Pulmonary Nodules: Sensitivity of MaximumIntensityProjectionversus ThatofVolumeRenderingof3D MultidetectorCTData 1

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.

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

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

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

[4]  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.

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

[6]  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.

[7]  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.

[8]  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.

[9]  Elliot K. Fishman,et al.  Computer-Assisted Detection of Pulmonary Nodules: Preliminary Observations Using a Prototype System with Multidetector-Row CT Data Sets , 2003, Journal of Digital Imaging.

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

[11]  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.

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

[13]  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.

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

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

[16]  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.

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

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

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

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

[22]  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.

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

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

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