Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images

Abstract. Task-based image quality procedures in CT that substitute a human observer with a model observer usually use single-slice images with uniform backgrounds from homogeneous phantoms. However, anatomical structures and inhomogeneities in organs generate noise that can affect the detection performance of human observers. The purpose of this work was to assess the impact of background type, uniform or liver, and the viewing modality, single- or multislice, on the detection performance of human and model observers. We collected abdominal CT scans from patients and homogeneous phantom scans in which we digitally inserted low-contrast signals that mimicked a liver lesion. We ran a rating experiment with the two background conditions with three signal sizes and three human observers presenting images in two reading modalities: single- and multislice. In addition, channelized Hotelling observers (CHO) for single- and multislice detection were implemented and evaluated according to their degree of correlation with the human observer performance. For human observers, there was a small but significant improvement in performance with multislice compared to the single-slice viewing mode. Our data did not reveal a significant difference between uniform and anatomical backgrounds. Model observers demonstrated a good correlation with human observers for both viewing modalities. Human observers have very similar performances in both multi- and single-slice viewing mode. It is therefore preferable to use single-slice CHO as this model is computationally more tractable than multislice CHO. However, using images from a homogeneous phantom can result in overestimating image quality as CHO performance tends to be higher in uniform than anatomical backgrounds, while human observers have similar detection performances.

[1]  Hoen-oh Shin,et al.  Insertion of virtual pulmonary nodules in CT data of the chest: development of a software tool , 2006, European Radiology.

[2]  Shuai Leng,et al.  Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. , 2013, Medical physics.

[3]  Damien Racine,et al.  Objective assessment of low contrast detectability in computed tomography with Channelized Hotelling Observer. , 2016, 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.

[4]  W J H Veldkamp,et al.  Low contrast detectability performance of model observers based on CT phantom images: kVp influence. , 2015, 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.

[5]  H.H. Barrett,et al.  Model observers for assessment of image quality , 1993, 2002 IEEE Nuclear Science Symposium Conference Record.

[6]  Adam Wunderlich,et al.  Exact Confidence Intervals for Channelized Hotelling Observer Performance in Image Quality Studies , 2015, IEEE Transactions on Medical Imaging.

[7]  Jovan G Brankov,et al.  Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection , 2013, Physics in medicine and biology.

[8]  J. Swets,et al.  Assessment of diagnostic technologies. , 1979, Science.

[9]  F R Verdun,et al.  Estimation of the noisy component of anatomical backgrounds. , 1999, Medical physics.

[10]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[11]  N. Obuchowski,et al.  Hypothesis testing of diagnostic accuracy for multiple readers and multiple tests: An anova approach with dependent observations , 1995 .

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

[13]  Ewout Vansteenkiste,et al.  Channelized Hotelling observers for the assessment of volumetric imaging data sets. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  Craig K. Abbey,et al.  Mass detection in breast tomosynthesis and digital mammography: a model observer study , 2009, Medical Imaging.

[15]  Shuai Leng,et al.  Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain , 2012, Medical Imaging.

[16]  Matthew A Kupinski,et al.  Correlation between a 2D channelized Hotelling observer and human observers in a low‐contrast detection task with multislice reading in CT , 2017, Medical physics.

[17]  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.

[18]  Ronald J. Jaszczak,et al.  Using the Hotelling observer on multi-slice and multi-view simulated SPECT myocardial images , 2001 .

[19]  Matthew A Kupinski,et al.  Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers. , 2014, Medical physics.

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

[21]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[22]  S. Hillis A comparison of denominator degrees of freedom methods for multiple observer ROC analysis , 2007, Statistics in medicine.

[23]  Jean-Baptiste Thibault,et al.  Evaluation of low contrast detectability performance using two-alternative forced choice method on computed tomography dose reduction algorithms , 2012, Medical Imaging.

[24]  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.

[25]  K. Berbaum,et al.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. , 1992, Investigative radiology.

[26]  J. Solomon,et al.  Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms. , 2016, Medical physics.

[27]  W J H Veldkamp,et al.  Automated assessment of low contrast sensitivity for CT systems using a model observer. , 2011, Medical physics.

[28]  Nancy A Obuchowski,et al.  A comparison of the Dorfman–Berbaum–Metz and Obuchowski–Rockette methods for receiver operating characteristic (ROC) data , 2005, Statistics in medicine.

[29]  Ehsan Samei,et al.  A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging , 2014, Physics in medicine and biology.

[30]  Stephen L Hillis,et al.  Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. , 2008, Academic radiology.