PURPOSE: Dedicated breast CT (bCT) is an emerging technology with the potential to improve the detection of breast cancer in screening and diagnostic capacities. Typically, the 3D volume reconstructed from the scanner is displayed as sectional images. The purpose of this study was to evaluate the effect of section thickness on the detectability of simulated masses using a prewhitened matched filter (PWMF) as a model observer. METHODS: A breast CT scanner has been designed and fabricated in the authors' laboratory with more than 200 women imaged in IRB-approved phase I and phase II trials to date. Of these, 151 bilateral data sets were selected on the basis of low artifact content, sufficient breast coverage, and excluding cases with breast implants. BIRADS breast density ratings were available for 144 of these patients. Spherical mass lesions of diameter 1, 2, 3, 5, 11, and 15 mm were mathematically generated and embedded at random locations within the parenchymal region of each bCT volume. Microcalcifications were not simulated in this study. For each viewing plane (sagittal, axial, and coronal) and section thickness (ranging from 0.3 to 44 mm), section images of the breast parenchyma containing the lesion were generated from the reconstructed bCT data sets by averaging voxels over the length of the section. Using signal known exactly (SKE) model observer methodology, receiver operating characteristic (ROC) curve analysis was performed on each generated projected image using a PWMF based model observer. ROC curves were generated for each breast data set, and the area under the ROC curve (AUC) was evaluated as well as the sensitivity at 95% specificity. RESULTS: For all lesion sizes, performance rises modestly to a peak before falling off substantially as section thickness increases over the range of the study. We find that the optimal section thickness tracks the size of the lesion to be detected linearly with a small positive offset and slopes ranging from 0.27 to 0.44. No significant differences were observed between left and right breasts. Performance measures are negatively correlated with measures of breast density, with an average correlation coefficient of -0.48 for the BIRADS breast density score and -0.81 for the proportion of glandular tissue in the breast interior. CONCLUSIONS: This study shows quantitatively how PWMF detection performance of a known lesion size is influenced by section thickness in dedicated breast CT. While the optimal section thickness is tuned to the size of the lesion being detected, overall performance is more robust for thin section images compared to thicker images.
PURPOSE: Dedicated breast CT (bCT) is an emerging technology with the potential to improve the detection of breast cancer in screening and diagnostic capacities. Typically, the 3D volume reconstructed from the scanner is displayed as sectional images. The purpose of this study was to evaluate the effect of section thickness on the detectability of simulated masses using a prewhitened matched filter (PWMF) as a model observer. METHODS: A breast CT scanner has been designed and fabricated in the authors' laboratory with more than 200 women imaged in IRB-approved phase I and phase II trials to date. Of these, 151 bilateral data sets were selected on the basis of low artifact content, sufficient breast coverage, and excluding cases with breast implants. BIRADS breast density ratings were available for 144 of these patients. Spherical mass lesions of diameter 1, 2, 3, 5, 11, and 15 mm were mathematically generated and embedded at random locations within the parenchymal region of each bCT volume. Microcalcifications were not simulated in this study. For each viewing plane (sagittal, axial, and coronal) and section thickness (ranging from 0.3 to 44 mm), section images of the breast parenchyma containing the lesion were generated from the reconstructed bCT data sets by averaging voxels over the length of the section. Using signal known exactly (SKE) model observer methodology, receiver operating characteristic (ROC) curve analysis was performed on each generated projected image using a PWMF based model observer. ROC curves were generated for each breast data set, and the area under the ROC curve (AUC) was evaluated as well as the sensitivity at 95% specificity. RESULTS: For all lesion sizes, performance rises modestly to a peak before falling off substantially as section thickness increases over the range of the study. We find that the optimal section thickness tracks the size of the lesion to be detected linearly with a small positive offset and slopes ranging from 0.27 to 0.44. No significant differences were observed between left and right breasts. Performance measures are negatively correlated with measures of breast density, with an average correlation coefficient of -0.48 for the BIRADS breast density score and -0.81 for the proportion of glandular tissue in the breast interior. CONCLUSIONS: This study shows quantitatively how PWMF detection performance of a known lesion size is influenced by section thickness in dedicated breast CT. While the optimal section thickness is tuned to the size of the lesion being detected, overall performance is more robust for thin section images compared to thicker images.
Authors: M J Yaffe; J M Boone; N Packard; O Alonzo-Proulx; S Y Huang; C L Peressotti; A Al-Mayah; K Brock Journal: Med Phys Date: 2009-12 Impact factor: 4.071
Authors: John M Boone; Alex L C Kwan; Kai Yang; George W Burkett; Karen K Lindfors; Thomas R Nelson Journal: J Mammary Gland Biol Neoplasia Date: 2006-04 Impact factor: 2.673
Authors: L Chen; J M Boone; C K Abbey; J Hargreaves; C Bateni; K K Lindfors; K Yang; A Nosratieh; A Hernandez; P Gazi Journal: Phys Med Biol Date: 2015-03-31 Impact factor: 3.609
Authors: Craig K Abbey; Frank W Samuelson; Rongping Zeng; John M Boone; Miguel P Eckstein; Kyle Myers Journal: Med Phys Date: 2018-04-11 Impact factor: 4.071