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3d wavelets denoising with the matlab function wavedec3  (MathWorks Inc)


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    MathWorks Inc 3d wavelets denoising with the matlab function wavedec3
    Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of <t>ML/DL-denoising</t> methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.
    3d Wavelets Denoising With The Matlab Function Wavedec3, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Average 90 stars, based on 1 article reviews
    3d wavelets denoising with the matlab function wavedec3 - by Bioz Stars, 2026-03
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    1) Product Images from "Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography"

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    Journal: Journal of Imaging

    doi: 10.3390/jimaging8060156

    Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of ML/DL-denoising methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.
    Figure Legend Snippet: Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of ML/DL-denoising methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.

    Techniques Used: Generated, Control

    Graphical overview of the Probabilistic Mumford–Shah (PMS) framework. ( A ) Summary of the parameters and variables. ( B ) Core rSPA algorithm idea: 3D-denoising with the regularized Scalable Probabilistic Approximation algorithm (rSPA). Given the (noisy) CT voxel data V , rSPA minimizes the function L ( C , Γ ) and seeks for the optimal segmentation of V in terms of the K spatially-persistent latent features characterized by the latent feature probabilities in K rows of the matrix Γ , as well as by the latent colors as K columns of the latent color matrix C . Persistency of the feature segmentation is imposed by the second term of the right-hand side of the function L ( C , Γ ) , which penalizes the differences in feature probability values in the spatially-neighboring points. ( C ) Denoising idea: latent feature probabilities are persistent (slowly-changing) 3D functions. ( D ) Graphical representation of the overlapping domain decomposition used in the parallel DD-rSPA algorithm.
    Figure Legend Snippet: Graphical overview of the Probabilistic Mumford–Shah (PMS) framework. ( A ) Summary of the parameters and variables. ( B ) Core rSPA algorithm idea: 3D-denoising with the regularized Scalable Probabilistic Approximation algorithm (rSPA). Given the (noisy) CT voxel data V , rSPA minimizes the function L ( C , Γ ) and seeks for the optimal segmentation of V in terms of the K spatially-persistent latent features characterized by the latent feature probabilities in K rows of the matrix Γ , as well as by the latent colors as K columns of the latent color matrix C . Persistency of the feature segmentation is imposed by the second term of the right-hand side of the function L ( C , Γ ) , which penalizes the differences in feature probability values in the spatially-neighboring points. ( C ) Denoising idea: latent feature probabilities are persistent (slowly-changing) 3D functions. ( D ) Graphical representation of the overlapping domain decomposition used in the parallel DD-rSPA algorithm.

    Techniques Used:

    Radiation exposure, quantum noise, and denoising performance of CNNs and rSPA in low-radiation and ultra-low-radiation thorax CT regimes. ( A ) Reference data of a thorax CT voxel fragment (approx. 5 cm 3 ) of a 19-year-old female with the BMI 27.5, acquired with the Somatum Emotion 16 2007 (Siemens Aktiengesellschaft, Berlin, Germany) at 130 kV tube voltage. ( B ) Simulated decrease in the radiation exposure CTDI vol from 15.6 mGy (reference frame) to 3.3 mGy (for low-radiation simulations) and 0.5 mGy (ultra-low-radiation) results in a significant increase of quantum noise. ( C ) Reconstructed images using CNNs. ( D ) Reconstructed images using rSPA. ( E ) 3D segmentation of the original reference frame. ( F ) 3D segmentation based on the images denoised using CNNs. ( G ) 3D-segmentation of the images denoised by rSPA.
    Figure Legend Snippet: Radiation exposure, quantum noise, and denoising performance of CNNs and rSPA in low-radiation and ultra-low-radiation thorax CT regimes. ( A ) Reference data of a thorax CT voxel fragment (approx. 5 cm 3 ) of a 19-year-old female with the BMI 27.5, acquired with the Somatum Emotion 16 2007 (Siemens Aktiengesellschaft, Berlin, Germany) at 130 kV tube voltage. ( B ) Simulated decrease in the radiation exposure CTDI vol from 15.6 mGy (reference frame) to 3.3 mGy (for low-radiation simulations) and 0.5 mGy (ultra-low-radiation) results in a significant increase of quantum noise. ( C ) Reconstructed images using CNNs. ( D ) Reconstructed images using rSPA. ( E ) 3D segmentation of the original reference frame. ( F ) 3D segmentation based on the images denoised using CNNs. ( G ) 3D-segmentation of the images denoised by rSPA.

    Techniques Used:

    Comparing denoising performance on synthetic CT images of noisy circles, with DL from additionally trained to recognize circles for non-Gaussian noise model: ( A ) medium noise scenario, corresponding to low-radiation regime with around 3.3 mGy; ( B ) high noise scenario, corresponding to ultra-low-radiation regime with 0.5 mGy.
    Figure Legend Snippet: Comparing denoising performance on synthetic CT images of noisy circles, with DL from additionally trained to recognize circles for non-Gaussian noise model: ( A ) medium noise scenario, corresponding to low-radiation regime with around 3.3 mGy; ( B ) high noise scenario, corresponding to ultra-low-radiation regime with 0.5 mGy.

    Techniques Used:

    Comparing denoising quality, cost and parallelizability: ( A – C ) comparison of PMS rSPA algorithm to the regularized Mumford–Shah denoising tool introduced in and to the additionally trained DL denoising algorithm from and ; ( D , E ) computational cost scaling and performance for DL (without taking into account time for additional training), sequential rSPA, parallel DD-rSPA and DD-rSPA followed by DL. Each point of each method’s curve and surface is obtained from statistical averaging of the respective values obtained by analyzing 10 randomly-generated images with these particular combinations of image size and noise level.
    Figure Legend Snippet: Comparing denoising quality, cost and parallelizability: ( A – C ) comparison of PMS rSPA algorithm to the regularized Mumford–Shah denoising tool introduced in and to the additionally trained DL denoising algorithm from and ; ( D , E ) computational cost scaling and performance for DL (without taking into account time for additional training), sequential rSPA, parallel DD-rSPA and DD-rSPA followed by DL. Each point of each method’s curve and surface is obtained from statistical averaging of the respective values obtained by analyzing 10 randomly-generated images with these particular combinations of image size and noise level.

    Techniques Used: Comparison, Generated

    Comparing CT image denoising performances for three CT noise models: ( A ) additive Gaussian noise model (CT noise variance is independent of the feature color); ( B ) multiplicative non-Gaussian noise model (CT noise variance changes with the amplitude of the underlying color signal); ( C ) empirical noise obtained from the thorax CT patient data. In ( A , B ), generation of synthetic images was performed for a patient with a water-equivalent diameter of 30 cm, which is subject to a Thorax CT with a typical tube voltage of 120 kV in the range of tube currents between 5–180 mA and a set of artificial anatomic features from A (with a feature contrast of 200 HU). In ( C ), real patient data were used. Comparison is performed with three primary image quality criteria: with mean squared error (left panels); with peak signal-to-noise ratio (middle panels); and with the 3D multiscale structural similarity index (right panels).
    Figure Legend Snippet: Comparing CT image denoising performances for three CT noise models: ( A ) additive Gaussian noise model (CT noise variance is independent of the feature color); ( B ) multiplicative non-Gaussian noise model (CT noise variance changes with the amplitude of the underlying color signal); ( C ) empirical noise obtained from the thorax CT patient data. In ( A , B ), generation of synthetic images was performed for a patient with a water-equivalent diameter of 30 cm, which is subject to a Thorax CT with a typical tube voltage of 120 kV in the range of tube currents between 5–180 mA and a set of artificial anatomic features from A (with a feature contrast of 200 HU). In ( C ), real patient data were used. Comparison is performed with three primary image quality criteria: with mean squared error (left panels); with peak signal-to-noise ratio (middle panels); and with the 3D multiscale structural similarity index (right panels).

    Techniques Used: Comparison

    Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM): ( A ) varying the true underlying feature contrast and LAR for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the true underlying feature contrast and LAR for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm; ( C ) denoising performance comparison when varying the patient size and the effective absorbed radiation dose density, with the 200 Hounsfield Units (HU) feature contrast differences.
    Figure Legend Snippet: Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM): ( A ) varying the true underlying feature contrast and LAR for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the true underlying feature contrast and LAR for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm; ( C ) denoising performance comparison when varying the patient size and the effective absorbed radiation dose density, with the 200 Hounsfield Units (HU) feature contrast differences.

    Techniques Used: Comparison

    Deterioration of CT image quality (decrease in 3D MS-SSIM index, baseline = 100%) caused by a reduction of lifetime attributable risk (LAR) for different methods. The CT scans pertain to the infant patient, with a fixed feature contrast of 200 HU.
    Figure Legend Snippet: Deterioration of CT image quality (decrease in 3D MS-SSIM index, baseline = 100%) caused by a reduction of lifetime attributable risk (LAR) for different methods. The CT scans pertain to the infant patient, with a fixed feature contrast of 200 HU.

    Techniques Used:

    Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM) for simulated thorax CT: ( A ) varying the absorbed radiation dose for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the absorbed radiation dose for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm. Noiseless thorax CT image used as reference in this performance comparison is available at https://www.dropbox.com/s/29x0xivg8l80q10/female_lung_thorax_CT_image_section_v2.mat?dl=0 (accessed on 18 March 2022). Dotted lines show 95% nonparametric confidence intervals (c.i.) obtained for every value of CTDI vol from 100 different independently-generated noisy synthetic CT images, using the MATLAB function quantile() .
    Figure Legend Snippet: Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM) for simulated thorax CT: ( A ) varying the absorbed radiation dose for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the absorbed radiation dose for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm. Noiseless thorax CT image used as reference in this performance comparison is available at https://www.dropbox.com/s/29x0xivg8l80q10/female_lung_thorax_CT_image_section_v2.mat?dl=0 (accessed on 18 March 2022). Dotted lines show 95% nonparametric confidence intervals (c.i.) obtained for every value of CTDI vol from 100 different independently-generated noisy synthetic CT images, using the MATLAB function quantile() .

    Techniques Used: Comparison, Generated



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    3d wavelets denoising with the matlab function wavedec3  (MathWorks Inc)
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    MathWorks Inc 3d wavelets denoising with the matlab function wavedec3
    Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of <t>ML/DL-denoising</t> methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.
    3d Wavelets Denoising With The Matlab Function Wavedec3, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/3d wavelets denoising with the matlab function wavedec3/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    3d wavelets denoising with the matlab function wavedec3 - by Bioz Stars, 2026-03
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    Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of ML/DL-denoising methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Graphical representation of our proposed pipeline workflow for automated generation and risk assessment of CT images. ( A ): Initial reference data can be either a set of real CT-data generated using high-dose radiation or artificially simulated data. ( B ) Exemplary high-quality and low-quantum noise image of lung vessels. ( C ) exemplary low-dose CT images with high quantum noise. ( D ) Workflow from image generation to subsequent benchmarking of ML/DL-denoising methods. Starting with high-quality data or artificially generated reference data, respectively, a spectrum of image noise σ is added for a multitude of combinations from patient-specific and CT control variables, as suggested in Equation . The noisy images are then denoised using various state-of-the-art methods and the processed images are compared to the original reference data.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques: Generated, Control

    Graphical overview of the Probabilistic Mumford–Shah (PMS) framework. ( A ) Summary of the parameters and variables. ( B ) Core rSPA algorithm idea: 3D-denoising with the regularized Scalable Probabilistic Approximation algorithm (rSPA). Given the (noisy) CT voxel data V , rSPA minimizes the function L ( C , Γ ) and seeks for the optimal segmentation of V in terms of the K spatially-persistent latent features characterized by the latent feature probabilities in K rows of the matrix Γ , as well as by the latent colors as K columns of the latent color matrix C . Persistency of the feature segmentation is imposed by the second term of the right-hand side of the function L ( C , Γ ) , which penalizes the differences in feature probability values in the spatially-neighboring points. ( C ) Denoising idea: latent feature probabilities are persistent (slowly-changing) 3D functions. ( D ) Graphical representation of the overlapping domain decomposition used in the parallel DD-rSPA algorithm.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Graphical overview of the Probabilistic Mumford–Shah (PMS) framework. ( A ) Summary of the parameters and variables. ( B ) Core rSPA algorithm idea: 3D-denoising with the regularized Scalable Probabilistic Approximation algorithm (rSPA). Given the (noisy) CT voxel data V , rSPA minimizes the function L ( C , Γ ) and seeks for the optimal segmentation of V in terms of the K spatially-persistent latent features characterized by the latent feature probabilities in K rows of the matrix Γ , as well as by the latent colors as K columns of the latent color matrix C . Persistency of the feature segmentation is imposed by the second term of the right-hand side of the function L ( C , Γ ) , which penalizes the differences in feature probability values in the spatially-neighboring points. ( C ) Denoising idea: latent feature probabilities are persistent (slowly-changing) 3D functions. ( D ) Graphical representation of the overlapping domain decomposition used in the parallel DD-rSPA algorithm.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques:

    Radiation exposure, quantum noise, and denoising performance of CNNs and rSPA in low-radiation and ultra-low-radiation thorax CT regimes. ( A ) Reference data of a thorax CT voxel fragment (approx. 5 cm 3 ) of a 19-year-old female with the BMI 27.5, acquired with the Somatum Emotion 16 2007 (Siemens Aktiengesellschaft, Berlin, Germany) at 130 kV tube voltage. ( B ) Simulated decrease in the radiation exposure CTDI vol from 15.6 mGy (reference frame) to 3.3 mGy (for low-radiation simulations) and 0.5 mGy (ultra-low-radiation) results in a significant increase of quantum noise. ( C ) Reconstructed images using CNNs. ( D ) Reconstructed images using rSPA. ( E ) 3D segmentation of the original reference frame. ( F ) 3D segmentation based on the images denoised using CNNs. ( G ) 3D-segmentation of the images denoised by rSPA.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Radiation exposure, quantum noise, and denoising performance of CNNs and rSPA in low-radiation and ultra-low-radiation thorax CT regimes. ( A ) Reference data of a thorax CT voxel fragment (approx. 5 cm 3 ) of a 19-year-old female with the BMI 27.5, acquired with the Somatum Emotion 16 2007 (Siemens Aktiengesellschaft, Berlin, Germany) at 130 kV tube voltage. ( B ) Simulated decrease in the radiation exposure CTDI vol from 15.6 mGy (reference frame) to 3.3 mGy (for low-radiation simulations) and 0.5 mGy (ultra-low-radiation) results in a significant increase of quantum noise. ( C ) Reconstructed images using CNNs. ( D ) Reconstructed images using rSPA. ( E ) 3D segmentation of the original reference frame. ( F ) 3D segmentation based on the images denoised using CNNs. ( G ) 3D-segmentation of the images denoised by rSPA.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques:

    Comparing denoising performance on synthetic CT images of noisy circles, with DL from additionally trained to recognize circles for non-Gaussian noise model: ( A ) medium noise scenario, corresponding to low-radiation regime with around 3.3 mGy; ( B ) high noise scenario, corresponding to ultra-low-radiation regime with 0.5 mGy.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Comparing denoising performance on synthetic CT images of noisy circles, with DL from additionally trained to recognize circles for non-Gaussian noise model: ( A ) medium noise scenario, corresponding to low-radiation regime with around 3.3 mGy; ( B ) high noise scenario, corresponding to ultra-low-radiation regime with 0.5 mGy.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques:

    Comparing denoising quality, cost and parallelizability: ( A – C ) comparison of PMS rSPA algorithm to the regularized Mumford–Shah denoising tool introduced in and to the additionally trained DL denoising algorithm from and ; ( D , E ) computational cost scaling and performance for DL (without taking into account time for additional training), sequential rSPA, parallel DD-rSPA and DD-rSPA followed by DL. Each point of each method’s curve and surface is obtained from statistical averaging of the respective values obtained by analyzing 10 randomly-generated images with these particular combinations of image size and noise level.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Comparing denoising quality, cost and parallelizability: ( A – C ) comparison of PMS rSPA algorithm to the regularized Mumford–Shah denoising tool introduced in and to the additionally trained DL denoising algorithm from and ; ( D , E ) computational cost scaling and performance for DL (without taking into account time for additional training), sequential rSPA, parallel DD-rSPA and DD-rSPA followed by DL. Each point of each method’s curve and surface is obtained from statistical averaging of the respective values obtained by analyzing 10 randomly-generated images with these particular combinations of image size and noise level.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques: Comparison, Generated

    Comparing CT image denoising performances for three CT noise models: ( A ) additive Gaussian noise model (CT noise variance is independent of the feature color); ( B ) multiplicative non-Gaussian noise model (CT noise variance changes with the amplitude of the underlying color signal); ( C ) empirical noise obtained from the thorax CT patient data. In ( A , B ), generation of synthetic images was performed for a patient with a water-equivalent diameter of 30 cm, which is subject to a Thorax CT with a typical tube voltage of 120 kV in the range of tube currents between 5–180 mA and a set of artificial anatomic features from A (with a feature contrast of 200 HU). In ( C ), real patient data were used. Comparison is performed with three primary image quality criteria: with mean squared error (left panels); with peak signal-to-noise ratio (middle panels); and with the 3D multiscale structural similarity index (right panels).

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Comparing CT image denoising performances for three CT noise models: ( A ) additive Gaussian noise model (CT noise variance is independent of the feature color); ( B ) multiplicative non-Gaussian noise model (CT noise variance changes with the amplitude of the underlying color signal); ( C ) empirical noise obtained from the thorax CT patient data. In ( A , B ), generation of synthetic images was performed for a patient with a water-equivalent diameter of 30 cm, which is subject to a Thorax CT with a typical tube voltage of 120 kV in the range of tube currents between 5–180 mA and a set of artificial anatomic features from A (with a feature contrast of 200 HU). In ( C ), real patient data were used. Comparison is performed with three primary image quality criteria: with mean squared error (left panels); with peak signal-to-noise ratio (middle panels); and with the 3D multiscale structural similarity index (right panels).

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques: Comparison

    Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM): ( A ) varying the true underlying feature contrast and LAR for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the true underlying feature contrast and LAR for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm; ( C ) denoising performance comparison when varying the patient size and the effective absorbed radiation dose density, with the 200 Hounsfield Units (HU) feature contrast differences.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM): ( A ) varying the true underlying feature contrast and LAR for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the true underlying feature contrast and LAR for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm; ( C ) denoising performance comparison when varying the patient size and the effective absorbed radiation dose density, with the 200 Hounsfield Units (HU) feature contrast differences.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques: Comparison

    Deterioration of CT image quality (decrease in 3D MS-SSIM index, baseline = 100%) caused by a reduction of lifetime attributable risk (LAR) for different methods. The CT scans pertain to the infant patient, with a fixed feature contrast of 200 HU.

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Deterioration of CT image quality (decrease in 3D MS-SSIM index, baseline = 100%) caused by a reduction of lifetime attributable risk (LAR) for different methods. The CT scans pertain to the infant patient, with a fixed feature contrast of 200 HU.

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques:

    Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM) for simulated thorax CT: ( A ) varying the absorbed radiation dose for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the absorbed radiation dose for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm. Noiseless thorax CT image used as reference in this performance comparison is available at https://www.dropbox.com/s/29x0xivg8l80q10/female_lung_thorax_CT_image_section_v2.mat?dl=0 (accessed on 18 March 2022). Dotted lines show 95% nonparametric confidence intervals (c.i.) obtained for every value of CTDI vol from 100 different independently-generated noisy synthetic CT images, using the MATLAB function quantile() .

    Journal: Journal of Imaging

    Article Title: Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography

    doi: 10.3390/jimaging8060156

    Figure Lengend Snippet: Comparing denoising methods with the average Multiscale Structural Similarity Index (3D MS-SSIM) for simulated thorax CT: ( A ) varying the absorbed radiation dose for a synthetic 30-year-old female patient with a water-equiv. cross-section of 27 cm; ( B ) varying the absorbed radiation dose for a synthetic 1-year-old female infant patient with a water-equiv. cross-section of 12.7 cm. Noiseless thorax CT image used as reference in this performance comparison is available at https://www.dropbox.com/s/29x0xivg8l80q10/female_lung_thorax_CT_image_section_v2.mat?dl=0 (accessed on 18 March 2022). Dotted lines show 95% nonparametric confidence intervals (c.i.) obtained for every value of CTDI vol from 100 different independently-generated noisy synthetic CT images, using the MATLAB function quantile() .

    Article Snippet: We used denoising methods based on local window filtering of the data (3D Gaussian filtering with the MATLAB function imgaussfilt3() , 3D local median filtering with the MATLAB function medfilt3() and bilateral filtering with the MATLAB function imbilatfilt() ) [ , , , ], spectral denoising methods (the 3D wavelets denoising with the MATLAB function wavedec3() ) [ , , , , ] and a deep learning denoising method based on pre-trained feed-forward denoising convolutional neural networks (DnCNNs, with the MATLAB functions denoiseImage() and denoisingNetwork() ) [ , , , ].

    Techniques: Comparison, Generated