Optimization of iterative reconstructions of /sup 99m/Tc cardiac SPECT studies using numerical observers

Narayanan, Manoj; Gifford, Howard C.; King, Michael A.; Pretorius, P. Hendrik; Farncombe, Troy; Bruyant, P.P.; Wernick, Miles N.

doi:10.1109/tns.2002.803805

Cited by 51 publications

(28 citation statements)

References 18 publications

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“…Since we are working in a SKE environment, the four feature extracting channels are applied on a windowed area centered at the known location of simulated perfusion defect in the MCAT phantom. 12 Note that the window used in this work had a size of 51 51 × pixels. …”

Section: Feature Extractionmentioning

confidence: 99%

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

et al. 2011

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In this paper, we present a numerical observer for image quality assessment, aiming to predict human observer accuracy in a cardiac perfusion defect detection task for single-photon emission computed tomography (SPECT). In medical imaging, image quality should be assessed by evaluating the human observer accuracy for a specific diagnostic task. This approach is known as task-based assessment. Such evaluations are important for optimizing and testing imaging devices and algorithms. Unfortunately, human observer studies with expert readers are costly and time-demanding. To address this problem, numerical observers have been developed as a surrogate for human readers to predict human diagnostic performance. The channelized Hotelling observer (CHO) with internal noise model has been found to predict human performance well in some situations, but does not always generalize well to unseen data. We have argued in the past that finding a model to predict human observers could be viewed as a machine learning problem. Following this approach, in this paper we propose a channelized relevance vector machine (CRVM) to predict human diagnostic scores in a detection task. We have previously used channelized support vector machines (CSVM) to predict human scores and have shown that this approach offers better and more robust predictions than the classical CHO method. The comparison of the proposed CRVM with our previously introduced CSVM method suggests that CRVM can achieve similar generalization accuracy, while dramatically reducing model complexity and computation time.

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Section: Feature Extractionmentioning

confidence: 99%

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

et al. 2011

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“…26 A total of 60 noise realizations of reconstructed images were used: 30 with the defect present and 30 with the defect absent; the statistics were then estimated for the CHO to obtain the area under the ROC curve as in Ref. 24. These ROC studies were "signalknown exactly" and "background-known exactly" observer studies.…”

Section: Iiib Chomentioning

confidence: 99%

Detectability of perfusion defect in five‐dimensional gated‐dynamic cardiac SPECT images

et al. 2010

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Purpose: In previous work, the authors developed and demonstrated the concept of an image reconstruction procedure aimed to unify gated and dynamic nuclear cardiac imaging, which the authors have termed five-dimensional (5D) SPECT. Gated imaging permits the clinician to evaluate wall motion and, through the use of stress and rest scans, allows perfusion defects to be observed. Dynamic imaging depicts kinetics in the myocardium, which can be used to evaluate perfusion, but traditional dynamic images are motionless and do not depict wall motion. In this article, the authors investigate the degree to which perfusion defects can be detected from the dynamic information conveyed by 5D images, a problem that is particularly challenging in the absence of multiple fast camera rotations. Methods: The authors first demonstrate the usefulness of dynamic reconstructed images for perfusion detection by using linear discriminant analyses ͑Fisher linear discriminant analysis and principal component analysis͒ and a numerical channelized Hotelling observer. The authors then derive three types of discriminant metrics for characterizing the temporal kinetic information in reconstructed dynamic images for differentiating perfusion defects from normal cardiac perfusion, which are the Fisher linear discriminant map, temporal derivative map, and kinetic parametric images. Results: Results are based on the NURBS-based cardiac-torso phantom with simulation of Tc99m-teboroxime as the imaging agent. The derived metric maps and quantitative contrast-to-noise ratio results demonstrate that the reconstructed dynamic images could yield higher detectability of the perfusion defect than conventional gated reconstruction while providing wall motion information simultaneously. Conclusions:The proposed metrics can be used to produce new types of visualizations, showing wall motion and perfusion information, that may potentially be useful for clinical evaluation. Since 5D imaging permits wall motion and kinetics to be observed simultaneously, it may ultimately obviate the need for separate stress and rest scans.

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“…These internal noise models have been added to the CHO in medical imaging to find detection thresholds in x-ray mammography, 45 optimize JPEG compression settings in coronary angiograms, 46,47 and optimize cardiac SPECT reconstruction parameters. 48,49 There are a number of considerations regarding the CHO and internal noise modeling. In x-ray angiograms, the choice of internal noise approach depends upon the CHO filters used.…”

Section: Introductionmentioning

confidence: 99%

Computational and human observer image quality evaluation of low dose, knowledge‐based CT iterative reconstruction

et al. 2015

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Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d ′ ) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre-Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P C . Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC c . With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of im...

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Optimization of iterative reconstructions of /sup 99m/Tc cardiac SPECT studies using numerical observers

Cited by 51 publications

References 18 publications

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

Detectability of perfusion defect in five‐dimensional gated‐dynamic cardiac SPECT images

Computational and human observer image quality evaluation of low dose, knowledge‐based CT iterative reconstruction

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