A method to extract image noise level from patient images in CT

Malkus, Annelise; Szczykutowicz, Timothy P.

doi:10.1002/mp.12240

Cited by 36 publications

(55 citation statements)

References 15 publications

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“…A rigorous assessment of the GN algorithm in these exams is needed. The cited references on variations on the GN algorithm 11,12 show promising results of general application of the GN algorithm to clinical CT image data.…”

Section: Discussionmentioning

confidence: 99%

“…Malkus et al. used a version of the GN algorithm to measure noise in the air surrounding a patient, and found that this measurement to be a correlated surrogate to region‐of‐interest (ROI) noise measurements in the liver 12 …”

Section: Introductionmentioning

confidence: 99%

“…11 Malkus et al used a version of the GN algorithm to measure noise in the air surrounding a patient, and found that this measurement to be a correlated surrogate to region-of-interest (ROI) noise measurements in the liver. 12 Although these variations on the GN algorithm are encouraging advances in monitoring clinical image quality, there is a general lack of thorough and quantitatively precise accuracy assessment of the GN algorithm with respect to a reference standard. This is mainly due to the difficulty in obtaining ground truth image quality metrics in patient studies.…”

Section: Introductionmentioning

confidence: 99%

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A Benchmark for automatic noise measurement in clinical computed tomography

et al. 2020

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Purpose: Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrastenhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature. Methods: Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters. Results: The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of AE0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively. Conclusions: The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Benchmark for automatic noise measurement in clinical computed tomography

et al. 2020

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“…Other studies proposed other methods to improve on the ROI-based method, e.g. , by subtracting two adjacent slices (similar to how digital subtraction angiography works) before calculating either a local (pixel-by-pixel) SD, a regional SD, or multiple regional SDs [ 16 – 19 ]. Such methods are particularly useful in situations where noise does not have a Gaussian distribution, or where pixel value differences exist due to anatomical structures [ 17 , 18 ].…”

Section: Discussionmentioning

confidence: 99%

Improved precision of noise estimation in CT with a volume-based approach

et al. 2021

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Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm2 ROIs and 27 0.75-cm3 VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (−40%); 4.7 versus 9.9 HU for ULD-CT (−53%). Mean systematic bias barely changed: −1.6 versus −0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time.

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“…Prior work has attempted to incorporate image quality measurements on patient CT images in addition to in‐phantom measurements . Several automated algorithms assessing essential image quality metrics on patient CT images and phantom CT images have been demonstrated recently with validated accuracies.…”

Section: Introductionmentioning

confidence: 99%

Validation of algorithmic CT image quality metrics with preferences of radiologists

et al. 2019

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Purpose: Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition protocols. The application of these techniques in clinical operation requires the knowledge of how the output of the computer algorithms corresponds to clinical expectations. This study addressed the need to validate algorithmic image quality measurements on clinical CT images with preferences of radiologists and determine the clinically acceptable range of algorithmic measurements for abdominal CT examinations. Materials and methods: Algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity) were performed on a clinical CT image dataset with supplemental measures of noise power spectrum from phantom images using techniques developed previously. The algorithmic measurements were compared to clinical expectations of image quality in an observer study with seven radiologists. Sets of CT liver images were selected from the dataset where images in the same set varied in terms of one metric at a time. These sets of images were shown via a web interface to one observer at a time. First, the observer rank ordered the CT images in a set according to his/her preference for the varying metric. The observer then selected his/her preferred acceptable range of the metric within the ranked images. The agreement between algorithmic and observer rankings of image quality were investigated and the clinically acceptable image quality in terms of algorithmic measurements were determined. Results: The overall rank-order agreements between algorithmic and observer assessments were 0.90, 0.98, and 1.00 for noise magnitude, liver parenchyma HU, and clarity, respectively. The results indicate a strong agreement between the algorithmic and observer assessments of image quality. Clinically acceptable thresholds (median) of algorithmic metric values were (17.8, 32.6) HU for noise magnitude, (92.1, 131.9) for liver parenchyma HU, and (0.47, 0.52) for clarity. Conclusions: The observer study results indicated that these algorithms can robustly assess the perceptual quality of clinical CT images in an automated fashion. Clinically acceptable ranges of algorithmic measurements were determined. The correspondence of these image quality assessment algorithms to clinical expectations paves the way toward establishing diagnostic reference levels in terms of clinically acceptable perceptual image quality and data-driven optimization of CT image acquisition protocols.

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A method to extract image noise level from patient images in CT

Cited by 36 publications

References 15 publications

A Benchmark for automatic noise measurement in clinical computed tomography

A Benchmark for automatic noise measurement in clinical computed tomography

Improved precision of noise estimation in CT with a volume-based approach

Validation of algorithmic CT image quality metrics with preferences of radiologists

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