Adaptive iterative dose reduction 3D (AIDR 3D) vs. filtered back projection: radiation dose reduction capabilities of wide volume and helical scanning techniques on area-detector CT in a chest phantom study

Background Computed tomography (CT) has important roles for lung cancer screening, and therefore radiation dose reduction by using iterative reconstruction technique and scanning methods receive widespread attention. Purpose To evaluate the effect of two reconstruction techniques (filtered back projection [FBP] and adaptive iterative dose reduction using three-dimensional processing [AIDR 3D]) and two acquisition techniques (wide-volume scan [WVS] and helical scan as 64-detector-row CT [64HS]) on the lung nodule identifications of using a chest phantom. Material and Methods A chest CT phantom including lung nodules was scanned using WVS and 64HS at nine different tube currents (TCs; range, 270–10 mA). All CT datasets were reconstructed with AIDR 3D and FBP. Standard deviation (SD) measurements by region of interest placement and qualitative nodule identifications were statistically compared. 64HS and WVS were evaluated separately, and FBP images acquired with 270 mA was defined as the standard reference. Results SDs of all datasets with AIDR 3D showed no significant differences (P > 0.05) with standard reference. When comparing nodule identifications, area under the curve on WVS with AIDR 3D with TC <30 mA, on 64HS with AIDR 3D with TC <40 mA, and on reconstructions with FBP and each scan method with TC <60 mA was significantly lower than with standard reference (P < 0.05). With the same TC and reconstruction, SDs and nodule identifications of WVS were not significantly different from 64HS (P > 0.05). Conclusion In term of SD of lung parenchyma and nodule identification, AIDR 3D can achieve more radiation dose reduction than FBP and there is no significant different between WVS and 64HS.

[1]  Noriyuki Tomiyama,et al.  Adaptive statistical iterative reconstruction technique for pulmonary CT: image quality of the cadaveric lung on standard- and reduced-dose CT. , 2010, Academic radiology.

[2]  D. Berry,et al.  Benefits and harms of CT screening for lung cancer: a systematic review. , 2012, JAMA.

[3]  Jiang Hsieh,et al.  Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique. , 2010, Radiology.

[4]  K. Murata,et al.  Image Quality of 320–Detector Row Wide-Volume Computed Tomography With Diffuse Lung Diseases: Comparison With 64–Detector Row Helical CT , 2012, Journal of computer assisted tomography.

[5]  Alvin C. Silva,et al.  Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. , 2010, AJR. American journal of roentgenology.

[6]  Takeshi Yoshikawa,et al.  Emphysema quantification by low-dose CT: potential impact of adaptive iterative dose reduction using 3D processing. , 2012, AJR. American journal of roentgenology.

[7]  Kenya Murase,et al.  Quantitatively assessed CT imaging measures of pulmonary interstitial pneumonia: effects of reconstruction algorithms on histogram parameters. , 2010, European journal of radiology.

[8]  H. Svanholm,et al.  Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic , 1989, APMIS : acta pathologica, microbiologica, et immunologica Scandinavica.

[9]  Takeshi Yoshikawa,et al.  Adaptive iterative dose reduction using 3D processing for reduced- and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings. , 2012, AJR. American journal of roentgenology.

[10]  Y. Ohno,et al.  Capability of abdominal 320-detector row CT for small vasculature assessment compared with that of 64-detector row CT. , 2011, European journal of radiology.

[11]  H. Kauczor,et al.  Quantitative analysis of emphysema in 3D using MDCT: influence of different reconstruction algorithms. , 2008, European journal of radiology.

[12]  S. Matsumoto,et al.  Influence of detector collimation and beam pitch for identification and image quality of ground-glass attenuation and nodules on 16- and 64-detector row CT systems: experimental study using chest phantom. , 2007, European journal of radiology.

[13]  Mathias Prokop,et al.  Pulmonary nodules: sensitivity of maximum intensity projection versus that of volume rendering of 3D multidetector CT data. , 2007, Radiology.

[14]  M. Kalra,et al.  Radiation Dose Reduction With Chest Computed Tomography Using Adaptive Statistical Iterative Reconstruction Technique: Initial Experience , 2010, Journal of computer assisted tomography.

[15]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[16]  Masashi Takahashi,et al.  Lung image quality with 320-row wide-volume CT scans: the effect of prospective ECG-gating and comparisons with 64-row helical CT scans. , 2012, Academic radiology.

[17]  J. Ferlay,et al.  Global Cancer Statistics, 2002 , 2005, CA: a cancer journal for clinicians.

[18]  S. Matsumoto,et al.  Iterative reconstruction technique vs filter back projection: utility for quantitative bronchial assessment on low-dose thin-section MDCT in patients with/without chronic obstructive pulmonary disease , 2014, European Radiology.

[19]  H. Bauknecht,et al.  Image quality and radiation exposure in 320-row temporal bone computed tomography. , 2010, Dento maxillo facial radiology.

[20]  M. L. R. D. Christenson,et al.  Pulmonary Nodules: Sensitivity of Maximum Intensity Projection versus That of Volume Rendering of 3D Multidetector CT Data , 2008 .

[21]  J. Remy,et al.  Chest computed tomography using iterative reconstruction vs filtered back projection (Part 1): evaluation of image noise reduction in 32 patients , 2011, European Radiology.

[22]  F. Diekmann,et al.  Dose Exposure of Patients Undergoing Comprehensive Stroke Imaging by Multidetector-Row CT: Comparison of 320-Detector Row and 64-Detector Row CT Scanners , 2010, American Journal of Neuroradiology.

[23]  Effect of reconstruction algorithm on image quality and identification of ground-glass opacities and partly solid nodules on low-dose thin-section CT: experimental study using chest phantom. , 2010, European journal of radiology.

[24]  M. Roizen Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[25]  Michael F McNitt-Gray,et al.  AAPM/RSNA Physics Tutorial for Residents: Topics in CT. Radiation dose in CT. , 2002, Radiographics : a review publication of the Radiological Society of North America, Inc.

[26]  M. Mascalchi,et al.  Four-Year Results of Low-Dose CT Screening and Nodule Management in the ITALUNG Trial , 2013, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[27]  Paul Pinsky,et al.  Baseline findings of a randomized feasibility trial of lung cancer screening with spiral CT scan vs chest radiograph: the Lung Screening Study of the National Cancer Institute. , 2004, Chest.

[28]  Hiroto Hatabu,et al.  Use of 3D adaptive raw-data filter in CT of the lung: effect on radiation dose reduction. , 2008, AJR. American journal of roentgenology.