Comparison of power spectra for tomosynthesis projections and reconstructed images

Engström, Emma; Reiser, Ingrid; Nishikawa, Robert M.

doi:10.1118/1.3116774

Cited by 61 publications

(65 citation statements)

References 10 publications

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“…The exponent b ¼ 3 was calculated based on fractal properties of self-similar objects (such as equal volumes of different sized spheres in the generic phantom above) and is similar to widely reported values. [25][26][27]43 The numerator j carries a dependence on kVp because of the effective attenuation coefficients of object and background, and decreases with scatter as (1=1 þ SPR) 2 . j and hence, anatomical clutter, decrease as a function of increasing kVp (reduced relative contrast).…”

Section: Quantum Noisementioning

confidence: 99%

“…The effect of anatomical background on detectability is a subject of considerable interest, commonly modeling the background as power-law noise j=f b included as an additive noise term in the "generalized" NEQ. For example, in breast imaging, the power-law characteristic in 2D mammographic images was characterized by Bochud et al, 21 Burgess, 22,23 and others 24 and extended to 3D breast imaging by Metheany et al, 25 Engstrom et al, 26 Glick et al, 27 Gong et al, 28 and Reiser and Nishikawa, 29 showing b differs in 2D projections versus 3D tomosynthesis and CBCT reconstructions. In a fairly general context, Gang et al 30 showed power-law background noise to arise from superposition of self-similar (fractal) structures in 2D and 3D images, derived relations between b in projection, tomosynthesis, and CBCT, and incorporated the result in a generalized detectability index.…”

Section: Introductionmentioning

confidence: 99%

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Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

et al. 2011

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Purpose: This work applies a cascaded systems model for cone-beam CT imaging performance to the design and optimization of a system for musculoskeletal extremity imaging. The model provides a quantitative guide to the selection of system geometry, source and detector components, acquisition techniques, and reconstruction parameters. Methods: The model is based on cascaded systems analysis of the 3D noise-power spectrum (NPS) and noise-equivalent quanta (NEQ) combined with factors of system geometry (magnification, focal spot size, and scatter-to-primary ratio) and anatomical background clutter. The model was extended to task-based analysis of detectability index (d 0 ) for tasks ranging in contrast and frequency content, and d 0 was computed as a function of system magnification, detector pixel size, focal spot size, kVp, dose, electronic noise, voxel size, and reconstruction filter to examine tradeoffs and optima among such factors in multivariate analysis. The model was tested quantitatively versus the measured NPS and qualitatively in cadaver images as a function of kVp, dose, pixel size, and reconstruction filter under conditions corresponding to the proposed scanner. Results: The analysis quantified trade-offs among factors of spatial resolution, noise, and dose. System magnification (M) was a critical design parameter with strong effect on spatial resolution, dose, and x-ray scatter, and a fairly robust optimum was identified at M $ 1.3 for the imaging tasks considered. The results suggested kVp selection in the range of $65-90 kVp, the lower end (65 kVp) maximizing subject contrast and the upper end maximizing NEQ (90 kVp). The analysis quantified fairly intuitive resultse.g., $0.1-0.2 mm pixel size (and a sharp reconstruction filter) optimal for high-frequency tasks (bone detail) compared to $0.4 mm pixel size (and a smooth reconstruction filter) for low-frequency (soft-tissue) tasks. This result suggests a specific protocol for 1 Â 1 (full-resolution) projection data acquisition followed by full-resolution reconstruction with a sharp filter for high-frequency tasks along with 2 Â 2 binning reconstruction with a smooth filter for low-frequency tasks. The analysis guided selection of specific source and detector components implemented on the proposed scanner. The analysis also quantified the potential benefits and points of diminishing return in focal spot size, reduced electronic noise, finer detector pixels, and low-dose limits of detectability. Theoretical results agreed quantitatively with the measured NPS and qualitatively with evaluation of cadaver images by a musculoskeletal radiologist. Conclusions: A fairly comprehensive model for 3D imaging performance in cone-beam CT combines factors of quantum noise, system geometry, anatomical background, and imaging task. The analysis provided a valuable, quantitative guide to design, optimization, and technique selection for a musculoskeletal extremities imaging system under development.

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Section: Quantum Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

et al. 2011

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“…We simulated two types of background images: a simple homogeneous background with linear attenuation set at a 50-50 mix of adipose and glandular tissue and a background with structured noise created by filtering white noise with a power law filter [32,33] f (ν) = κ/ν β , with ν the frequency, β = 3 and κ = 10 −5 mm −1 . The linear attenuation coefficients of this background were rescaled to values between the attenuation of adipose and glandular tissue.…”

Section: E Phantom Simulation and Reconstructionmentioning

confidence: 99%

Patchwork reconstruction with resolution modeling for digital breast tomosynthesis

2013

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Purpose: Digital breast tomosynthesis is a relatively new diagnostic x-ray modality that allows high resolution breast imaging while suppressing interference from overlapping anatomical structures. However, proper visualization of micro-calcifications remains a challenge. For the subset of systems we consider, the main cause of deterioration is movement of the x-ray source during exposures. We propose a modified grouped coordinate ascent algorithm that includes a specific acquisition model to compensate for this deterioration.Methods: We create a resolution model based on the movement of the x-ray source during image acquisition and combine this with a grouped coordinate ascent algorithm. Choosing planes parallel to the detector surface as the groups enables efficient implementation of the position dependent resolution model. In the current implementation, the resolution model is approximated by a Gaussian smoothing kernel.The effect of the resolution model on the iterative reconstruction is evaluated by measuring contrast to noise ratio (CNR) of spherical micro-calcifications in a homogeneous background. After this, our new reconstruction method is compared to the optimized filtered backprojection method for the considered system, by performing two observer studies: the first study simulates clusters of spherical micro-calcifications in a power law background for a free search task; the second study simulates smooth or irregular micro-calcifications in the same type of backgrounds for a classification task.Results: Including the resolution model in the iterative reconstruction methods increases the CNR of micro-calcifications. The first observer study shows a significant improvement in detection of micro-calcifications (p = 0.029) while the second study shows that performance on a classification task remains the same (p = 0.935) compared to the filtered backprojection method.Conclusions: Our method shows higher CNR and improved visualization of micro-calcifications in an observer experiment on synthetic data. Further study of the negative results of the classification task showed performance variations throughout the volume linked to the changing noise structure introduced by the combination of the resolution model and the smoothing prior.

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“…In each mammogram, the region of uniform breast thickness was identified by eroding the skin line and excluding the pectoralis muscle in the MLO views. 10 Square ROIs of 384 pixels side length were extracted, with centers spaced by 200 pixels in each direction. In total, 875 ROIs were extracted from 92 mammograms.…”

Section: Iia Image Datasetmentioning

confidence: 99%

“…2 Power-law backgrounds have been widely used as a statistical model for breast backgrounds, [3][4][5][6][7][8][9] and power-law exponents in clinical tomosynthesis and breast CT images have been investigated. 10,11 To date, power-law coefficients have been estimated from the radial averaged of the power spectrum. However, we have observed that, in general, breast structure in a mammographic ROI is oriented and therefore the corresponding periodogram does not exhibit radial symmetry.…”

Section: Introductionmentioning

confidence: 99%

On the orientation of mammographic structure

2011

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Purpose: Burgess et al. have shown that the power-spectral density of mammographic breast tissue P(f) follows a power-law, P(f) ¼ c=f b . 1 Due to the complexity of the breast anatomy, breast phantoms often make use of power-law backgrounds to approximate the irregular texture of breast images. However, the current methodology of estimating power-law coefficients assumes that the breast structure is isotropic. The purpose of this letter is to demonstrate that breast anatomic structure is not isotropic, but in fact has a preferred orientation. Further, we present a formalism to estimate power-law coefficients b and c while accounting for tissue orientation in mammographic regions-of-interests (ROIs). We then show the effect of structure orientation on b and c, as well as on the appearance of simulated power-law backgrounds. Methods: When breast tissue exhibits a preferred orientation, the radial symmetry in the associated power spectrum is broken. The new symmetry was fit by an ellipsoidal model. Ellipse tilt angle and axis ratio were accounted for in the power-law fit. Results: On average, breast structure was found to point toward the nipple: the average orientation in MLO views was 22.5 , while it was 5 for CC views, and the mean orientation for left breasts was negative while it was positive for right breasts. For both power-law magnitude and exponent, the mean difference was statistically significant ( ¼ À0.096, ¼À0.192). Conclusions: A formalism for quantification of breast structure and structure orientation is provided. The difference in power-law coefficient estimates when accounting for orientation was found to be statistically significant. Examples of statistically defined backgrounds indicate that breast structure is mimicked more closely when structure orientation is accounted for.

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Comparison of power spectra for tomosynthesis projections and reconstructed images

Cited by 61 publications

References 10 publications

Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

Patchwork reconstruction with resolution modeling for digital breast tomosynthesis

On the orientation of mammographic structure

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