&lt;title&gt;Psychophysical Validation Of The Hotelling Trace Criterion As A Metric For System Performance&lt;/title&gt;

Fiete, Robert D.; Barrett, Harrison H.; Cargill, E. B.; Myers, Kyle J.; Smith, Warren E.; Dwyer, Samuel J.; Schneider, Roger H.

doi:10.1117/12.967010

Cited by 7 publications

(5 citation statements)

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“…Task-based assessment has a long history in the field of nuclear medicine, where it has been used to study the effects on image quality of various system parameters, such as the aperture opening, the collimator, and even the reconstruction algorithm [12,19,20,21].…”

Section: Task-based Assessmentmentioning

confidence: 99%

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Task-Based Optimization of Computed Tomography Imaging Systems

Sanchez

2015

Preprint

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The integrand of Equation 4.1 for the microcalcification detection task. Right: The reconstructed microcalcification signal energy as a function of radius from the signal, E y (r). In this case, the resulting ROI would have dimension 14×14 pixels. The results shown here correspond to 50 projection views . . . . x 4.3 Efficiency values are shown for various views and Hanning window widths (relative to the Nyquist frequency on the detector) for both the Rayleigh task (left) and the detection task (right). The Nyquist frequency in this case is ν N = 1 2∆u ≈ 1.3mm −1 . While the efficiency values for moderate filtering to no filtering were seen to have a dependence on ROI size, the same trend pictured here was seen for ROI sizes up to roughly 1.5cm in diameter. . . . . . . . . . . . . . . . . . . 4.4 Dependency of CHO efficiency on number of channels for a range of reconstruction filter widths. For most filter widths, the CHO efficiency stabilizes with 50 channels, however the performance estimates for this task and system are not sensitive to the number of channels used, in general. Subsequent results shown are for 50 channels.

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Section: Task-based Assessmentmentioning

confidence: 99%

“…The Hotelling Observer (HO) is the optimal linear model observer in the limit of Gaussian noise, and it has been used for prediction of human observer task performance [22,20].…”

Section: Mathematics Of the Hotelling Observermentioning

confidence: 99%

Task-Based Optimization of Computed Tomography Imaging Systems

Sanchez

2015

Preprint

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“…The HO is the optimal linear model observer in the limit of Gaussian noise, and it has been used for prediction of human observer task performance. 42,43 Even in cases where the HO does not accurately predict human performance, it often still constitutes a useful upper bound on human task performance. 3,4,44 In a classification task, the HO operates by computing a scalar value which is a linear combination of the image pixel values.…”

Section: F Hotelling Observer Metricsmentioning

confidence: 99%

Task‐based optimization of dedicated breast CT via Hotelling observer metrics

2014

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Purpose: The purpose of this work is to develop and demonstrate a set of practical metrics for CT systems optimization. These metrics, based on the Hotelling observer (HO) figure of merit, are task-based. The authors therefore take the specific example of optimizing a dedicated breast CT system, including the reconstruction algorithm, for two relevant tasks, signal detection and Rayleigh discrimination. Methods: A dedicated breast CT system is simulated using specifications in the literature from an existing prototype. The authors optimize configuration and image reconstruction algorithm parameters for two tasks: the detection of simulated microcalcifications and the discrimination of two adjacent, high-contrast signals, known as the Rayleigh discrimination task. The effects on task performance of breast diameter, signal location, image grid size, projection view number, and reconstruction filter were all investigated. Two HO metrics were evaluated: the percentage of correct decisions in a two-alternative forced choice experiment (equivalent to area under the ROC curve or AUC), and the HO efficiency, defined as the squared ratio of HO signal-to-noise ratio (SNR) in the reconstructed image to HO SNR in the projection data. Results: The ease and efficiency of the HO metric computation allows a rapid high-resolution survey of many system parameters. Optimization of a range of system parameters using the HO results in images that subjectively appear optimal for the tasks investigated. Further, the results of assessment through the HO reproduce closely many existing results in the literature regarding the impact of parameter selection on image quality. Conclusions: This study demonstrates the utility of a task-based approach to system design, evaluation, and optimization. The methodology presented is equally applicable to determining the impact of a wide range of factors, including patient parameters, system and acquisition design, and the reconstruction algorithm. The results demonstrate the versatility of the proposed HO formalism by not only generating a set of parameters that are optimal for a given task but also by qualitatively reproducing many existing results from the breast CT literature. Meanwhile, the implementation of the proposed methodology is straightforward and entirely simulation-based. This is an attractive feature for many system optimization problems, where the goal is to analyze the individual system components such as the image reconstruction algorithm. Final assessment of the system as a whole should be based also on real data studies. C

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“…This type of analysis has been used extensively to examine the effects of computer processing on the diagnostic utility of medical images. Types of processing that have been evaluated include compression [22,21,65,30,6,9,35,56,14], and enhancement (unsharp masking, histogram equalization, and noise reduction).…”

Section: ¡ ¦mentioning

confidence: 99%