Pulmonary Nodules: Sensitivity of Maximum Intensity Projection versus That of Volume Rendering of 3D Multidetector CT Data

Purpose: To prospectively compare maximum intensity projection (MIP) and volume rendering (VR) of multidetector computed tomographic (CT) data for the detection of small intrapulmonary nodules.

Materials and Methods: This institutional review board–approved prospective study included 20 oncology patients (eight women and 12 men; mean age, 56 years ± 16 [standard deviation]) who underwent clinically indicated standard-dose thoracic multidetector CT and provided informed consent. Transverse thin slabs of the chest (thickness, 7 mm; reconstruction increment, 3.5 mm) were created by using MIP and VR techniques to reconstruct CT data (collimation, 16 × 0.75 mm) and were reviewed in interactive cine mode. Mean, minimum, and maximum reading time per examination and per radiologist was documented. Three radiologists digitally annotated all nodules seen in a way that clearly determined their locations. The maximum number of nodules detected by the three observers and confirmed by consensus served as the reference standard. Descriptive statistics were calculated, with P < .05 indicating a significant difference. The Wilcoxon matched-pairs signed rank test and confidence intervals for differences between methods were used to compare the sensitivities of the two methods.

Results: VR performed significantly better than MIP with regard to both detection rate (P < .001) and reporting time (P < .001). The superiority of VR was significant for all three observers and for nodules smaller than 11 mm in diameter and was pronounced for perihilar nodules (P = .023). Sensitivities achieved with VR ranged from 76.5% to 97.3%, depending on nodule size.

Conclusion: VR is the superior reading method compared with MIP for the detection of small solid intrapulmonary nodules.

© RSNA, 2007

References

  • 1 Naidich DP. Volumetric scans change perceptions in thoracic CT. Diagn Imaging (San Franc) 1993; 15: 70–74. Google Scholar
  • 2 Seltzer SE, Judy PF, Adams DF, et al. Spiral CT of the chest: comparison of cine and film-based viewing. Radiology 1995; 197: 73–78. LinkGoogle Scholar
  • 3 Napel S, Rubin GD, Jeffrey RB Jr. STS-MIP: a new reconstruction technique for CT of the chest. J Comput Assist Tomogr 1993; 17: 832–838. Crossref, MedlineGoogle Scholar
  • 4 Diederich S, Lentschig MG, Winter F, Roos N, Bongartz G. Detection of pulmonary nodules with overlapping vs non-overlapping image reconstruction at spiral CT. Eur Radiol 1999; 9: 281–286. Crossref, MedlineGoogle Scholar
  • 5 Rubin GD, Lyo JK, Paik DS, et al. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 2005; 234: 274–283. LinkGoogle Scholar
  • 6 Ravenel JG, McAdams HP, Remy-Jardin M, Remy J. Multidimensional imaging of the thorax: practical applications. J Thorac Imaging 2001; 16: 269–281. Crossref, MedlineGoogle Scholar
  • 7 Remy-Jardin M, Remy J, Giraud F, Marquette CH. Pulmonary nodules: detection with thick-section spiral CT versus conventional CT. Radiology 1993; 187: 513–520. LinkGoogle Scholar
  • 8 Schaefer-Prokop C, Prokop M. New imaging techniques in the treatment guidelines for lung cancer. Eur Respir J Suppl 2002; 35: 71s–83s. MedlineGoogle Scholar
  • 9 Wormanns D, Ludwig K, Beyer F, Heindel W, Diederich S. Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT. Eur Radiol 2005; 15: 14–22. Crossref, MedlineGoogle Scholar
  • 10 Abe H, Ishida T, Shiraishi J, et al. Effect of temporal subtraction images on radiologists' detection of lung cancer on CT: results of the observer performance study with use of film computed tomography images. Acad Radiol 2004; 11: 1337–1343. Crossref, MedlineGoogle Scholar
  • 11 Armato SG 3rd, Roy AS, MacMahon H, et al. Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program. Acad Radiol 2005; 12: 337–346. Crossref, MedlineGoogle Scholar
  • 12 Kozuka T, Johkoh T, Hamada S, et al. Detection of pulmonary metastases with multi-detector row CT scans of 5-mm nominal section thickness: autopsy lung study. Radiology 2003; 226: 231–234. LinkGoogle Scholar
  • 13 Gruden JF, Ouanounou S, Tigges S, Norris SD, Klausner TS. Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT. AJR Am J Roentgenol 2002; 179: 149–157. Crossref, MedlineGoogle Scholar
  • 14 Lawler LP, Wood SA, Pannu HK, Fishman EK. Computer-assisted detection of pulmonary nodules: preliminary observations using a prototype system with multidetector-row CT data sets. J Digit Imaging 2003; 16: 251–261. Crossref, MedlineGoogle Scholar
  • 15 Marten K, Grillhosl A, Seyfarth T, Obenauer S, Rummeny EJ, Engelke C. Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings. Eur Radiol 2005; 15: 203–212. Crossref, MedlineGoogle Scholar
  • 16 Kim JS, Kim JH, Cho G, Bae KT. Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval—initial results. Radiology 2005; 236: 295–299. LinkGoogle Scholar
  • 17 Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm—preliminary results. Radiology 2005; 236: 286–293. LinkGoogle Scholar
  • 18 Coakley FV, Cohen MD, Johnson MS, Gonin R, Hanna MP. Maximum intensity projection images in the detection of simulated pulmonary nodules by spiral CT. Br J Radiol 1998; 71: 135–140. Crossref, MedlineGoogle Scholar
  • 19 Diederich S, Lentschig MG, Overbeck TR, Wormanns D, Heindel W. Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting. Eur Radiol 2001; 11: 1345–1350. Crossref, MedlineGoogle Scholar
  • 20 Eibel R, Turk TR, Kulinna C, Herrmann K, Reiser MF. Multidetector-row CT of the lungs: multiplanar reconstructions and maximum intensity projections for the detection of pulmonary nodules [in German]. Rofo 2001; 173: 815–821. Crossref, MedlineGoogle Scholar
  • 21 Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Earlbaum Associates, 1988. Google Scholar
  • 22 Mori K, Sasagawa M, Moriyama N. Detection of nodular lesions in the lung using helical computed tomography: comparison of fast couch speed technique with conventional computed tomography. Jpn J Clin Oncol 1994; 24: 252–257. MedlineGoogle Scholar
  • 23 Croisille P, Souto M, Cova M, et al. Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT—work in progress. Radiology 1995; 197: 397–401. LinkGoogle Scholar
  • 24 Fischbach F, Knollmann F, Griesshaber V, Freund T, Akkol E, Felix R. Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness. Eur Radiol 2003; 13: 2378–2383. Crossref, MedlineGoogle Scholar
  • 25 Kuszyk BS, Heath DG, Johnson PT, Eng J, Fishman EK. CT angiography with volume rendering for quantifying vascular stenoses: in vitro validation of accuracy. AJR Am J Roentgenol 1999; 173: 449–455. Crossref, MedlineGoogle Scholar
  • 26 Brown MS, Goldin JG, Rogers S, et al. Computer-aided lung nodule detection in CT: results of large-scale observer test. Acad Radiol 2005; 12: 681–686. Crossref, MedlineGoogle Scholar
  • 27 Armato SG 3rd, Li F, Giger ML, MacMahon H, Sone S, Doi K. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 2002; 225: 685–692. LinkGoogle Scholar

Article History

Published in print: 2007