Quantitation of clinical feedback on image quality differences between two <scp>CT</scp> scanner models

Bache, S; Stauduhar, Paul J.; Liu, Xinming; Loyer, Evelyne M.; John, R.

doi:10.1002/acm2.12050

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…13 The statistical LCD measurement feature sometimes exists within CT scanners in the form of built-in tools, but it is limited to particular vendors through the operator panel. 23 In addition, the built-in software from certain vendors can only be used for particular phantoms.…”

Section: Introductionmentioning

confidence: 99%

Automated development of the contrast–detail curve based on statistical low‐contrast detectability in CT images

Anam

Naufal

Matsubara

et al. 2022

J Applied Clin Med Phys

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We have developed a software to automatically find the contrastdetail (C-D) curve based on the statistical low-contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics. Methods: A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2-pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C-D curve was obtained and the target size at an MDC of 0.6% (i.e., 6-HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer. Results: The developed system was able to measure C-D curves from different phantoms and scanners. We found that the C-D curves follow a power-law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low-contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD. Conclusions: The statistical LCD measurement method can generate a C-D curve automatically, quickly, and objectively.

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Section: Introductionmentioning

confidence: 99%

Automated development of the contrast–detail curve based on statistical low‐contrast detectability in CT images

Anam

Naufal

Matsubara

et al. 2022

J Applied Clin Med Phys

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“…For example, a normal pixel sound for soft tissue is 62.1 HU, while, for standard and high-goal parts, they are 31.5 and 57.5 HU, respectively, for a 1- and 32-mm section thickness. The sound level of CT images is a significant factor, compared to the location of LCD objects [ 1 , 24 ]. The outcomes do not show any noteworthy changes in the CNR values among other object estimates, down to the smallest measurable object (5 mm).…”

Section: Discussionmentioning

confidence: 99%

“…The outcomes do not show any noteworthy changes in the CNR values among other object estimates, down to the smallest measurable object (5 mm). It was observed that the ability to identify objects depends on the differentiation standard of the object, as well as its size [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Step-by-step instructions to control undefined, minor lesions (e.g., nodules in CT lung screening) have become a significant concern.…”

Section: Discussionmentioning

confidence: 99%

The Feasibility of Contrast-to-Noise Ratio on Measurements to Evaluate CT Image Quality in Terms of Low-Contrast Detailed Detectability

Alsleem

Almohiy

2020

Medical Sciences

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Background: To evaluate contrast-to-noise ratio (CNR) measurements in assessing image quality, in the context of the detectability performance of low-contrast detail (LCD), in computed tomography (CT) images, since exposure to elevated ionising-type radiation is considered to present excessive carcinogenic risk, whilst also causing distress in study subjects. Methods: An LCD phantom module (CTP515) was utilised in the study. Three dissimilar contrast items were used to analyse the ramifications of the proportions of an object on the CNR. Three multidetector CT (MDCT) scanners were used, with 16-MDCT, 64-MDCT and 80-MDCT frameworks, respectively. The CT scans were recreated using three dissimilar remaking algorithms—soft, standard and lung. The effects exerted on the CNR by various remodelling algorithms, as well as the contrast of various objects along with the size of the objects, were explored. The Hounsfield units of each chosen object (one unit representing the outer portion of the object) and the background and the standard deviation of the noise parameter were quantified, and algorithms were developed using MATLAB. Results: The CNR information was greatly influenced by changing the image recreation calculations and was very much increased in the soft-tissue recreation images using 16-MDCT and 64-MDCT. The CNR information was also increased more in the optimum recreation images than in the reproduced images from the computational procedure used in the 80-MDCT. The results did not show any remarkable contrasts in the CNR values between the different object sizes. Overall, a higher kVp produced an improved CNR in all the CT scanners. In particular, there were prominent upgrades in the CNR information when the kVp was increased from 80 to 120. Higher mAs levels gave better CNR values overall, especially for greater section thicknesses. Based on the CNR estimations, the 64-MDCT provided the best correlation among the CT scanners. Conclusions: The objective LCD appraisal method, based on CNR measurements, was confirmed as being useful for checking the different impacts of kVp, mAs and section thickness on the nature of the picture. This procedure was similarly viable in assessing the impacts of the different reconstruction calculations and the different differentiation questions on the nature of the image.

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“…Increased image noise impeded clinical interpretation of post‐contrast CT scans from an updated CT scanner that was recently added to a fleet of existing scanners from the same vendor. The goals of this work were to quantify and standardize image quality on the new and an existing scanner using phantom images and determine why this issue was not discovered upon acceptance testing, which used conventional techniques employing water or water‐equivalent uniform sections of phantom to quantify image noise …”

Section: Discussionmentioning

confidence: 99%

“…These advancements create challenges in harmonizing image quality and appearance from a large fleet of clinical scanners. Several groups, however, have quantified and homogenized image appearance on similar scanner models using uniform sections of water or water‐equivalent materials . At our institution, feedback from radiologists indicated that differences in image appearance and noise on a new CT scanner we recently added to our fleet impeded reading of post‐contrast scans (Fig.…”

Section: Introductionmentioning

confidence: 99%

Quantification and homogenization of image noise between two CT scanner models

Einstein

Rong

Jensen

et al. 2019

J Applied Clin Med Phys

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Feedback from radiologists indicated that differences in image appearance and noise impeded reading of post-contrast computed tomography (CT) scans from an updated CT scanner that was recently added to a fleet of existing scanners from the same vendor, despite using identically named reconstruction algorithms. The goals of this work were to quantify and possibly standardize image quality on the new and an existing scanner using phantom images. Three months of daily quality control images were analyzed to determine the mean CT number and noise magnitude in a water phantom. Next, subtraction images from the uniformity section of an American College of Radiology CT phantom were used to generate noise power spectra for both scanners. Then, a semi-anthropomorphic liver phantom was imaged with both scanners in triplicate using identical body protocols to quantify differences CT number and noise magnitude. Finally, the scanner dependence of CT number and noise magnitude on material attenuation was quantified using a multi-energy CT phantom with 15 material inserts. Significant differences between scanners were determined using a paired or Welch's t test as appropriate. In daily quality control images, the new scanner exhibited slightly higher CT number (0.697 vs. 0.412, P < 0.001, n = 85) and slightly lower noise magnitude (4.85 vs. 4.94, P < 0.001, n = 85). Measured NPS was not significantly different between the existing and new scanners. Interestingly, it was observed that the noise magnitude from the new scanner increased with increasing material attenuation in both the liver (P = 0.008) and multi-energy (P < 0.001) phantoms. Using an alternate reconstruction algorithm with the new scanner eliminated this deviation at high material attenuations. While standard noise evaluation in a water phantom was unable to discern differences between the scanners, more comprehensive testing with higher attenuation materials allowed for the characterization and homogenization of image quality. K E Y W O R D S computed tomography, image noise, image quality, noise power spectrum ---

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Quantitation of clinical feedback on image quality differences between two CT scanner models

Cited by 5 publications

References 7 publications

Automated development of the contrast–detail curve based on statistical low‐contrast detectability in CT images

Automated development of the contrast–detail curve based on statistical low‐contrast detectability in CT images

The Feasibility of Contrast-to-Noise Ratio on Measurements to Evaluate CT Image Quality in Terms of Low-Contrast Detailed Detectability

Quantification and homogenization of image noise between two CT scanner models

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