A Benchmark for automatic noise measurement in clinical computed tomography

Ahmad, Moiz; Jacobsen, Megan C.; Thomas, Matthew A.; Chen, Henry; Layman, Rick R.; Jones, A. Kyle

doi:10.1002/mp.14635

Cited by 11 publications

(16 citation statements)

References 13 publications

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“…First, it is well known that standard deviations within ROIs incompletely reflect the degree and quality of image noise in a CT dataset and contain no information about its frequency distribution [10]. Nevertheless, this parameter is frequently chosen in image quality studies on clinical CT datasets and some excellent recent work has emphasized the close relationship between this simple parameter and more comprehensive measures of image noise for the types of image noise typically encountered in clinical CT scans [11,12]. Second, the mice in our study were scanned without any other attenuating objects in the gantry-except for X-ray transparent casing and the patient table.…”

Section: Discussionmentioning

confidence: 99%

Visualization of bone details in a novel photon-counting dual-source CT scanner—comparison with energy-integrating CT

et al. 2021

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Objectives Photon-counting detector CT (PCD-CT) promises a leap in spatial resolution due to smaller detector pixel sizes than implemented in energy-integrating detector CTs (EID-CT). Our objective was to compare the visualization of smallest bone details between PCD-CT and EID-CT using a mouse as a specimen. Materials and methods Two euthanized mice were scanned at a 20-slice EID-CT and a dual-source PCD-CT in single-pixel mode at various CTDIVol values. Image noise and signal-to-noise ratio (SNR) were evaluated using repeated ROI measurements. Edge sharpness of bones was compared by the maximal slope within CT value plots along sampling lines intersecting predefined bones of the spine. Two readers evaluated bone detail visualization at four regions of the spine on a three-point Likert scale at various CTDIVol’s. Two radiologists selected the series with better detail visualization among each of 20 SNR-matched pairs of EID-CT and PCD-CT series. Results In CTDIVol-matched scans, PCD-CT series showed significantly lower image noise (NoiseCTDI=5 mGy: 16.27 ± 1.39 vs. 23.46 ± 0.96 HU, p < 0.01), higher SNR (SNRCTDI=5 mGy: 20.57 ± 1.89 vs. 14.00 ± 0.66, p < 0.01), and higher edge sharpness (Edge Slopelumbar spine: 981 ± 160 vs. 608 ± 146 HU/mm, p < 0.01) than EID-CT series. Two radiologists considered the delineation of bone details as feasible at consistently lower CTDIVol values at PCD-CT than at EID-CT. In comparison of SNR-matched reconstructions, PCD-CT series were still considered superior in almost all cases. Conclusions In this head-to-head comparison, PCD-CT showed superior objective and subjective image quality characteristics over EID-CT for the delineation of tiniest bone details. Even in SNR-matched pairs (acquired at different CTDIVol’s), PCD-CT was strongly preferred by radiologists. Key Points • In dose-matched scans, photon-counting detector CT series showed significantly less image noise, higher signal-to-noise ratio, and higher edge sharpness than energy-integrating detector CT series. • Human observers considered the delineation of tiny bone details as feasible at much lower dose levels in photon-counting detector CT than in energy-integrating detector CT. • In direct comparison of series matched for signal-to-noise ratio, photon-counting detector CT series were considered superior in almost all cases.

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Section: Discussionmentioning

confidence: 99%

Visualization of bone details in a novel photon-counting dual-source CT scanner—comparison with energy-integrating CT

et al. 2021

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“…Whether phantom‐based image quality tests of deep learning reconstruction are even relevant is an open question. A broader discussion of potential applications in quality control and standardization enabled by the automatic noise measurement is presented elsewhere 14 …”

Section: Discussionmentioning

confidence: 99%

“…The GN algorithm accuracy found in this study is more or less consistent with previous studies: Ahmad et al reported a percent RMS error of 8.6% in the validation study of the GN algorithm applied to abdomen CT examinations. 14 Christianson et al found a percent F I G U R E 4 Scatterplot of GN versus head size…”

Section: Discussionmentioning

confidence: 99%

“…The soft tissue mask lower threshold was fixed at 0 HU. This optimization search space was informed by results of the previous GN algorithm validation study as an optimal range 14 …”

Section: Methodsmentioning

confidence: 99%

“…A broader discussion of potential applications in quality control and standardization enabled by the automatic noise measurement is presented elsewhere. 14 The limitations of this study are as follows. The reference noise values obtained in this study are not strictly ground truth values, even though they were acquired with high precision.…”

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Assessment of the global noise algorithm for automatic noise measurement in head CT examinations

2021

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The global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. Accuracy of the GN algorithm has been assessed in abdomen CT exams, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise 15 measurement in head CT exams.Methods: A publicly available image dataset of 99 head CT exams was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values.Reference noise values were acquired using a manual noise measurement procedure.The procedure used a consistent instruction protocol and multiple observers to miti-20 gate the influence of intra-and inter-observer variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the 6 different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear 25 regression model, and the CT scanner's x-ray beam quality was inferred from the model fit parameters.Results: Across all head CT exams in the dataset, the range of reference noise was 2.9 -10.2 HU. A precision of ±0.33 HU was achieved in the reference noise measurements. After optimization, then GN algorithm had a RMS error 0.34 HU 30 corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p < 10 −7 ). The CT scanner x-ray beam quality estimated from the slope parameter was 3.5 cm water 35 HVL (2.8-4.8 cm 95% C.I.). Conclusion:The GN algorithm was validated for application in head CT exams.The GN algorithm was accurate in comparison to reference manual measurement, with errors comparable to inter-observer variation in manual measurement. The GN algorithm can detect noise differences in exams performed on different scanner mod-40 els or using different scan protocols. The trend of GN across patients of different head sizes closely follows that predicted by a physical model of x-ray attenuation.

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Automatic vessel attenuation measurement for quality control of contrast‐enhanced CT: Validation on the portal vein

McCoy,

Marisetty,

Tan

et al. 2024

Medical Physics

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BackgroundAdequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast‐enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.PurposeThe purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast‐enhanced abdomen CT examinations.MethodsInput CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast‐enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations.ResultsThe best classifier was found to be a random forest, with a precision of 0.892 in the held‐out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU.ConclusionsThe method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time‐consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast‐enhanced CT examinations.

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A Benchmark for automatic noise measurement in clinical computed tomography

Cited by 11 publications

References 13 publications

Visualization of bone details in a novel photon-counting dual-source CT scanner—comparison with energy-integrating CT

Visualization of bone details in a novel photon-counting dual-source CT scanner—comparison with energy-integrating CT

Assessment of the global noise algorithm for automatic noise measurement in head CT examinations

Automatic vessel attenuation measurement for quality control of contrast‐enhanced CT: Validation on the portal vein

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