Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction

We investigate the detectability of breast cone beam computed tomography images using human and model observers and the variations of exponent, β, of the inverse power-law spectrum for various reconstruction filters and interpolation methods in the Feldkamp-Davis-Kress (FDK) reconstruction. Using computer simulation, a breast volume with a 50% volume glandular fraction and a 2mm diameter lesion are generated and projection data are acquired. In the FDK reconstruction, projection data are apodized using one of three reconstruction filters; Hanning, Shepp-Logan, or Ram-Lak, and back-projection is performed with and without Fourier interpolation. We conduct signal-known-exactly and background-known-statistically detection tasks. Detectability is evaluated by human observers and their performance is compared with anthropomorphic model observers (a non-prewhitening observer with eye filter (NPWE) and a channelized Hotelling observer with either Gabor channels or dense difference-of-Gaussian channels). Our results show that the NPWE observer with a peak frequency of 7cyc/degree attains the best correlation with human observers for the various reconstruction filters and interpolation methods. We also discover that breast images with smaller β do not yield higher detectability in the presence of quantum noise.

[1]  Xin He,et al.  Model Observers in Medical Imaging Research , 2013, Theranostics.

[2]  Sung Hun Kim,et al.  Differentiation of malignant and benign breast lesions: Added value of the qualitative analysis of breast lesions on diffusion-weighted imaging (DWI) using readout-segmented echo-planar imaging at 3.0 T , 2017, PloS one.

[3]  John M Boone,et al.  Technique factors and their relationship to radiation dose in pendant geometry breast CT. , 2005, Medical physics.

[4]  M. Glas,et al.  Principles of Computerized Tomographic Imaging , 2000 .

[5]  Zhaoying Bian,et al.  Iterative Image Reconstruction for Sparse-View CT Using Normal-Dose Image Induced Total Variation Prior , 2013, PloS one.

[6]  A. Jemal,et al.  Cancer statistics, 2012 , 2012, CA: a cancer journal for clinicians.

[7]  Ehsan Samei,et al.  Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance. , 2010, Medical physics.

[8]  Arthur E Burgess,et al.  Signal detection in power-law noise: effect of spectrum exponents. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  John M Boone,et al.  Characterizing anatomical variability in breast CT images. , 2008, Medical physics.

[10]  Biao Chen,et al.  Cone-beam volume CT breast imaging: feasibility study. , 2002, Medical physics.

[11]  A. Burgess,et al.  Human observer detection experiments with mammograms and power-law noise. , 2001, Medical physics.

[12]  Aruna A. Vedula,et al.  Microcalcification detection using cone-beam CT mammography with a flat-panel imager. , 2004, Physics in medicine and biology.

[13]  Jongduk Baek,et al.  Effect of anatomical noise on the detectability of cone beam CT images with different slice direction, slice thickness, and volume glandular fraction. , 2016, Optics express.

[14]  Srinivasan Vedantham,et al.  Investigation of optimal kVp settings for CT mammography using a flat-panel imager , 2002, SPIE Medical Imaging.

[15]  Miguel P Eckstein,et al.  Evaluation of internal noise methods for Hotelling observer models. , 2007, Medical physics.

[16]  Aruna A. Vedula,et al.  A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. , 2006, Medical physics.

[17]  A. Burgess.  Statistically defined backgrounds: performance of a modified nonprewhitening observer model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  B. Tsui,et al.  A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT , 1993 .

[19]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[20]  Jongduk Baek,et al.  Investigation on slice direction dependent detectability of volumetric cone beam CT images. , 2016, Optics express.

[21]  R M Nishikawa,et al.  On the orientation of mammographic structure. , 2011, Medical physics.

[22]  Anita Nosratieh,et al.  Anatomical complexity in breast parenchyma and its implications for optimal breast imaging strategies. , 2012, Medical physics.

[23]  Karla K. Evans,et al.  If You Don’t Find It Often, You Often Don’t Find It: Why Some Cancers Are Missed in Breast Cancer Screening , 2013, PloS one.

[24]  R. Siddon.  Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[25]  A. Mushlin,et al.  Estimating the accuracy of screening mammography: a meta-analysis. , 1998, American journal of preventive medicine.

[26]  J. Boone,et al.  Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities. , 2013, Physics in medicine and biology.

[27]  Ho Kyung Kim,et al.  Fourier Analysis of Noise Characteristics in Cone-Beam Microtomography Laboratory Scanners , 2017, IEEE Transactions on Biomedical Engineering.

[28]  P. C. Johns,et al.  X-ray characterisation of normal and neoplastic breast tissues. , 1987, Physics in medicine and biology.

[29]  Stephen J. Glick,et al.  Investigation of statistical iterative reconstruction for dedicated breast CT , 2012, Medical Imaging.

[30]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  Xiaochuan Pan,et al.  Investigation of iterative image reconstruction in low-dose breast CT , 2014, Physics in medicine and biology.

[32]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[33]  Kai Yang,et al.  Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions. , 2012, Medical physics.

[34]  Nico Karssemeijer,et al.  Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study , 2014, IEEE Transactions on Medical Imaging.

[35]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[36]  Daniel Kolditz,et al.  Iterative reconstruction methods in X-ray CT. , 2012, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[37]  R M Nishikawa,et al.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. , 2010, Medical physics.

[38]  Craig K. Abbey,et al.  An Ideal Observer for a Model of X-Ray Imaging in Breast Parenchymal Tissue , 2008, Digital Mammography / IWDM.

[39]  Craig K. Abbey,et al.  Bayesian Detection of Random Signals on Random Backgrounds , 1997, IPMI.

[40]  Adam Wunderlich,et al.  Image covariance and lesion detectability in direct fan-beam x-ray computed tomography , 2008, Physics in medicine and biology.

[41]  Jongduk Baek,et al.  The noise power spectrum in CT with direct fan beam reconstruction. , 2010, Medical physics.

[42]  M Ruschin,et al.  Visibility of microcalcification clusters and masses in breast tomosynthesis image volumes and digital mammography: a 4AFC human observer study. , 2012, Medical physics.

[43]  J. Ouyang,et al.  Myocardial Defect Detection Using PET-CT: Phantom Studies , 2014, PloS one.

[44]  Damien Racine,et al.  Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography , 2016, Journal of medical imaging.

[45]  Harrison H Barrett,et al.  Validating the use of channels to estimate the ideal linear observer. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[46]  S. Xie,et al.  Label-Free Detection of Breast Masses Using Multiphoton Microscopy , 2013, PloS one.

[47]  Lingyun Chen,et al.  Visibility of microcalcification in cone beam breast CT: effects of X-ray tube voltage and radiation dose. , 2007, Medical physics.