Nonparametric signal detectability evaluation using an exponential transformation of the FROC curve

Popescu, Lucreţiu M.

doi:10.1118/1.3633938

Cited by 25 publications

(34 citation statements)

References 35 publications

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“…The area under the EFROC curve has an intuitive interpretation: the probability that the most suspicious location in the given search volume (or area) is the signal, instead of a false signal-like noise clump randomly occurring in the background. As discussed in [10], it can be related with other ways of carrying out the measurements or analyzing the data. Even when the conditions for the equivalence between EFROC and AFROC are not met, the analysis of the free-response results by means of exponential transformation can prove to be a convenient and powerful data analysis tool for signal detectability evaluation.…”

Section: B Free-response Data Analysismentioning

confidence: 99%

“…We can express the distribution of the scattered events as function of position and energy as (10) and the distribution of the randoms as (11) The trues distribution with position and energies as function of image elements is (12) where represents the image elements values, and is the probability density of an emission from image element to be detected as a coincidence event at the point .…”

Section: Image Reconstruction With Energy Dependent Correctionsmentioning

confidence: 99%

“…This problem can be addressed by applying a monotonic transformation of the abscissa that maps the interval into a finite interval. In [10], we have studied the case of using an exponential transformation function of the form , where is the average number of false marks per image, the abscissa in the FROC graph, as illustrated in Fig. 6.…”

Section: B Free-response Data Analysismentioning

confidence: 99%

“…We test these improvements by carrying out signal detection measurements using a recently developed image quality evaluation procedure that measures the detectability of signals at unknown locations. The procedure combines an automatic signal detection technique with a novel nonparametric free-response data analysis method [10]. Here, we present the first practical demonstration of this method.…”

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confidence: 99%

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PET Energy-Based Scatter Estimation in the Presence of Randoms, and Image Reconstruction With Energy-Dependent Scatter and Randoms Corrections

Popescu

2012

IEEE Trans. Nucl. Sci.

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In this paper, we address the problem of energy-based scatter estimation in PET in the presence of randoms. We refine a previous proposed model for comprehensive use of the energy information in PET [Phys. Med. Biol., vol. 51, pp. 2919-2937, 2006] by introducing a model for the random coincidences. This model is used to estimate the scatter components in randoms from delayed coincidence data, and then of the nonrandom coincidence scatters from the prompt coincidence data. By performing these estimations on a spatial grid covering the data space, we obtain scatter and randoms correction terms that depend both on position and energy. These terms are incorporated into the list-mode image reconstruction algorithm, thus allowing for an individual treatment of each event according to its position and energy information. We test this approach by using a recently developed image quality evaluation procedure that measures the detectability of signals at unknown locations. The procedure combines a three-dimensional image scanning technique with a novel nonparametric freeresponse data analysis method that provides a signal detectability metric by using an exponential transformation of the free-response operating characteristic (EFROC). The method allows for scaling of the detectability index to any given image size, a property that makes it advantageous for application to phantom experiments with multiple signals, or large background volumes that can be scanned for false signals. Such experiments can provide substantial signal detectability information with only a few images, as we show here. Image reconstructions using traditional, no energy-dependent, and energy-dependent corrections are compared for different time-of-flight (TOF) resolutions, and number of counts. A comparison of PET time-of-flight (TOF) performance versus no-TOF is also presented.

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Section: B Free-response Data Analysismentioning

confidence: 99%

Section: Image Reconstruction With Energy Dependent Correctionsmentioning

confidence: 99%

Section: B Free-response Data Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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PET Energy-Based Scatter Estimation in the Presence of Randoms, and Image Reconstruction With Energy-Dependent Scatter and Randoms Corrections

Popescu

2012

IEEE Trans. Nucl. Sci.

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“…In contrast, AUC is considered criterion-free because it does not depend on any one operating point. We also note that there are other approaches to defining imaging performance based on assessing localization accuracy [32, 33, 34] with a similar debate over FOMs [35, 36, 37] that this work will not address.…”

Section: Introductionmentioning

confidence: 99%

The Reproducibility of Changes in Diagnostic Figures of Merit Across Laboratory and Clinical Imaging Reader Studies

Samuelson

Abbey

2017

Academic Radiology

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Rationale and Objectives In this paper we examine which comparisons of reading performance between diagnostic imaging systems made in controlled retrospective laboratory studies may be representative of what we observe in later clinical studies. The change in a meaningful diagnostic figure of merit between two diagnostic modalities should be qualitatively or quantitatively comparable across all kinds of studies. Materials and Methods In this meta-study we examine the reproducibility of relative sensitivity, false positive fraction, area under the ROC curve, and expected utility across laboratory and observational clinical studies for several different breast imaging modalities, including screen film mammography, digital mammography, breast tomosynthesis, and ultrasound. Results Across studies of all types the changes in the false positive fractions yielded very small probabilities of having a common mean value. The probabilities of relative sensitivity being the same across ultrasound and tomosynthesis studies were low. No evidence was found for different mean values of relative area under the ROC curve or relative expected utility within any of the study sets. Conclusion The comparison demonstrates that the ratios of areas under the ROC curve and expected utilities are reproducible across laboratory and clinical studies, whereas sensitivity and false positive fraction are not.

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Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233

et al. 2019

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Background: The rapid development and complexity of new x-ray computed tomography (CT) technologies and the need for evidence-based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. Aims: This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. Materials and Methods: The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task-based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. Results: The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. Discussion: In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification-based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). Conclusion: We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance-based criteria for CT imaging.

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Nonparametric signal detectability evaluation using an exponential transformation of the FROC curve

Abstract: The new nonparametric estimator has sensitivity and scalability properties that make it particularly advantageous for signal detectability evaluation in phantom experiments using model observers.

Cited by 25 publications

References 35 publications

PET Energy-Based Scatter Estimation in the Presence of Randoms, and Image Reconstruction With Energy-Dependent Scatter and Randoms Corrections

PET Energy-Based Scatter Estimation in the Presence of Randoms, and Image Reconstruction With Energy-Dependent Scatter and Randoms Corrections

The Reproducibility of Changes in Diagnostic Figures of Merit Across Laboratory and Clinical Imaging Reader Studies

Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233

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