Low Contrast Sensitivity of Radiologic, CT, Nuclear Medicine, and Ultrasound Medical Imaging Systems

Wagner, Robert F.

doi:10.1109/tmi.1983.4307624

Cited by 25 publications

(11 citation statements)

References 45 publications

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“…The image that produces the larger decision variable is chosen by the observer as the image containing the signal. For a linear observer, a decision variable, , is a linear function of the image defined by (2) where is a stochastic internal noise component [29], [30]. The internal noise component is often assumed to be a zero-mean Gaussian random variable, independent of .…”

Section: B Linear Internal Response Variablesmentioning

confidence: 99%

“…The superscript in (2) indicates the transpose operation and, hence, is the scalar product of and . The vector is the observer template that determines how the image influences the decision variable.…”

Section: B Linear Internal Response Variablesmentioning

confidence: 99%

“…In this case, the sequence of events-often referred to as the imaging chain-includes image formation, acquisition, processing, and display. While all the elements of the imaging chain must be working harmoniously to ensure the best image, there is a convenient conceptual division between the image acquisition stage and the image processing stage [2]. The objective of image formation and acquisition is to pack as much relevant diagnostic information into the raw image data as possible while the objective of image processing and display is to make the relevant information in the data available to the observer.…”

Section: Introductionmentioning

confidence: 99%

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Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments

Abbey¹,

Eckstein

2002

IEEE Trans. Med. Imaging

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For performing simple detection and discrimination tasks in image noise, human observers are often modeled by a cross-correlation between the image and an observer template followed by the injection of the observer's internal noise. This paper is concerned with estimating this template using the two-alternative forced-choice (2AFC) experimental paradigm. The basic idea behind the estimation procedure is to average the noise fields of the images used in a 2AFC experiment with a weight that depends on whether the observer got the trial correct or incorrect. We describe a method that produces unbiased estimates of the observer template up to a constant of proportionality under the linear cross-correlation model. The method proposed here is different from some previous methods in the way it assigns weights to the noise fields and we show that the resulting errors in the estimated template are minimized. We also propose and validate a formula for approximating the error covariance associated with the template estimates.

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Section: B Linear Internal Response Variablesmentioning

confidence: 99%

Section: B Linear Internal Response Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments

Abbey¹,

Eckstein

2002

IEEE Trans. Med. Imaging

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“…Inverse geometry CT calls for a small detector, and sufficient transmission ray sampling is provided by sweeping the x-ray source [44]. Advanced forms of beam modulation or beam blocking are being considered for different sampling schemes which may provide information for accurate image reconstruction with reduced x-ray exposure [45-48]. Such methods may also be useful for estimating and compensating for x-ray scatter.…”

Section: Mri and Ct Backgroundmentioning

confidence: 99%

Compressive sensing in medical imaging

2015

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The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.

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“…Wagner [1] approached the assessment of data quality by first partitioning the image formation process into acquisition and display stages (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Objective Assessment of Sonographic Quality I: Task Information

Nguyen

Abbey

Insana

2013

IEEE Trans. Med. Imaging

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In this paper, we explore relationships between the performance of the ideal observer and information-based measures of class separability in the context of sonographic breast-lesion diagnosis. This investigation was motivated by a finding that, since the test statistic of the ideal observer in sonography is a quadratic function of the echo data, it is not generally normally distributed. We found for some types of boundary discrimination tasks often required for sonographic lesion diagnosis, the deviation of the test statistic from a normal distribution can be significant. Hence the usual relationships between performance and information metrics become uncertain. Using Monte Carlo studies involving five common sonographic lesion-discrimination tasks, we found in each case that the detectability index d(A)(2) from receiver operating characteristic analysis was well approximated by the Kullback-Leibler divergence J, a measure of clinical task information available from the recorded radio-frequency echo data. However, the lesion signal-to-noise ratio, SNR(I)(2), calculated from moments of the ideal observer test statistic, consistently underestimates d(A)(2) for high-contrast boundary discrimination tasks. Thus, in a companion paper, we established a relationship between image-quality properties of the imaging system and J in order to predict ideal performance. These relationships provide a rigorous basis for sonographic instrument evaluation and design.

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Low Contrast Sensitivity of Radiologic, CT, Nuclear Medicine, and Ultrasound Medical Imaging Systems

Cited by 25 publications

References 45 publications

Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments

Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments

Compressive sensing in medical imaging

Objective Assessment of Sonographic Quality I: Task Information

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