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

https://doi.org/10.1016/j.ejrad.2006.11.017Get rights and content

Abstract

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.

Introduction

Small pulmonary nodules are frequently discovered by computed tomography (CT) of the chest, and the majority of these nodules turn out to be benign [1], [2]. Naturally, differentiation between benign and malignant nodules is of paramount importance, but this is difficult, and many studies have focused on resolving this problem. These studies have examined the morphology involved by evaluating the edge or internal characteristics of the nodules [3], [4], the relationships of the nodules to normal anatomical structures such as bronchi [5] and the enhancement pattern on CT [6], [7]. One of the most reliable criteria indicating benign lesions is the presence of calcification [3], [8]. But many nodules cannot be determined as benign lesions by CT, or by positron emission tomography or biopsy. Consequently, if the potential for malignancy cannot be ruled out, these nodules are usually followed in size by CT.

It is generally agreed that stability in the size of a nodule for more than 2 years is a good predictor of nodule dignity [8], [9]. Although the size of pulmonary nodules is usually determined and compared using two-dimensional CT evaluation, such measurements are not reliable for small nodules [10]. More reliable estimation of pulmonary nodule volume can now be obtained using three-dimensional volumetric measurement by computer software, with users recommending a thin slice-thickness and small field-of-view (FOV) [11], [12], [13]. CT images can be acquired using different parameters, and thus we investigated the effect of changing the reconstruction parameters on computer-aided volumetric measurements.

Section snippets

Materials and methods

The study involved 50 nonconsecutive patients (27 men, 23 women; average age, 58.3 years; range, 16–82 years) who were examined between September 2004 and February 2005. CT examinations were performed for clinical indication in all patients. If the applicable nodules were found after CT examinations, the data were used for this retrospective study. Patients with solid pulmonary nodules less than 2 cm in diameter, and with clear and regular margins were examined, while those with ground-glass

Unsuccessful volume measurement

All 50 nodules were solid, and located in the peripheral part of the lungs. The diameter of the 50 nodules was 9.1 ± 3.4 mm on average, ranging from 4.1 to 19.2 mm. Volume measurement for all 48 reconstruction parameters could be successfully performed in 39 of 50 nodules, and the diameter of these 39 nodules was 8.7 ± 2.7 mm on average, ranging from 4.3 to 16.4 mm. The number of unsuccessful volume measurements for each reconstruction parameter set, of the 50 nodules, is summarized in Table 1. For 11

Discussion

Recently developed computer software that automatically calculates three-dimensional nodule volume is now available. The software extracts the pulmonary nodules by recognizing density, gradient strength and a shape constraint of the nodule in a slice-by-slice manner, and there is no statistically significant difference between the nodule areas delineated by a radiologist and those obtained by the software [15]. The repeatability of three-dimensional volume measurement by automatic volumetric

Conclusion

Volumetric measurements of pulmonary nodules can change by changing the reconstruction parameters. In the present study, the measured volume of a nodule obtained using the high-spatial frequency algorithm and non-overlapping reconstruction was significantly larger than that using the low-frequency spatial algorithm and overlapping reconstruction. Although differences in slice thickness and FOV did not affect volume measurements, the frequencies of the maximum measured volume occurring with a 5 

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