Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest <scp>CT</scp> images

Abadi, Ehsan; Sanders, Jeremiah; Samei, Ehsan

doi:10.1002/mp.12438

Cited by 39 publications

(23 citation statements)

References 37 publications

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“…In summary, algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity – a new metric of normalized detectability) were performed on a clinical CT image dataset using techniques which were developed previously . The algorithmic measurements were compared to clinical expectations of image quality by radiologists in an observer study.…”

Section: Methodsmentioning

confidence: 99%

“…The automated algorithm for measuring organ Hounsfield Units (HU) values was adapted in this study for measuring liver parenchyma HU and aorta HU. Algorithmic aorta HU measurements can help to identify those CT series that are likely to be acquired during undesired phase of contrast‐enhanced CT.…”

Section: Methodsmentioning

confidence: 99%

“…Several automated algorithms assessing essential image quality metrics on patient CT images and phantom CT images have been demonstrated recently with validated accuracies. These algorithmic image quality metrics include organ Hounsfield Unit (HU), noise magnitude, spatial resolution, noise texture, and detectability index . Automated assessment of image quality on clinical CT images by computer algorithms has the potential to greatly facilitate data‐driven monitoring and optimization of CT image acquisition protocols.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of algorithmic CT image quality metrics with preferences of radiologists

et al. 2019

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“…In another way, the ATCM might produce a consistent image quality of a patient either by increasing the radiation exposure for a large-sized patient or limiting the exposure for a small patient. Thus, the radiologists' concern about producing acceptable images for diagnoses by the ATCM system has been largely solved [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Establishment of CTPA Local Diagnostic Reference Levels with Noise Magnitude as a Quality Indicator in a Tertiary Care Hospital

et al. 2020

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This study aimed to establish the local diagnostic reference levels (LDRLs) of computed tomography pulmonary angiography (CTPA) examinations based on body size with regard to noise magnitude as a quality indicator. The records of 127 patients (55 males and 72 females) who had undergone CTPAs using a 128-slice CT scanner were retrieved. The dose information, scanning acquisition parameters, and patient demographics were recorded in standardized forms. The body size of patients was categorized into three groups based on their anteroposterior body length: P1 (14–19 cm), P2 (19–24 cm), and P3 (24–31 cm), and the radiation dose exposure was statistically compared. The image noise was determined quantitatively by measuring the standard deviation of the region of interest (ROI) at five different arteries—the ascending and descending aorta, pulmonary trunk, and the left and right main pulmonary arteries. We observed that the LDRL values were significantly different between body sizes (p < 0.05), and the median values of the CT dose index volume (CTDIvol) for P1, P2, and P3 were 6.13, 8.3, and 21.40 mGy, respectively. It was noted that the noise reference values were 23.78, 24.26, and 23.97 HU for P1, P2, and P3, respectively, which were not significantly different from each other (p > 0.05). The CTDIvol of 9 mGy and dose length product (DLP) of 329 mGy∙cm in this study were lower than those reported by other studies conducted elsewhere. This study successfully established the LDRLs of a local healthcare institution with the inclusion of the noise magnitude, which is comparable with other established references.

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“…Prior work has attempted to overcome some of the limitations of the aforementioned methods by measuring noise, contrast, and resolution in individual patient images (i.e., in vivo). [16][17][18] This approach offers patient-specific characterization of image quality, which further facilitates patent-based quality monitoring. However, the attributes of noise, contrast, or resolution by themselves provide isolated depictions of image quality, not capturing the overall attribute of an image to depict a potential abnormality.…”

Section: Introductionmentioning

confidence: 99%

Estimating detectability index in vivo: development and validation of an automated methodology

2017

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This study's purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients' CT images (i.e., ). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient's CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with [Formula: see text] HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model. This method was applied to CT data from a previous study in which diagnostic performance of 16 readers was measured for the task of detecting subtle, hypoattenuating liver lesions ([Formula: see text]), using a two-alternative-forced-choice (2AFC) method, over six dose levels and two reconstruction algorithms. detectability indices were estimated and compared to the human readers' binary 2AFC outcomes using a generalized linear mixed-effects statistical model. The results of this modeling showed that the detectability indices were strongly related to 2AFC outcomes ([Formula: see text]). Linear comparison between human-detection accuracy and model-predicted detection accuracy (for like conditions) resulted in Pearson and Spearman correlation coefficients exceeding 0.84. These results suggest the potential utility of using estimates of a detectability index for an automated image quality tracking system that could be implemented clinically.

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Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

Cited by 39 publications

References 37 publications

Validation of algorithmic CT image quality metrics with preferences of radiologists

Validation of algorithmic CT image quality metrics with preferences of radiologists

Establishment of CTPA Local Diagnostic Reference Levels with Noise Magnitude as a Quality Indicator in a Tertiary Care Hospital

Estimating detectability index in vivo: development and validation of an automated methodology

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