Analysis of noise power spectrum for linear and non-linear reconstruction algorithms for CT

Pal, Debashish; Kulkarni, Shweta S.; Yadava, G; Thibault, Baptiste; Sauer, Ken D.; Hsieh, Jiang

doi:10.1109/nssmic.2011.6153844

Cited by 8 publications

(12 citation statements)

References 3 publications

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“…[14][15][16][17] To investigate this potential interplay between noise and spatial resolution, we measured noise standard deviation value σ locally on the edges of FOIs where spatial resolution was assessed with the help of the acquired image ensembles:…”

Section: D the Tradeoff Of Spatial Resolution With Edge Noisementioning

confidence: 99%

“…14,16,17 An immediate question based on the results in Fig. 10(a) is, how does the noise magnitude of Veo reconstructions compared with that of FBP reconstructions if the noise was strictly measured on the edges of test objects?…”

Section: E Spatial Resolution-noise Tradeoffmentioning

confidence: 99%

“…Due to the intrinsic nonlinearity of MBIR methods, current CT protocols that may have been optimized for filtered backprojection (FBP) are not directly applicable to MBIR. [14][15][16][17] However, the basic principle of CT protocol optimization remains the same for both FBP and MBIR: First, the physical characteristics (e.g., noise variance, noise power spectrum, spatial resolution) of the CT systems need to be quantified using physical phantoms; Second, the dependence of image quality metrics on scanning parameters (e.g., kV, mAs, slice thickness) needs to be understood both experimentally and theoretically if possible; Third, clinical diagnostic imaging tasks need to be identified and the relationship between image quality metrics measured by physicists and diagnostic quality as assessed by radiologists should be established. This enables translation from image quality requirement for each specific task to scanning parameters; Fourth, CT scanning parameters are prescribed to generate images with desirable qualities that meet the requirement of clinicians; Fifth, the prescribed protocols need to be continuously reviewed and refined by a joint group of clinicians, medical physicists, and technologists to ensure they are repeatable, robust, implementable, and relevant.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…[14][15][16][17] For example, several studies have reported significant reduction in noise magnitude when compared to FBP. 14,16,17 It has been found that with MBIR the magnitude of noise reduction is related to the radiation dose level through a power law. 17 Further, these studies found that the amount of spatial noise correlation increases in MBIR images, which was demonstrated in the spatial frequency domain as a shift of the peak of the noise power spectrum (NPS) toward lower spatial frequencies with the amount of shift related to the radiation dose through a power law.…”

Section: Introductionmentioning

confidence: 99%

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Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

Garrett

et al. 2014

Med. Phys.

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Purpose:Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. Methods:The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDI vol =4,8,12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo R , GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d . Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) The z resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. Conclusions: Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in ...

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Section: D the Tradeoff Of Spatial Resolution With Edge Noisementioning

confidence: 99%

Section: E Spatial Resolution-noise Tradeoffmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

Garrett

et al. 2014

Med. Phys.

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Impact of iterative reconstruction algorithms on the applicability of Fourier‐based detectability index for x‐ray CT imaging

Pouget

Dedieu

2021

Medical Physics

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Purpose The increasing application of iterative reconstruction algorithms in clinical computed tomography to improve image quality and reduce radiation dose, elicits strong interest, and needs model observers to optimize CT scanning protocols objectively and efficiently. The current paradigm for evaluating imaging system performance relies on Fourier methods, which presuppose a linear, wide‐sense stationary system. Long‐range correlations introduced by iterative reconstruction algorithms may narrow the applicability of Fourier techniques. Differences in the implementation of reconstruction algorithms between manufacturers add further complexity. The present work set out to quantify the errors entailed by the use of Fourier methods, which can lead to design decisions that do not correlate with detectability. Methods To address this question, we evaluated the noise properties and the detectability index of the ideal linear observer using the spatial approach and the Fourier‐based approach. For this purpose, a homogeneous phantom was imaged on two scanners: the Revolution CT (GE Healthcare) and the Somatom Definition AS+ (Siemens Healthcare) at different exposure levels. Images were reconstructed using different strength levels of IR algorithms available on the systems considered: Adaptative Statistical Iterative Reconstruction (ASIR‐V) and Sinogram Affirmed Iterative Reconstruction (SAFIRE). Results Our findings highlight that the spatial domain estimate of the detectability index is higher than the Fourier domain estimate. This trend is found to be dependent on the specific regularization used by IR algorithms as well as the signal to be detected. The eigenanalysis of the noise covariance matrix and of its circulant approximation yields explanation about the evoked trends. In particular, this analysis suggests that the predictive power of the Fourier‐based ideal linear observer depends on the ability of each basis analyzed to be relevant to the signal to be detected. Conclusion The applicability of Fourier techniques is dependent on the specific regularization used by IR algorithms. These results argue for verifying the assumptions made when using Fourier methods since Fourier‐task‐based detectability index does not always correlate with signal detectability.

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Experimental measurement of local noise power spectrum (NPS) in photon counting detector‐CT (PCD‐CT) using a single data acquisition

Zhang,

Li,

Zhang

et al. 2024

Medical Physics

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BackgroundAccurate noise power spectra (NPS) measurement in clinical X‐ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable.PurposeTo develop a method for estimating local NPS from a single photon counting detector‐CT (PCD‐CT) acquisition.MethodsA novel nearly statistical bias‐free estimator was constructed from the raw counts data of PCD‐CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point‐wise covariance between any two image pixel/voxel locations and . A Fourier transform is applied to obtain the desired point‐wise NPS for any chosen location . The method was validated using experimental data acquired from a benchtop PCD‐CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensemble averaging.ResultsThe experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given local ROI.ConclusionA new method was developed to enable local NPS from a single PCD‐CT acquisition.

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Analysis of noise power spectrum for linear and non-linear reconstruction algorithms for CT

Cited by 8 publications

References 3 publications

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

Impact of iterative reconstruction algorithms on the applicability of Fourier‐based detectability index for x‐ray CT imaging

Experimental measurement of local noise power spectrum (NPS) in photon counting detector‐CT (PCD‐CT) using a single data acquisition

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