Development of a dual‐energy computed tomography quality control program: Characterization of scanner response and definition of relevant parameters for a fast‐kVp switching dual‐energy computed tomography system

Nute, J; Jacobsen, Megan C.; Stefan, Wolfgang; Wei, Wei; Cody, Dianna D.

doi:10.1002/mp.12812

Cited by 26 publications

(26 citation statements)

References 37 publications

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“…The insert materials are clinically validated standard contrast‐agent and tissue‐mimicking materials. Such insert materials have been used for routine clinical dual‐energy CT quality assurance/performance evaluation and the validation of dual‐energy CT techniques in many previous studies 14,36–38 . As for real inserts, we only used the images of standard iodine inserts with 2, 10, and 15 mgI/cc (with 30 mm diameter) as a part of training set, while the remaining real insert materials (including iodine inserts with other concentrations) were reserved for testing (Table II).…”

Section: Methodsmentioning

confidence: 99%

“…Such insert materials have been used for routine clinical dual-energy CT quality assurance/performance evaluation and the validation of dual-energy CT techniques in many previous studies. 14,[36][37][38] As for real inserts, we only used the images of standard iodine inserts with 2, 10, and 15 mgI/cc (with 30 mm diameter) as a part of training set, while the remaining real insert materials (including iodine inserts with other concentrations) were reserved for testing (Table II). This is because there were relatively more concentrations available for iodine inserts than other insert materials, and we aimed to reserve as many real insert materials for testing as possible.…”

Section: B2 Image Preprocessingmentioning

confidence: 99%

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Deep‐learning‐based direct inversion for material decomposition

et al. 2020

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Purpose To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi‐energy computed tomography (CT) images without performing conventional material decomposition. Methods The proposed CNN (denoted as Incept‐net) followed the general framework of encoder–decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi‐energy CT. Incept‐net was implemented with a customized loss function, including an in‐house‐designed image‐gradient‐correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood–iodine mixture, and fat] were scanned using a research photon‐counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different‐configurations of full field‐of‐view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept‐net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least‐square‐based material decomposition (LS‐MD), total‐variation regularized material decomposition (TV‐MD), and U‐net‐based method. Results Incept‐net improved accuracy of the predicted mass density of basis materials compared with the U‐net, TV‐MD, and LS‐MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept‐net, U‐net, TV‐MD, and LS‐MD, respectively, across all iodine‐present inserts (2.0–24.0 mgI/cc). With the LS‐MD as the baseline, Incept‐net and U‐net achieved comparable noise reduction (both around 95%), both higher than TV‐MD (85%). The proposed IGC regularizer effectively helped both Incept‐net and U‐net to reduce image artifact. Incept‐net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept‐net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept‐net achieved relatively improved performance than the comparator U‐net, indicating that performance gain by Incept‐net was not achieved by simply increasing network learning capacity. Conclusion Incept‐net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept‐net generalized and performed well with unseen image structures and d...

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Section: Methodsmentioning

confidence: 99%

Section: B2 Image Preprocessingmentioning

confidence: 99%

Deep‐learning‐based direct inversion for material decomposition

et al. 2020

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“…(c); Gammex Inc.]. This phantom contained 15 insert rods simulating a variety of materials and concentrations . Identical axial body protocols were used to quantify differences in CT number and noise magnitude.…”

Section: Methodsmentioning

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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“…In selecting the Preset Families to include in a profile, consider the following: It has been observed in the GE Discovery scanners that material decomposition values are more accurate for low‐current, long rotation time combinations than at high‐current, short rotation time combinations . This is likely because for short rotation times, the time spent switching between tube voltages is a higher fraction of the total rotation time, reducing the accuracy of the acquired data.…”

Section: Methodsmentioning

confidence: 99%