Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers.

PURPOSE A number of different techniques have been developed to reduce radiation dose in x-ray computed tomography (CT) imaging. In this paper, the authors will compare task-based measures of image quality of CT images reconstructed by two algorithms: conventional filtered back projection (FBP), and a new iterative reconstruction algorithm (IR). METHODS To assess image quality, the authors used the performance of a channelized Hotelling observer acting on reconstructed image slices. The selected channels are dense difference Gaussian channels (DDOG).A body phantom and a head phantom were imaged 50 times at different dose levels to obtain the data needed to assess image quality. The phantoms consisted of uniform backgrounds with low contrast signals embedded at various locations. The tasks the observer model performed included (1) detection of a signal of known location and shape, and (2) detection and localization of a signal of known shape. The employed DDOG channels are based on the response of the human visual system. Performance was assessed using the areas under ROC curves and areas under localization ROC curves. RESULTS For signal known exactly (SKE) and location unknown/signal shape known tasks with circular signals of different sizes and contrasts, the authors' task-based measures showed that a FBP equivalent image quality can be achieved at lower dose levels using the IR algorithm. For the SKE case, the range of dose reduction is 50%-67% (head phantom) and 68%-82% (body phantom). For the study of location unknown/signal shape known, the dose reduction range can be reached at 67%-75% for head phantom and 67%-77% for body phantom case. These results suggest that the IR images at lower dose settings can reach the same image quality when compared to full dose conventional FBP images. CONCLUSIONS The work presented provides an objective way to quantitatively assess the image quality of a newly introduced CT IR algorithm. The performance of the model observers using the IR images was always higher than that seen using the FBP images in the authors' SKE and SKE location unknown detection tasks. To achieve a FBP-equivalent image quality in CT systems, the authors can lower the radiation dose by using this IR image reconstruction algorithm. Further studies are warranted using clinical data and human observer to validate these results for more complicated and realistic tasks.

[1]  Michael A. King,et al.  LROC analysis of detector-response compensation in SPECT , 2000, IEEE Transactions on Medical Imaging.

[2]  Brandon D Gallas,et al.  One-shot estimate of MRMC variance: AUC. , 2006, Academic radiology.

[3]  C E Metz,et al.  Variance-component modeling in the analysis of receiver operating characteristic index estimates. , 1997, Academic radiology.

[4]  E. Krupinski,et al.  Visual scanning patterns of radiologists searching mammograms. , 1996, Academic radiology.

[5]  Zhou Yu,et al.  Recent Advances in CT Image Reconstruction , 2013, Current Radiology Reports.

[6]  Craig K. Abbey,et al.  Human-observer templates for detection of a simulated lesion in mammographic images , 2002, SPIE Medical Imaging.

[7]  Matthew A. Kupinski,et al.  Probabilistic foundations of the MRMC method , 2005, SPIE Medical Imaging.

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

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

[10]  M. Kalra,et al.  Strategies for CT radiation dose optimization. , 2004, Radiology.

[11]  Keinosuke Fukunaga,et al.  Effects of Sample Size in Classifier Design , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Matt A. King,et al.  Channelized hotelling and human observer correlation for lesion detection in hepatic SPECT imaging. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[13]  C A Roe,et al.  Dorfman-Berbaum-Metz method for statistical analysis of multireader, multimodality receiver operating characteristic data: validation with computer simulation. , 1997, Academic radiology.

[14]  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.

[15]  Howard C. Gifford,et al.  Penalized Maximum Likelihood Reconstruction for Improved Microcalcification Detection in Breast Tomosynthesis , 2011, IEEE Transactions on Medical Imaging.

[16]  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.

[17]  Matthew A. Kupinski,et al.  Adaptive SPECT , 2008, IEEE Transactions on Medical Imaging.

[18]  T. Yoshizumi,et al.  Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources--1950-2007. , 2009, Radiology.

[19]  Thomas L Toth,et al.  Low-dose CT of the abdomen: evaluation of image improvement with use of noise reduction filters pilot study. , 2003, Radiology.

[20]  N A Obuchowski,et al.  Multireader receiver operating characteristic studies: a comparison of study designs. , 1995, Academic radiology.

[21]  R. F. Wagner,et al.  Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis. , 2000, Academic radiology.

[22]  L. Tanoue Computed Tomography — An Increasing Source of Radiation Exposure , 2009 .

[23]  W A Kalender,et al.  Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements. , 1999, Medical physics.

[24]  Dan J Kadrmas,et al.  Comparative evaluation of lesion detectability for 6 PET imaging platforms using a highly reproducible whole-body phantom with (22)Na lesions and localization ROC analysis. , 2002, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[25]  K S Berbaum,et al.  Monte Carlo validation of a multireader method for receiver operating characteristic discrete rating data: factorial experimental design. , 1998, Academic radiology.

[26]  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.

[27]  N A Obuchowski,et al.  Multireader, multimodality receiver operating characteristic curve studies: hypothesis testing and sample size estimation using an analysis of variance approach with dependent observations. , 1995, Academic radiology.

[28]  Berkman Sahiner,et al.  Components of variance in ROC analysis of CADx classifier performance , 1998, Medical Imaging.

[29]  C. McCollough,et al.  CT dose reduction and dose management tools: overview of available options. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[30]  H. Barrett,et al.  Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  Gene Gindi,et al.  Decision strategies that maximize the area under the LROC curve , 2005, IEEE Transactions on Medical Imaging.

[32]  Thomas Flohr,et al.  Multislice helical CT of the heart with retrospective ECG gating: reduction of radiation exposure by ECG-controlled tube current modulation , 2002, European Radiology.

[33]  Craig K. Abbey,et al.  Stabilized estimates of Hotelling-observer detection performance in patient-structured noise , 1998, Medical Imaging.

[34]  Matthew A. Kupinski,et al.  Optimizing a multiple-pinhole SPECT system using the ideal observer , 2003, SPIE Medical Imaging.

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

[36]  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.

[37]  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.

[38]  Berkman Sahiner,et al.  Finite-sample effects and resampling plans: applications to linear classifiers in computer-aided diagnosis , 1997, Medical Imaging.