Parallel cascades: New ways to describe noise transfer in medical imaging systems

Yao, Jingwu; Cunningham, Ian

doi:10.1118/1.1405842

Cited by 72 publications

(96 citation statements)

References 34 publications

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“…The basic aspects of cascaded linear systems analysis have been discussed in detail in previous work [24][25][26] as has its application in a variety of 2D imaging applications. [2][3][4][5][6][7][8][9][10][11][12] The basic concepts and derivation of the 2D projection NPS are briefly summarized below.…”

Section: Iia Background: Cascaded Linear Systems Analysismentioning

confidence: 99%

Cascaded systems analysis of the 3D noise transfer characteristics of flat‐panel cone‐beam CT

2008

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The physical factors that govern 2D and 3D imaging performance may be understood from quantitative analysis of the spatial-frequency-dependent signal and noise transfer characteristics ͓e.g., modulation transfer function ͑MTF͒, noise-power spectrum ͑NPS͒, detective quantum efficiency ͑DQE͒, and noise-equivalent quanta ͑NEQ͔͒ along with a task-based assessment of performance ͑e.g., detectability index͒. This paper advances a theoretical framework based on cascaded systems analysis for calculation of such metrics in cone-beam CT ͑CBCT͒. The model considers the 2D projection NPS propagated through a series of reconstruction stages to yield the 3D NPS and allows quantitative investigation of tradeoffs in image quality associated with acquisition and reconstruction techniques. While the mathematical process of 3D image reconstruction is deterministic, it is shown that the process is irreversible, the associated reconstruction parameters significantly affect the 3D DQE and NEQ, and system optimization should consider the full 3D imaging chain. Factors considered in the cascade include: system geometry; number of projection views; logarithmic scaling; ramp, apodization, and interpolation filters; 3D back-projection; and 3D sampling ͑noise aliasing͒. The model is validated in comparison to experiment across a broad range of dose, reconstruction filters, and voxel sizes, and the effects of 3D noise correlation on detectability are explored. The work presents a model for the 3D NPS, DQE, and NEQ of CBCT that reduces to conventional descriptions of axial CT as a special case and provides a fairly general framework that can be applied to the design and optimization of CBCT systems for various applications.

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Section: Iia Background: Cascaded Linear Systems Analysismentioning

confidence: 99%

Cascaded systems analysis of the 3D noise transfer characteristics of flat‐panel cone‐beam CT

2008

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“…The fundamental aspects of cascaded systems analysis have been discussed in previous work 3,4,33,34 in modeling the imaging chain as a series of amplification, blur, and sampling stages, where output from one stage serves as the input for the next. Early work involved application of cascaded systems analysis in modeling of indirect and direct-detection FPDs, 3,4 extended to descriptions of mammography 35 and angiography, 7,19 dual-energy imaging, 36,37 tomosynthesis, 15 and CBCT.…”

Section: Iia Cascaded Systems Analysismentioning

confidence: 99%

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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“…Parallel events may occur ͑depending on the x-ray energy and phosphor͒ as a result of the production of secondary x rays. 46,47 The signal and NPS at the output of stage 2 can be written as 38 ⌽ 2 ͑u,v͒ = g 1 ⌽ 0 T 2 ͑u,v͒W…”

Section: Iia2 Stage 2 X-ray-to-light Conversionmentioning

confidence: 99%

Accurate MTF measurement in digital radiography using noise response

et al. 2010

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Purpose:The authors describe a new technique to determine the system presampled modulation transfer function ͑MTF͒ in digital radiography using only the detector noise response. Methods: A cascaded-linear systems analysis was used to develop an exact relationship between the two-dimensional noise power spectrum ͑NPS͒ and the presampled MTF for a generalized detector system. This relationship was then utilized to determine the two-dimensional presampled MTF. For simplicity, aliasing of the correlated noise component of the NPS was assumed to be negligible. Accuracy of this method was investigated using simulated images from a simple detector model in which the "true" MTF was known exactly. Measurements were also performed on three detector technologies ͑an x-ray image intensifier, an indirect flat panel detector, and a solid state x-ray image intensifier͒, and the results were compared using the standard edge-response method. Flat-field and edge images were acquired and analyzed according to guidelines set forth by the International Electrotechnical Commission, using the RQA 5 spectrum. Results: The presampled MTF determined using the noise-response method for the simulated detector system was in close agreement with the true MTF with an averaged percent difference of 0.3% and a maximum difference of 1.1% observed at the Nyquist frequency ͑f N ͒. The edgeresponse method of the simulated detector system also showed very good agreement at lower spatial frequencies ͑less than 0.5 f N ͒ with an averaged percent difference of 1.6% but showed significant discrepancies at higher spatial frequencies ͑greater than 0.5 f N ͒ with an averaged percent difference of 17%. Discrepancies were in part a result of noise in the edge image and phasing errors. For all three detector systems, the MTFs obtained using the two methods were found to be in good agreement at spatial frequencies less than 0.5 f N with an averaged percent difference of 3.4%. Above 0.5 f N , differences increased to an average of 20%. Deviations of the experimental results largely followed the trend seen in the simulation results, suggesting that differences between the two methods could be explained as resulting from the inherent inaccuracies of the edgeresponse measurement technique used in this study. Aliasing of the correlated noise component was shown to have a minimal effect on the measured MTF for the three detectors studied. Systems with significant aliasing of the correlated noise component ͑e.g., a-Se based detectors͒ would likely require a more sophisticated fitting scheme to provide accurate results. Conclusions: Results indicate that the noise-response method, a simple technique, can be used to accurately measure the MTF of digital x-ray detectors, while alleviating the problems and inaccuracies associated with use of precision test objects, such as a slit or an edge.

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Parallel cascades: New ways to describe noise transfer in medical imaging systems

Cited by 72 publications

References 34 publications

Cascaded systems analysis of the 3D noise transfer characteristics of flat‐panel cone‐beam CT

Cascaded systems analysis of the 3D noise transfer characteristics of flat‐panel cone‐beam CT

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

Accurate MTF measurement in digital radiography using noise response

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