Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers

Tseng, Hsin Wu; Fan, Jiahua; Kupinski, Matthew A.; Sainath, Paavana; Hsieh, Jiang

doi:10.1118/1.4881143

Cited by 31 publications

(20 citation statements)

References 38 publications

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“…Second, the findings are relevant to the development of model observers. Many groups are currently working the development of model observers for 3D image modalities [26] including digital breast tomosynthesis [27]–[29] and computed tomography [30], [31]. Our results suggest that model observers for 3D images require taking into account visual processing in the periphery.…”

Section: Discussionmentioning

confidence: 88%

The role of extra-foveal processing in 3D imaging

2017

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The field of medical image quality has relied on the assumption that metrics of image quality for simple visual detection tasks are a reliable proxy for the more clinically realistic visual search tasks. Rank order of signal detectability across conditions often generalizes from detection to search tasks. Here, we argue that search in 3D images represents a paradigm shift in medical imaging: radiologists typically cannot exhaustively scrutinize all regions of interest with the high acuity fovea requiring detection of signals with extra-foveal areas (visual periphery) of the human retina. We hypothesize that extra-foveal processing can alter the detectability of certain types of signals in medical images with important implications for search in 3D medical images. We compare visual search of two different types of signals in 2D vs. 3D images. We show that a small microcalcification-like signal is more highly detectable than a larger mass-like signal in 2D search, but its detectability largely decreases (relative to the larger signal) in the 3D search task. Utilizing measurements of observer detectability as a function retinal eccentricity and observer eye fixations we can predict the pattern of results in the 2D and 3D search studies. Our findings: 1) suggest that observer performance findings with 2D search might not always generalize to 3D search; 2) motivate the development of a new family of model observers that take into account the inhomogeneous visual processing across the retina (foveated model observers).

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Section: Discussionmentioning

confidence: 88%

The role of extra-foveal processing in 3D imaging

2017

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“…Sub-image dimensions were chosen to be approximately five times the size of the largest feature in a manner similar to that in Ref. 24 where a uniform phantom had various sizes and contrast CT features with the intent to compare multiple image reconstruction methods. Since PET is known to have spatially varying noise and resolution, our design was to have as many sub-images as possible to cover a wide range of locations in the PET field-of-view.…”

Section: C Head-size Detectability Phantom Design and Model Observmentioning

confidence: 99%

A phantom design for assessment of detectability in PET imaging

Wollenweber¹,

Alessio

Kinahan

2016

Medical Physics

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“…Compared with other image quality FOMs such as the modulation transfer function (MTF), the observer model-derived d 0 has the advantage of being taskdependent in addition to taking both spatial resolution and noise into account. Therefore, observer models have been widely used in the development and assessment of many imaging modalities, including mammography, 10-12 dualenergy digital radiography, 13,14 digital tomosynthesis, [15][16][17][18][19][20] CT, [21][22][23][24][25][26][27][28][29][30][31][32][33][34] cone beam CT 17,18,31,[35][36][37][38] and differential-phase contrast CT. 39,40 A variety of observer models have previously been developed, and they differ from each other in the number and nature of hypotheses about the human visual perception that are integrated into the model to obtain a quantitative response variable. Although these observer models have been well validated for low-contrast imaging tasks, a direct application of these observer models to high-contrast and high-spatial resolution discrimination imaging tasks may lead to poor correlation with human observer performance.…”

Section: Introductionmentioning

confidence: 99%

Modified ideal observer model (MIOM) for high‐contrast and high‐spatial resolution CT imaging tasks

et al. 2017

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Purpose Although a variety of mathematical observer models have been developed to predict human observer performance for low contrast lesion detection tasks, their predictive power for high contrast and high spatial resolution discrimination imaging tasks, including those in CT bone imaging, could be limited. The purpose of this work was to develop a modified observer model that has improved correlation with human observer performance for these tasks. Methods The proposed observer model, referred to as the modified ideal observer model (MIOM), uses a weight function to penalize components in the task function that have less contribution to the actual human observer performance for high contrast and high spatial resolution discrimination tasks. To validate MIOM, both human observer and observer model studies were performed, each using exactly the same CT imaging task [discrimination of a connected component in a high contrast (1000 HU) high spatial resolution bone fracture model (0.3 mm)] and experimental CT image data. For the human observer studies, three physicist observers rated the connectivity of the fracture model using a five-point Likert scale; for the observer model studies, a total of five observer models, including both conventional models and the proposed MIOM, were used to calculate the discrimination capability of the CT images in resolving the connected component. Images used in the studies encompassed 9 different reconstruction kernels. Correlation between human and observer model performance for these kernels were quantified using the Spearman rank correlation coefficient (ρ). After the validation study, an example application of MIOM was presented, in which the observer model was used to select the optimal reconstruction kernel for a High-Resolution (Hi-Res, GE Healthcare) CT scan technique. Results The performance of the proposed MIOM correlated well with that of the human observers with a Spearman rank correlation coefficient ρ of 0.88 (p = 0.003). In comparison, the value of ρ was 0.05 (p = 0.904) for the ideal observer, 0.05 (p = 0.904) for the non-prewhitening observer, −0.18 (p = 0.634) for the non-prewhitening observer with eye filter and internal noise, and 0.30 (p = 0.427) for the prewhitening observer with eye filter and internal noise. Using the validated MIOM, the optimal reconstruction kernel for the Hi-Res mode to perform high spatial resolution and high contrast discrimination imaging tasks was determined to be the HD Ultra kernel at the center of the scan field of view (SFOV), or the Lung kernel at the peripheral region of the SFOV. This result was consistent with visual observations of nasal CT images of an in vivo canine subject. Conclusions Compared with other observer models, the proposed modified ideal observer model provides significantly improved correlation with human observers for high contrast and high spatial resolution CT imaging tasks.

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Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers

Cited by 31 publications

References 38 publications

The role of extra-foveal processing in 3D imaging

The role of extra-foveal processing in 3D imaging

A phantom design for assessment of detectability in PET imaging

Modified ideal observer model (MIOM) for high‐contrast and high‐spatial resolution CT imaging tasks

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