Effect of noise correlation on detectability of disk signals in medical imaging

Myers, Kyle J.; Barrett, Harrison H.; Borgstrom, Mark; Patton, Dennis D.; Seeley, George W.

doi:10.1364/josaa.2.001752

Cited by 199 publications

(165 citation statements)

References 13 publications

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“…It is of particular interest, therefore, that, although noise reductions have been shown here by the hybrid algorithm consistent with previous studies [15,[27][28][29][30][31], this noise benefit does not necessarily translate to an improvement in low-contrast object detection, even though conservation of noise textures [26,28], enhanced lesion conspicuity [27,[32][33][34] and improved image quality [15,24,[34][35][36] have all been reported with hybrid FBP/iterative methods compared with standard FBP reconstruction. This may be explained in part by the fact that iDose 4 uses FBP for its initial estimate, and, consequently, the B and iDose 4 algorithms will produce similar covariance properties resulting in similar performance for low-contrast lesion detection [37][38][39]. Our results support previous studies indicating that the covariance properties of images are at least as important for lesion detection tasks as the variance (noise) properties [40].…”

Section: Discussionsupporting

confidence: 85%

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT

Dobeli¹,

Lewis²,

Meikle³

et al. 2013

The British Journal of Radiology

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Cite this article as: Dobeli KL, Lewis SJ, Meikle SR, Thiele DL, Brennan PC. Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT. Br J Radiol 2013;86:20120500. Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT Objective:To compare the doseoptimisation potential of a smoothing filtered backprojection (FBP) and a hybrid FBP/iterative algorithm to that of a standard FBP algorithm at three slice thicknesses for hepatic lesion detection with multidetector CT.Methods: A liver phantom containing a 9.5-mm opacity with a density of 10 HU below background was scanned at 125, 100, 75, 50 and 25 mAs. Data were reconstructed with standard FBP (B), smoothing FBP (A) and hybrid FBP/iterative (iDose 4 ) algorithms at 5-, 3-and 1-mm collimation. 10 observers marked opacities using a four-point confidence scale. Jackknife alternative freeresponse receiver operating characteristic figure of merit (FOM), sensitivity and noise were calculated.Results: Compared with the 125-mAs/5-mm setting for each algorithm, significant reductions in FOM (p,0.05) and sensitivity (p,0.05) were found for all three algorithms for all exposures at 1-mm thickness and for all slice thicknesses at 25 mAs, with the exception of the 25-mAs/5-mm setting for the B algorithm. Sensitivity was also significantly reduced for all exposures at 3-mm thickness for the A algorithm (p,0.05). Noise for the A and iDose 4 algorithms was approximately 13% and 21% lower, respectively, than for the B algorithm.Conclusion: Superior performance for hepatic lesion detection was not shown with either a smoothing FBP algorithm or a hybrid FBP/iterative algorithm compared with a standard FBP technique, even though noise reduction with thinner slices was demonstrated with the alternative approaches.

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Section: Discussionsupporting

confidence: 85%

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT

Dobeli¹,

Lewis²,

Meikle³

et al. 2013

The British Journal of Radiology

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“…On the side of image data, the most simplified approach of task-based image quality assessment restricts the task of interest to detecting whether a known object (signal) is present at one specified location in a known background, the so-called binary signal-knownexactly and background-known-exactly (SKE/BKE) detection task [22,29,30]. More complicated and more clinically relevant, are the paradigms of background-known-statistically (BKS) [13,15,17,28,[31][32][33][34][35][36][37][38] and signal-known-statistically (SKS) [38][39][40] which incorporate background and signal variability, respectively.…”

Section: Osa Published Bymentioning

confidence: 99%

Channelized Hotelling observers for the assessment of volumetric imaging data sets

Goossens²,

et al. 2011

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OSA Published byCurrent clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For 3D images, however, these models have been little explored so far. In this work, first, two novel designs of a multi-slice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multi-slice models and a volumetric model. Four different random background statistics are considered, both Gaussian (non-correlated and correlated Gaussian noise) and non-Gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multi-slice models, the second proposed CHO could most closely approach the volumetric model whereas the first new CHO seems to be least affected by the number of training samples.

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“…[21][22][23][24] Efforts toward task-based assessment and optimization of statistical reconstruction have mostly been concentrated in emission tomography. [25][26][27][28][29][30][31][32][33][34][35][36][37] The work reported below investigates the nonstationary noise, resolution, and task-based performance in CBCT reconstructed with both FBP and statistical algorithms. For the latter, the current analysis pertains to penalized likelihood (PL) estimation with a quadratic penalty.…”

Section: Introductionmentioning

confidence: 99%

Task‐based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

et al. 2014

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Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according to Fessler ["Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography," IEEE Trans. Image Process. 5(3), 493-506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP is isotropic and independent of location to a first order approximation, whereas the MTF of PL is anisotropic in a manner complementary to the NPS. Task-based detectability demonstrates dependence on the task, object, spatial location, and smoothing parameters. A spatially varying regularization "map" designed from locally optimal regularization can improve overall detectability beyond that achievable with the commonly used constant regularization parameter. Conclusions: Analytical models for task-based FBP and PL reconstruction are predictive of nonstationary noise and resolution characteristics, providing a valuable framework for understanding and optimizing system performance in CT and CBCT.

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Effect of noise correlation on detectability of disk signals in medical imaging

Cited by 199 publications

References 13 publications

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT

Channelized Hotelling observers for the assessment of volumetric imaging data sets

Task‐based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

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