five months apart for study of reproducibility. The phantom nodules were segmented with a three-dimensional active contour (3DAC) model that we previously developed for use on patient nodules. The percentage volume errors relative to the ground-truth volumes were estimated under the various imaging conditions. There was no statistically significant difference in volume error for repeated CT scans or scans taken with techniques where only pitch, field of view, or tube current (mA) were changed. However, the slice thickness significantly (p < 0.05) affected the volume error. Therefore, to evaluate nodule growth, consistent imaging conditions and high resolution should be used for acquisition of the serial CT scans, especially for smaller nodules. Understanding the effects of scanning and reconstruction parameters on volume measurements by 3DAC allows better interpretation of data and assessment of growth. Tracking nodule growth with computerized segmentation methods would reduce inter- and intraobserver variabilities.

Pulmonary Nodules: 3D Volumetric Measurement with Multidetector CT—Effect of Intravenous Contrast Medium

To retrospectively evaluate the effect of contrast medium on the three-dimensional volumetric measurement of pulmonary nodules. The study was approved by the local institutional review committee, with waiver of informed consent. Sixty pulmonary nodules in 60 patients (17 women, 43 men; age range, 29-82 years) were imaged before and after administration of contrast medium with a 64-channel multidetector computed tomographic (CT) scanner; reconstructed images with a section thickness of 0.625 mm were obtained by using a bone algorithm and a standard algorithm. Volumetric measurements of pulmonary nodules were performed by using commercially available software, and the postcontrast volume ratio was calculated by dividing the postcontrast volume by the precontrast volume. Precontrast and postcontrast volumes were then analyzed by using a Wilcoxon signed rank test. The median measured volumes of pulmonary nodules were 817 mm(3) (precontrast imaging, bone algorithm), 887 mm(3) (postcontrast imaging, bone algorithm), 812 mm(3) (precontrast imaging, standard algorithm), and 855 mm(3) (postcontrast imaging, standard algorithm). The measured volumes obtained with the bone algorithm were significantly larger than those obtained with the standard algorithm, both before and after administration of contrast medium (P < .01); with both the standard algorithm and the bone algorithm, the measured postcontrast volumes were significantly larger than the precontrast volumes (P < .01). The postcontrast volume ratio was more than 1.0 in 45 cases (75%) when the bone algorithm was used and in 53 cases (88%) when the standard algorithm was used. The mean postcontrast volume ratio was 1.054 with the bone algorithm and 1.065 with the standard algorithm. The measured volume of pulmonary nodules obtained by using three-dimensional volumetric software increased after administration of contrast medium. Moreover, the measured volume of pulmonary nodules that was obtained with the bone algorithm was larger than that obtained with the standard algorithm, regardless of whether contrast medium was used.

Computer-assisted lung nodule volumetry from multi-detector row CT: Influence of image reconstruction parameters

Purpose To investigate differences in volumetric measurement of pulmonary nodules caused by changing the reconstruction parameters for multi-detector row CT. Materials and methods Thirty-nine pulmonary nodules less than 2 cm in diameter were examined by multi-slice CT. All nodules were solid, and located in the peripheral part of the lungs. The resultant 48 parameters images were reconstructed by changing slice thickness (1.25, 2.5, 3.75, or 5 mm), field of view (FOV: 10, 20, or 30 cm), algorithm (high-spatial frequency algorithm or low-spatial frequency algorithm) and reconstruction interval (reconstruction with 50% overlapping of the reconstructed slices or non-overlapping reconstruction). Volumetric measurements were calculated using commercially available software. The differences between nodule volumes were analyzed by the Kruskal–Wallis test and the Wilcoxon Signed-Ranks test. Results The diameter of the nodules was 8.7 ± 2.7 mm on average, ranging from 4.3 to 16.4 mm. Pulmonary nodule volume did not change significantly with changes in slice thickness or FOV ( p > 0.05), but was significantly larger with the high-spatial frequency algorithm than the low-spatial frequency algorithm ( p < 0.05), except for one reconstruction parameter. The volumes determined by non-overlapping reconstruction were significantly larger than those of overlapping reconstruction ( p < 0.05), except for a 1.25 mm thickness with 10 cm FOV with the high-spatial frequency algorithm, and 5 mm thickness. The maximum difference in measured volume was 16% on average between the 1.25 mm slice thickness/10 cm FOV/high-spatial frequency algorithm parameters and overlapping reconstruction. Conclusion Volumetric measurements of pulmonary nodules differ with changes in the reconstruction parameters, with a tendency toward larger volumes in high-spatial frequency algorithm and non-overlapping reconstruction compared to the low-spatial frequency algorithm and overlapping reconstruction.