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 x 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.

[1]  G D Rubin,et al.  STS-MIP: a new reconstruction technique for CT of the chest. , 1993, Journal of computer assisted tomography.

[2]  M. Rifkin.  Ultrasound, MR back up prostate cancer diagnosis. , 1993, Diagnostic imaging.

[3]  Stefan Tigges,et al.  Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT. , 2002, AJR. American journal of roentgenology.

[4]  R Kikinis,et al.  Spiral CT of the chest: comparison of cine and film-based viewing. , 1995, Radiology.

[5]  Karin A. Herrmann,et al.  Mehrschicht-Spiral-CT der Lunge: Multiplanare Rekonstruktionen und Maximum-Intensitäts-Projektionen in der Detektion von Lungenrundherden , 2001 .

[6]  Sumit K. Shah,et al.  Computer-aided lung nodule detection in CT: results of large-scale observer test. , 2005, Academic radiology.

[7]  J. Remy,et al.  Multidimensional Imaging of the Thorax: Practical Applications , 2001, Journal of thoracic imaging.

[8]  K. Bae,et al.  Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results. , 2005, Radiology.

[9]  M. Cohen,et al.  Maximum intensity projection images in the detection of simulated pulmonary nodules by spiral CT. , 1998, The British journal of radiology.

[10]  Kunio Doi,et al.  Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program(1). , 2005, Academic radiology.

[11]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[12]  K. Marten,et al.  Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings , 2005, European Radiology.

[13]  S. Diederich,et al.  Detection of pulmonary nodules with overlapping vs non-overlapping image reconstruction at spiral CT , 1999, European Radiology.

[14]  K. Mori,et al.  Detection of nodular lesions in the lung using helical computed tomography: comparison of fast couch speed technique with conventional computed tomography. , 1994, Japanese journal of clinical oncology.

[15]  W. Heindel,et al.  Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT , 2004, European Radiology.

[16]  K. Bae,et al.  Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results. , 2005, Radiology.

[17]  E. Fishman,et al.  CT angiography with volume rendering for quantifying vascular stenoses: in vitro validation of accuracy. , 1999, AJR. American journal of roentgenology.

[18]  W. Heindel,et al.  Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting , 2001, European Radiology.

[19]  M. Reiser,et al.  [Multidetector-row CT of the lungs: Multiplanar reconstructions and maximum intensity projections for the detection of pulmonary nodules]. , 2001, RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin.

[20]  Kunio Doi,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. , 2004, Academic radiology.

[21]  D. Naidich.  Volumetric scans change perceptions in thoracic CT. , 1993, Diagnostic imaging.

[22]  M. Prokop,et al.  New imaging techniques in the treatment guidelines for lung cancer , 2002, European Respiratory Journal.

[23]  F. Fischbach,et al.  Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness , 2003, European Radiology.

[24]  P Croisille,et al.  Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT. Work in progress. , 1995, Radiology.

[25]  Noriyuki Tomiyama,et al.  Detection of pulmonary metastases with multi-detector row CT scans of 5-mm nominal section thickness: autopsy lung study. , 2003, Radiology.

[26]  Elliot K. Fishman,et al.  Erratum: Computer-Assisted Detection of Pulmonary Nodules: Preliminary Observations Using a Prototype System with Multidetector-Row CT Data Sets (Journal of Digital Imaging (September 2003) 16:3 (251) DOI: 10.1007/s10278-003-1654-y) , 2003 .

[27]  J. Remy,et al.  Pulmonary nodules: detection with thick-section spiral CT versus conventional CT. , 1993, Radiology.

[28]  Anthony J. Sherbondy,et al.  Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. , 2005, Radiology.

[29]  S. Armato,et al.  Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. , 2002, Radiology.