Using the Hotelling observer on multislice and multiview simulated SPECT myocardial images

Mu, Chunlai; Bowsher, J.E.; Baydush, Alan H.; Gilland, K.L.; DeLong, David M.; Jaszczak, R.J.

doi:10.1109/tns.2002.1039546

Cited by 31 publications

(25 citation statements)

References 16 publications

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“…Given that ms compared to ss image presentation provides access to more image information, this finding is expected and in line with the literature. [21][22][23][24][25] However, as suggested by several previous studies, 4,6,19 our experiments indicate that the level of human detection performance is influenced by image properties. In particular, when comparing ss to ms (while AUC ss ≈ 0.7), we find that the difference in performance increases as the task complexity decreases.…”

Section: Discussionsupporting

confidence: 72%

“…As remarked by Chen et al, 21 ms images provide additional information which allows for a better distinction between true signals and noise or background structures. When comparing 2D to 3D detection performance of the models for Gaussian-filtered noise background with Gaussian signal as a target, the results of Platisa et al 4 indicated that the difference was smaller when the correlation size in the background and the size of the signal were more similar (when the frequency content of the signal and the background were more similar).…”

Section: Discussionmentioning

confidence: 95%

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Volumetric detection tasks with varying complexity: human observer performance

Platiša

Kumcu

Platiša³

et al. 2012

SPIE Proceedings

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This study explores detection performance trends of human observers with respect to two parameters: task complexity determined by the frequency content of background and signal, and image viewing mode: singleslice (ss) versus multi-slice (ms) stack-browsing image presentation. The images are 3D correlated Gaussian noise with a 3D Gaussian signal centered in the image volume. We consider three different noise kernels while keeping the signal spread constant across all images. In ss mode, only the central slice of the volume is presented to the observer, while in ms mode all slices are available. All human readings are conducted in a controlled viewing environment on a 5MP digital mammography medical display. Overall, in line with the literature, we find that human performance increases in ms relative to ss image presentation mode. Furthermore, our experiments indicate that the extent of difference between ms and ss performance is influenced by the properties of image data: the difference in performance increases (from ΔAUC= 0.14 to ΔAUC= 0.30) as the difference in the frequency content of the signal and the background increases. Future studies shall focus on comparing the results of the present study to existing observer models for volumetric images, ultimately aiming at designing an anthropomorphic model observer for volumetric detection tasks.

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Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 95%

Volumetric detection tasks with varying complexity: human observer performance

Platiša

Kumcu

Platiša³

et al. 2012

SPIE Proceedings

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“…The Hotelling observer's signal-to-noise ratio (SNR Hot ) is a measure of the task performance and has been widely used to evaluate the imaging hardware for tumor detection task in imaging modalities such as PET [9,10], SPECT [11,12], digital mammography [13,14], and optical coherence tomography [15]. Evaluation of imaging system for target detection tasks using the Hotelling observer requires a large number of imaging data sets and, thus, is not easily performed using patient image or experimentally obtained phantom data.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a fluorescence-enhanced optical imaging system using the Hotelling observer

Sahu¹,

Joshi²,

Kupinski³

et al. 2006

Opt. Express

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This study represents a first attempt to assess the detection capability of a fluorescence-enhanced optical imaging system as quantified by the Hotelling observer. The imaging system is simulated by the diffusion approximation of the time-dependent radiative transfer equation, which describes near infra-red (NIR) light propagation through a breast phantom of clinically relevant volume. Random structures in the background are introduced using a lumpy-object model as a representation of anatomical structure as well as non-uniform distribution of disease markers. The systematic errors and noise associated with the actual experimental conditions are incorporated into the simulated boundary measurements to acquire imaging data sets. A large number of imaging data sets is considered in order to perform Hotelling observer studies. We find that the signal-to-noise ratio (SNR) of Hotelling observer (i) decreases as the strength of lumpy perturbations in the background increases, (ii) decreases as the target depth increases, and (iii) increases as excitation light leakage decreases, and reaches a maximum for filter optical density values of 5 or higher.

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“…Our observer design was motivated by Platisa et al and Chen et al, who built and validated a sophisticated two-layered numerical observer. 37,38 In addition, our recent studies demonstrated that the use of additional signal information in numerical observers could lead to more accurate image assessment. [39][40][41][42] These studies encouraged us to apply the proposed numerical observer to evaluate the discrimination performance using spectral CT images incorporating the extra spectral information.…”

Section: Introductionmentioning

confidence: 99%

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

Lorsakul

Fakhri

Worstell³

et al. 2016

J. Med. Imag

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Abstract. Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dualenergy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p < 0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p < 0.01) for FBP-Ramp images and 53.2% to 67.7% (all p < 0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energ...

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Using the Hotelling observer on multislice and multiview simulated SPECT myocardial images

Cited by 31 publications

References 16 publications

Volumetric detection tasks with varying complexity: human observer performance

Volumetric detection tasks with varying complexity: human observer performance

Assessment of a fluorescence-enhanced optical imaging system using the Hotelling observer

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

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