低曝光剂量对CT检出肺结节的影响

目的　探讨低剂量扫描对CT检出肺结节的影响。方法　选择肺转移瘤 2 6例 ,分别应用 195mAs / 6.5mm(剂量 /层厚 ) ,195mAs/ 3 .2mm ,5 0mAs/ 3 .2mm ,3 0mAs/ 3 .2mm 4种方案扫描肺部 ,统计在不同曝光剂量条件下检出的肺结节的数量、大小、部位。结果　 195mAs/ 3 .2mm ,5 0mAs/ 3 .2mm ,3 0mAs/ 3 .2mm组之间的肺结节检出数在总体上无显著差异 ,但按肺结节大小分类分析时 ,随曝光剂量的降低 ,对 <2mm和 5mm的肺结节的检出数有所减少 ,并且低剂量下 ,图像噪声明显增加。虽然 3 0mAs/ 3 .2mm组的图像噪声比 195mAs/ 6.5mm的明显高 ,但对肺结节的检出数明显高于 195mAs/ 6.5mm组。结论　总的来说 ,减低曝光剂量 ,图像噪声增加 ,图像质量下降 ,伪影可以掩盖细小肺结节 ,但在一定范围内适当降低曝光剂量 ,对肺结节的检出数无显著影响。低剂量薄层厚扫描筛查肺结节优于常规剂量常规层厚扫描。     

CT检查肺部小病灶的扫描方法探讨

目的：探讨CT检查肺部小病灶，为发现早期肺癌的扫描方法。方法：对50例无症状的要求常规体检者行肺部螺旋CT扫描，扫描参数为：120kVP、80mA，层厚为肺门部8mm．肺尖部和肺底部层厚10mm，螺距为1。若发现小病灶从小病灶上缘，以3mm呈厚，3mm层距，扫到病灶下缘为止。在CT图像上，统计病变的检出例数，结果：采用以上方法。显示3mm以上的肺结节21例、纵膈淋巴结增大7例及冠状动脉钙化1例。结论：肺门部8mm、肺尖部和肺底部10mm层厚的分段扫描，加上病灶位置薄层扫描，可以实现CT检查肺部小病灶，对肺癌的早发现具有重要的作用。     

16层螺旋CT低剂量肺普查的临床应用价值

目的:探讨16层螺旋CT低剂量扫描在肺普查中的应用价值。方法:对1206名体检者行16层螺旋CT低剂量肺扫描,扫描参数为管电压120KV,管电流25mA,层厚7.5mm,螺距1.35,床速27.5mm/圈。对15例小结节提高管电流至180mA重复扫描,进行对比。发现疑似或肯定的小结节就选择1.25mm层厚和1.25mm的间隔重建,再应用高级肺结节分析软件定量分析小结节的容积、密度。结果:共检出各类肺部疾病265例,阳性率为21.97%。检出非钙化性肺小结节97例,检出率为8.04%。发现的小结节直径为3~30mm。在对15例两种剂量扫描的小结节测量体积时,尽管低剂量扫描的体积要大于正常剂量扫描的体积,但是,其差异无显著性(t=1.747,0.2>P>0.1)。有12例3~4mm钙化性小结节未重建前测得的的最高点密度CT值为-571~61HU,重建后为399~1880HU,有明显差异(t=12.95,P<0.001)。结论:低剂量螺旋CT以发现3mm以上的小结节为主要目的。其射线剂量低,明显减少了对人群的辐射危害,完全可以应用于健康人群普查以利于早期肺癌的筛选和诊断。     

多层螺旋CT胸部低剂量扫描对肺结节或肿块的评价

目的评价低剂量螺旋CT扫描对肺结节的临床可行性及合适扫描方案。方法42例病例平扫均采用低剂量全肺螺旋扫描,发现肺结节或肿块病例在病变部位加低剂量及常规剂量薄层轴扫,首次发现者静脉注射造影剂后行常规剂量(200mAs)全肺扫描及病变部位薄层延迟轴扫。全肺扫描采用标准算法分别重建出低剂量7.5mm、5mm及常规剂量5mm横断面图像,同时利用标准算法及高分辨算法分别重建出2种剂量VR图像。比较2种剂量横断面5mm、薄层图像以及低剂量5mm与7.5mm图像对肺结节或肿块的形态特征、大小、测量密度的差别;比较低剂量及不同算法对三维VR图像质量的影响。结果2种剂量图像对肺结节或肿块大小、形态的显示无明显差异,但是低剂量薄层扫描图像部分征象显示不如常规剂量图像。密度测量方面,低剂量薄层扫描图像标准差较大,与常规剂量有明显差异。重建VR图像质量2组剂量无明显差异,标准算法重建好于或等于高分辨算法图像。结论低剂量扫描7.5mm标准算法图像重建,病灶局部加常规剂量薄层轴扫,可以准确显示肺结节或肿块形态、大小、密度特征而且大大降低了病人的辐射剂量,具有较好的临床实用价值。     

低剂量与常规剂量螺旋CT显示磨玻璃密度结节的对照研究

目的探讨低剂量及常规剂量螺旋CT扫描在检出磨玻璃密度肺结节(ground-glass nodule,GGN)的数目、边缘、内部结构与周边结构是否在统计学上存在显著差异。方法对经常规剂量(200mA)与层厚(5mm)螺旋CT扫描发现GGN患者56例同时行肺部低剂量(30mA)螺旋CT扫描,所得图像分别重建2mm、1mm,并按结节直径5mm,5~10mm,10mm不同大小分组,记录结节数目、边缘、内部特征以及周边结构等。结果低剂量与常规剂量螺旋CT扫描在检出GGN的数目、边缘、内部结构与周边结构无统计学意义。结论30mA低剂量螺旋CT扫描及层厚2mm可对GGN作出较好的判断,值得在早期肺癌筛查中应用。     

胸部螺旋CT技术参数的最佳选择及应用

本文目的在于通过对螺旋ＣＴ及常规ＣＴ影像进行对比。取得肺成像的最佳螺旋ＣＴ扫描条件。作者对钨丝模型、低对比模型、空气干燥膨胀肺标本在相同ｋＶ、ｍＡｓ条件下进行不同层厚、不同重建算法、不同螺距的螺旋ＣＴ扫描及常规ＣＴ扫描，分析两种扫描成像的分辨率差异，支气管及血管的可见度及噪音。结果显示薄层、超高分辨、高分辨重建算法时影像分辨率高。厚层、标准、软组织重建算法时两种扫描方式影像略有差异。作者还对３２例     

肺结节模型低剂量扫描及薄层重建对结节CT值的影响

目的 探讨低剂量CT扫描及薄层重建对肺结节CT值的影响.方法 选用CT值为-900 HU的塑料模拟肺基底,用直径不同的球体模拟肺结节(直径分别为20.0、10.0、5.0 mm及2.5 mm);每种直径的结节分别由3种不同材料(其CT值分别为:100 HU、60 HU及-100 HU).肺基底中央固定由上述3种材料分别制成的3个圆柱体(半径为10.0 mm、长为50.0 mm),其CT值作为每种材料的准确CT值.扫描参数:管电压120 kV、层厚5.0 mm、扫描间隔5.0 mm、螺距0.938:1、扫描时间0.8 s/r,改变管电流(20～120 mA,间隔10 mA)重复扫描肺结节模型.重建层厚0.625 mm,重建间隔0.625 mm,标准重建算法.在工作站上测量重建层厚为5 mm及0.625 mm条件下肺结节的CT值及SD值(直径为2.5 mm结节未测量).统计分析不同扫描剂量、不同重建层厚对结节CT值及标准差(SD)值的影响. 结果 ①在层厚为0.625 mm时:直径为5.0 mm、密度为-100 HU的结节在不同管电流条件下测得CT值之间有明显统计学差异(P=0.01＜0.05);其它结节在不同管电流条件下测得CT值无显著性差异(P值均＞0.05);所有结节的SD值在不同管电流条件下有明显的统计学差异(P值均＜0.05),且管电流越小,SD越大.②在层厚为5.0 mm时:相同密度结节(直径20.0 mm、10.0 mm、5.0 mm)CT值与标准CT值有明显的统计学差异(P值均＜0.05);采用薄层重建(层厚0.625 mm)时:直径为20.0 mm结节的CT值与标准CT值之间无明显的统计学差异(P＞0.05);直径为10.0 mm及5.0 mm结节的CT值与标准CT值仍有显著性差异(P＜0.05),但其CT值与标准CT值间的差值明显减小.结论 在低剂量条件下扫描可以比较准确地测得肺结节的CT值(直径较小、密度较低的结节不能准确测得结节CT值),同时随着剂量的减低噪声明显增大;采用薄层重建能提高小结节CT值测量的准确性.     

多层螺旋CT低剂量扫描诊断肺结节的价值

目的:探讨多层螺旋CT低剂量扫描诊断肺结节的价值。材料和方法:对147例肺部结节患者分别行多层螺旋CT低剂量扫描和常规剂量扫描,进行双盲法分析。结果:低剂量螺旋CT显示的结节较常规剂量扫描稍小,但二者对结节的检出无明显差异。结论:低剂量螺旋CT扫描与常规螺旋CT扫描发现肺结节的几率相仿,但可以有效地降低患者的受照剂量。     

16层螺旋CT中混叠假影的分析

目的认识CT图像中混叠假影(aliasingartifacts)的表现,并探讨成像参数对它的影响。方法使用PHILIPSBrilliance16SCT机器,在不同的扫描和重建参数条件下,扫描浸入水中的成人干颅,对所得原始横断图像在假影方面作出比较评价。结果大部分方案图像可见混叠假影,表现为在纵轴方向上有着径线急剧变化的高密度物体边缘发出的轮辐状假影。探测器组合16×1·5、重建层厚2mm时,螺距为0·3、0·6和0·9mm的图像均见混叠假影,其程度随螺距增大而加重;探测器组合16×0·75、螺距0·6时,重建层厚为0·8和1mm的图像均有混叠假影,且0·8mm图像较1mm图像严重,2mm图像未见混叠假影;螺距0·6、重建层厚2mm时,探测器组合16×0·75的图像未见混叠假影,16×1·5的图像存在该假影;重建间隔和管电流的变化对混叠假影无影响。结论采样不足引起混叠假影,可酌情选择薄的采集层厚、小的螺距和稍宽的重建层厚来抑制或消除这种假影。     

基于AI对肺小结节CT图像层厚与结节特征的检出效能影响

目的探讨基于深度学习的人工智能(AI)辅助诊断系统对肺小结节的CT检出效能,并观察不同图像层厚与结节特征对其检出效能的影响。方法选取我院胸部CT平扫检查200例,导入人工智能软件工作站中,根据不同CT图像层厚、结节类型、结节大小以及机型,分别记录AI与医师对结节的检出情况,比较其检出率、灵敏度、假阳性率以及检出时间,并比较AI及医师的检出情况。结果AI在厚层(5 mm)与薄层(1.5 mm)对于pGGN的检出差异无统计学意义(P>0.05);AI在厚层对于mGGN的检出高于薄层(t=2.282,P=0.025);AI在薄层对于SN的检出明显高于厚层(t=-10.377,P<0.001),AI在薄层对pGGN、mGGN及SN检出灵敏度均明显高于厚层,经统计学分析分别为,t=-4.823,P<0.001,t=-4.048,P<0.001,t=-10.186,P<0.001。AI在64排CT机型及16排CT机型中共检出肺小结节分别为491、627枚,t=-0.428,P=0.427,P>0.05,二者差异无统计学意义。结论CT扫描层厚越厚,基于深度学习的AI对肺小结节检出的漏诊率越高,CT扫描层厚越薄,AI对肺小结节的检出越有利,但其假阳性率和检出时间也随之增高。     

Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness

The purpose of this study was to find out if the use of 1.25-mm collimated thin-slice technique helps to detect more small pulmonary lung nodules than the use of 5 mm. A total of 100 patient examinations that allowed a reconstruction of 1.25-mm slice thickness in addition to the standard of 5-mm slices were included in a prospective study. Acquisition technique included four rows of 1-mm slices. Two sets of contiguous images were reconstructed and compared with 1.25- and 5-mm slice thickness, respectively. Two radiologists performed a film-based analysis of the images. The size and the confidence of the seen nodules were reported. We did not perform a histological verification, according to the normal clinical procedure, although it would be optimal regarding research. Statistical analysis was performed by using longitudinal analysis described by Brunner and Langer [10]. In addition, sensitivity, specificity, negative predictive value and positive predictive value were calculated for each reader using the 1.25-mm sections as the gold standard. As an index for concordance the kappa value was used. A value of p<0.05 was regarded as significant. In 37 patients pulmonary nodules were detected. Twenty-four patients showed more than one nodule; among these, 7 patients had disseminated disease and were excluded from the study. Pulmonary nodules larger than 10 mm in size were equally well depicted with both modalities, whereas lesions smaller than 5 mm in size were significantly better depicted with 1.25 mm (p<0.05). Using 1.25 mm as the gold standard, sensitivity for 5-mm reconstruction interval was 88 and 86% for observers A and B, respectively. No false-positive results were reported for 5-mm sections. Interobserver agreement for nodule detection determined for 1.25-mm reconstruction intervals showed a k value of 0.753, indicating a good agreement, and 0.562 for 5-mm reconstruction intervals, indicating a moderate agreement. Brunner and Langer [10] analysis showed significant differences for slice thickness and no significant difference between the observers. Reduced slice thickness demonstrated an improvement of small nodule detection, confidence levels, and interobserver agreement. Application of thin-slice multidetector-row CT may raise the sensitivity for lung nodule detection, although the higher detection rate of smaller nodules has to be evaluated from a clinical perspective and remains problematic about how the detection of small nodules will effect patient outcome.     

MSCT重建参数对肺结节定量测量的影响

目的:评价MSCT不同的重建参数层厚、重建野、迭代算法(iDose)重建级别对肺结节各项指标测量的影响,并比较不同iDose级别与肺结节标准算法(standard)对于肺结节体积测量的一致性。方法:搜集2019年7月-2020年1月首次在本院行CT检查的56名患者,共计82个肺结节。皆采用8组不同重建参数进行处理,根据单因素变量分组并命名为层厚组、重建野组、iDose级别组。测量体积、最大直径、最大表面积、平均/最大/最小CT值6项指标,采用秩和检验对不同组内进行差异性分析;并采用Bland-Altman方法对不同iDose级别与standard算法下测得的体积进行一致性分析。结果:不同层厚组、重建野组内测量的各项指标以及iDose级别组中最大、最小、平均CT值间的差异具有统计学意义(P<0.001)。不同iDose级别组中最大径线、体积、最大面积之间的差异无统计学意义(P=0.505、0.949、0.294)。Bland-Altman一致性散点分析图表明Standard算法与iDose5级别算法测量肺结节体积有较好的一致性。结论:不同的重建参数对肺结节各项指标的测量有一定影响。iDose5与Standard算法对于肺结节体积的测量有较好的一致性。推荐采用层厚1.25 mm、重建野18~36 cm、iDose级别3~4的重建参数来对肺结节进行后处理。     

Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy

PURPOSE: To evaluate the effect of various multi-detector row computed tomographic (CT) reconstruction parameters and nodule segmentation thresholds on the accuracy of volumetric measurement of synthetic lung nodules. MATERIALS AND METHODS: Synthetic lung nodules of four different diameters (3.2, 4.8, 6.4, and 12.7 mm) were scanned with multi-detector row CT. Images were reconstructed at various section thicknesses (0.75, 1.0, 2.0, 3.0, and 5.0 mm), fields of view (30, 20, and 10 cm), and reconstruction intervals (0.5, 1.0, and 2.0 mm). The nodules were segmented from the simulated background lung region by using four segmentation thresholds (-300, -400, -500, and -600 HU), and their volumes were estimated and compared with a reference standard (measurements according to fluid displacement) by computing the absolute percentage error (APE). APE was regressed against nodule size, and multivariate analysis of variance (MANOVA) was performed with APE as the dependent variable and with four within-subject factors (field of view, reconstruction interval, threshold, and section thickness). RESULTS: The MANOVA demonstrated statistically significant effects for threshold (P = .02), section thickness (P < .01), and interaction of threshold and section thickness (P = .04). The regression of mean APE values on nodule size indicates that APE progressively increases with decreasing synthetic nodule size (R2 = 0.99, P < .01). CONCLUSION: For accurate measurement of lung nodule volume, it is critical to select a section thickness and/or segmentation threshold appropriate for the size of a nodule.     

CT densitometry of pulmonary nodules in a frozen human thorax

The influence of (1) calcium concentration, (2) exposure technique, (3) reconstruction algorithm, (4) nodule size, and (5) nodule location on the CT attenuation values (CT density) of pulmonary nodules was examined in a frozen human thorax. Nodules with calcium concentrations of 0-310 mg/ml and diameters of either 0.95 or 1.59 cm were inserted into a frozen, unembalmed human thorax. The nodules were placed either at the lung apex or 4 cm below the tracheal carina. Each nodule was scanned on a GE CT 9800 scanner; five different exposure techniques were used. The slice thickness was uniformly 1.5 mm. As expected, increasing the kilovoltage caused a significant decrease in CT nodule density in all nodules with calcium concentrations greater than 80 mg/ml. The inverse relationship between kilovoltage and nodule density was exaggerated with increasing calcium concentration. A high-resolution (bone) algorithm gave a significantly higher CT number than did a smoothed (standard) algorithm, regardless of nodule size and location, but this difference could be attributed almost entirely to the edge-enhancement effect of the bone algorithm. The CT density of the larger nodules was significantly higher than that of the smaller nodules at calcium concentrations greater than 65 mg/ml for both standard and bone algorithms. Densities were significantly higher in the mid lung than in the apex with a standard algorithm, but this was not the case with a bone algorithm. The GE CT 9800 scanner had a linear response between CT density and increasing calcium concentration within the confines of a human thorax. A high-resolution (bone) reconstruction algorithm has higher spatial resolution but does show an edge-enhancing effect not found with the smoothed algorithm. Two major variables in CT densitometry for pulmonary nodules are the kilo electron voltage of the X-ray beam and the reconstruction algorithm used; these two parameters should be standardized, with a high kilovoltage and high-resolution algorithm favored on the GE CT 9800 scanner.     

Ultra-low-dose MDCT of the chest: influence on automated lung nodule detection.

OBJECTIVE: To evaluate the relationship between CT dose and the performance of a computer-aided diagnosis (CAD) system, and to determine how best to minimize patient exposure to ionizing radiation while maintaining sufficient image quality for automated lung nodule detection, by the use of lung cancer screening CT. MATERIALS AND METHODS: Twenty-five asymptomatic volunteers participated in the study. Each volunteer underwent a low-dose CT scan without contrast enhancement (multidetector CT with 16 detector rows, 1.25 mm section thickness, 120 kVp, beam pitch 1.35, 0.6 second rotation time, with 1.25 mm thickness reconstruction at 1.25 mm intervals) using four different amperages 32, 16, 8, and 4 mAs. All series were analyzed using a commercially available CAD system for automatic lung nodule detection and the results were reviewed by a consensus reading by two radiologists. The McNemar test and Kappa analysis were used to compare differences in terms of the abilities to detect pulmonary nodules. RESULTS: A total of 78 non-calcified true nodules were visualized in the 25 study subjects. The sensitivities for nodule detection were as follows: 72% at 32 mAs, 64% at 16 mAs, 59% at 8 mAs, and 40% at 4 mAs. Although the overall nodule-detecting performance was best at 32 mAs, no significant difference in nodule detectability was observed between scans at 16 mAs or 8 mAs versus 32 mAs. However, scans performed at 4 mAs were significantly inferior to those performed at 32 mAs (p < 0.001). CONCLUSION: Reducing the radiation dose (i.e. reducing the amperage) lowers lung nodule detectability by CAD. However, relatively low dose scans were found to be acceptable and to cause no significant reduction in nodule detectability versus usual low-dose CT.     

Comparison of various parameters (pitch 1 and 2) in the study of the lung with spiral computerized tomography]

INTRODUCTION: In Spiral CT, the pitch is the ratio of the distance the tabletop travels per 360 degrees rotation to nominal slice width, expressed in mm. Performing Spiral CT examinations with pitch 2 allows to reduce examination time, exposure and contrast dose, and X-ray tube overload. We investigated the yield of pitch 2 in lung parenchyma studies, particularly relative to diagnostic image quality. MATERIAL AND METHODS: Thirty patients were submitted to Spiral CT with pitch 1 [10 mm slice thickness, 10 mm/s table feed; 10 mm (a') and 5 mm (a") reconstruction index: protocol A] and with pitch 2 [10 mm slice thickness, 20 mm/s table feed; 10 mm (b') and 5 mm (b") reconstruction index: protocol B]. Five expert radiologists evaluated the images separately and blindly, grading noise, bronchial wall resolution and diagnostic yield on a 0-5 point scale. The results of protocol A versus protocol B images were analyzed statistically using the Mann-Whitney U-test. RESULTS: The mean scores for each parameter ranged 4.13 (.70 standard deviation) for protocol B with 5 mm reconstruction index (b") to 4.81 (.44 standard deviation) for protocol A with 10 mm reconstruction index (a'). These values (max: 5) indicate very positive results on both protocol A and B images. There were no statistically significant interprotocol differences, except for bronchial wall resolution, in favor of protocol A with 5 mm reconstruction index (a") (p = .025), and for diagnostic yield, in favor of protocol A with 10 mm reconstruction index (a') (p = .018). CONCLUSIONS: Spiral CT with pitch 2 is a reliable tool for lung parenchyma studies which permits to reduce examination time and contrast dose, as well as X-ray tube overload and exposure dose.     

Detection of pulmonary nodules with overlapping vs non-overlapping image reconstruction at spiral CT

The aim of this study was to analyze whether overlapping image reconstruction increases numbers of pulmonary nodules detected at helical CT. Forty-eight helical CT scans (21with a slice thickness of 10 mm; 27 with a slice thickness of 5 mm) of patients with known pulmonary nodules were reconstructed both with overlapping and non-overlapping image reconstruction. Two readers recorded number and size of pulmonary nodules as well as diagnostic confidence. With overlapping image reconstruction each reader diagnosed more pulmonary nodules (slice thickness 10 mm: +24.0 and +26.7 %, both p < 0.01; slice thickness 5 mm: +9.5 and +11.9 %, both not significant) and more “definite” nodules (slice thickness 10 mm: +20.3 %, p < 0.05, and +30.8 %, p < 0.005; slice thickness 5 mm: +18.0 and +17.0 %, both p < 0.05). Nodules diagnosed with overlapping image reconstruction only were almost exclusively smaller than the slice thickness. The increase in number of nodules detected was not associated with a decrease in diagnostic confidence. Overlapping image reconstruction improves detection of pulmonary nodules smaller than the slice thickness at spiral CT. Received: 8 April 1998; Revision received: 16 July 1998; Accepted: 9 September 1998     

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 16.4mm. 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 5mm 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.     

Automated volumetry of pulmonary nodules on multidetector CT: influence of slice thickness, reconstruction algorithm and tube current. Preliminary results

PURPOSE: To evaluate the influence of slice thickness, reconstruction algorithm and tube current (mA) on the performance of a software package in determining the volume of solid pulmonary nodules on multidetector-row computed tomography (MDCT). MATERIALS AND METHODS: A chest phantom containing artificial solid nodules with known volume was imaged with two MDCT scans at 100 and 40 mAs (200 mA and 80 mA, 0.5-s rotation time), respectively. Data were reconstructed with slice thicknesses of 1.25 and 2.5 mm and five different algorithms. The volumes of three nodules (juxtavascular, intraparenchymal, juxtapleural) were calculated using three-dimensional (3D) volumetric software. Differences between estimated and real volume were reported for each nodule and reconstruction set. RESULTS: The software segmented all nodules on 1.25-mm-thick reconstructions, independently from the mAs. It did not segment the juxtapleural nodule on 2.5-mm-thick reconstructions at 40 mAs. Mean values of the differences, which better approximated the real volume of the nodules, were obtained with high-spatial-resolution algorithms on both 100 and 40 mAs images at 1.25-mm slice thickness. CONCLUSIONS: Slice thickness, reconstruction algorithm and tube current can affect the 3D volume measurement of solid nodules. The best performance of the software, on both 100 and 40 mAs images, was observed with a slice thickness of 1.25 mm and high-spatial-resolution algorithms.