Autocalibration method for non-stationary CT bias correction

Vegas-Sánchez-Ferrero, Gonzalo; Ledesma-Carbayo, María J.; Washko, George R.; Estépar, Raúl San Jośe

doi:10.1016/j.media.2017.12.004

Cited by 8 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(b) as the distribution becomes more symmetric. This effect has been extensively observed in the literature for clinical CT scans — especially in the trachea — due to the increase of noise variance caused by the surrounding densities of tissues …”

Section: Methodsmentioning

confidence: 80%

“…The presence of different tissues can be effectively modeled by a mixture model of nc‐Γ distributions to the CT:

p false(x (r) false) = \sum_{j = 1}^{J} π_{j} false(boldr false) f_{normalΓ} false(x (r) false| α_{j} (r), β_{j} (r), δ false)

for J components, where

π_{j}

are the weights of the mixture and

α_{j}

β_{j}

the parameters of the j ‐th component. This model allows us to describe the heterogeneous nature of tissues, the spatially variant response of noise, and its statistical properties described in the literature …”

Section: Methodsmentioning

confidence: 99%

“…We also represent the regression line

false⟨ X (r) false⟩ = b_{normalair} {\overset{σ}{true}}^{2} false(boldr false) + a_{normalair}

. The regression coefficient depends on tissue density …”

Section: Methodsmentioning

confidence: 99%

“…Noise model. The noise generation was performed following the characteristics of noise described in the literature: (a) right skewness, (b) linear relationship between mean and variance, and (c) spatially variant noise . Following these features, the signal is set as the most likely value of a random realization of noise: the mode.…”

Section: Methodsmentioning

confidence: 99%

“…This methodology characterizes the signal/noise relationship through a statistical model that describes the spatially variant nature of noise and retrieves both components of signal and noise. Then, we combine the information obtained from the signal and noise estimates to remove the spatially variant and systematic biases across the scan following the method we proposed . Since this methodology does not consider any combination of parameters for the reconstruction as a preferential standard, we refer to this method as a harmonization methodology.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Harmonization of chest CT scans for different doses and reconstruction methods

Vegas-Sánchez-Ferrero

Ledesma-Carbayo

Washko

et al. 2019

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose: To develop and validate a computed tomography (CT) harmonization technique by combining noise-stabilization and autocalibration methodologies to provide reliable densitometry measurements in heterogeneous acquisition protocols. Methods: We propose to reduce the effects of spatially variant noise such as nonuniform patterns of noise and biases. The method combines the statistical characterization of the signal-to-noise relationship in the CT image intensities, which allows us to estimate both the signal and spatially variant variance of noise, with an autocalibration technique that reduces the nonuniform biases caused by noise and reconstruction techniques. The method is firstly validated with anthropomorphic synthetic images that simulate CT acquisitions with variable scanning parameters: different dosage, nonhomogeneous variance of noise, and various reconstruction methods. We finally evaluate these effects and the ability of our method to provide consistent densitometric measurements in a cohort of clinical chest CT scans from two vendors (Siemens, n = 54 subjects; and GE, n = 50 subjects) acquired with several reconstruction algorithms (filtered back-projection and iterative reconstructions) with highdose and low-dose protocols. Results: The harmonization reduces the effect of nonhomogeneous noise without compromising the resolution of the images (25% RMSE reduction in both clinical datasets). An analysis through hierarchical linear models showed that the average biases induced by differences in dosage and reconstruction methods are also reduced up to 74.20%, enabling comparable results between high-dose and low-dose reconstructions. We also assessed the statistical similarity between acquisitions obtaining increases of up to 30% points and showing that the low-dose vs high-dose comparisons of harmonized data obtain similar and even higher similarity than the observed for high-dose vs high-dose comparisons of nonharmonized data. Conclusion: The proposed harmonization technique allows to compare measures of low-dose with high-dose acquisitions without using a specific reconstruction as a reference. Since the harmonization does not require a precalibration with a phantom, it can be applied to retrospective studies. This approach might be suitable for multicenter trials for which a reference reconstruction is not feasible or hard to define due to differences in vendors, models, and reconstruction techniques.

show abstract

Section: Methodsmentioning

confidence: 80%

“…The presence of different tissues can be effectively modeled by a mixture model of nc‐Γ distributions to the CT:

p false(x (r) false) = \sum_{j = 1}^{J} π_{j} false(boldr false) f_{normalΓ} false(x (r) false| α_{j} (r), β_{j} (r), δ false)

for J components, where

π_{j}

are the weights of the mixture and

α_{j}

β_{j}

Section: Methodsmentioning

confidence: 99%

“…We also represent the regression line

false⟨ X (r) false⟩ = b_{normalair} {\overset{σ}{true}}^{2} false(boldr false) + a_{normalair}

. The regression coefficient depends on tissue density …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Harmonization of chest CT scans for different doses and reconstruction methods

Vegas-Sánchez-Ferrero

Ledesma-Carbayo

Washko

et al. 2019

Medical Physics

Self Cite

View full text Add to dashboard Cite

show abstract

Deep‐learning‐based direct inversion for material decomposition

et al. 2020

View full text Add to dashboard Cite

Purpose To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi‐energy computed tomography (CT) images without performing conventional material decomposition. Methods The proposed CNN (denoted as Incept‐net) followed the general framework of encoder–decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi‐energy CT. Incept‐net was implemented with a customized loss function, including an in‐house‐designed image‐gradient‐correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood–iodine mixture, and fat] were scanned using a research photon‐counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different‐configurations of full field‐of‐view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept‐net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least‐square‐based material decomposition (LS‐MD), total‐variation regularized material decomposition (TV‐MD), and U‐net‐based method. Results Incept‐net improved accuracy of the predicted mass density of basis materials compared with the U‐net, TV‐MD, and LS‐MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept‐net, U‐net, TV‐MD, and LS‐MD, respectively, across all iodine‐present inserts (2.0–24.0 mgI/cc). With the LS‐MD as the baseline, Incept‐net and U‐net achieved comparable noise reduction (both around 95%), both higher than TV‐MD (85%). The proposed IGC regularizer effectively helped both Incept‐net and U‐net to reduce image artifact. Incept‐net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept‐net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept‐net achieved relatively improved performance than the comparator U‐net, indicating that performance gain by Incept‐net was not achieved by simply increasing network learning capacity. Conclusion Incept‐net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept‐net generalized and performed well with unseen image structures and d...

show abstract

Automatic quantification of epicardial adipose tissue volume

Sun

et al. 2021

Medical Physics

View full text Add to dashboard Cite

Purpose Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans. Methods A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi‐slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT. Results The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1% ± 0.7% and 96.9% ± 0.6%, respectively. The inter‐observer variability was also assessed, resulting in a Dice index of 97.0% ± 0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4% ± 1.5% and 93.3% ± 1.3%, respectively, and the same measurement between the experts themselves was 93.6% ± 1.9%. The Pearson’s correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99. Conclusions This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg.

show abstract

Autocalibration method for non-stationary CT bias correction

Cited by 8 publications

References 25 publications

Harmonization of chest CT scans for different doses and reconstruction methods

Harmonization of chest CT scans for different doses and reconstruction methods

Deep‐learning‐based direct inversion for material decomposition

Automatic quantification of epicardial adipose tissue volume

Contact Info

Product

Resources

About