Phantom task‐based image quality assessment of three generations of rapid kV‐switching dual‐energy CT systems on virtual monoenergetic images

Greffier, Joël; Viry, Anaïs; Barbotteau, Y.; Frandon, Julien; Loisy, Maeliss; Oliveira, Fabien de; Beregi, Jean Paul; Dabli, Djamel

doi:10.1002/mp.15558

Cited by 19 publications

(17 citation statements)

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“…The mean relative differences (±SDs) between two values were computed for the five dose levels following the same methodology previously published 36 . This allows comparison of the results obtained for each metric between two reconstruction algorithms for all dose levels.…”

Section: Methodsmentioning

confidence: 99%

Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

et al. 2022

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Background Recently, computed tomography (CT) manufacturers have developed deep‐learning‐based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels. Purpose To assess the impact of an artificial intelligence deep‐learning reconstruction (AI‐DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications. Methods Acquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU‐41 phantoms were performed at five dose levels (CTDIvol: 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI‐DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task‐based transfer function, and detectability index (d′) were computed: d′‐modeled detection of a soft tissue mediastinal nodule (low‐contrast soft tissue chest nodule within the mediastinum [LCN]), ground‐glass opacity (GGO), or high‐contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four‐ or five‐point scale. Results From Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: −66.3% ± 0.5% for mediastinal images and −63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: −35.3% ± 2.2% for mediastinal images and −13.3% ± 2.2% for parenchymal images), and the detectability (d′) of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low‐contrast polyethylene insert and the opposite for the high‐contrast air insert. Compared to the i4 used in clinical practice, d′ values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required. Conclusion With AI‐DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

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Section: Methodsmentioning

confidence: 99%

Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

et al. 2022

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“…In an unblinded comparison of images at similar noise magnitude, we confirmed that the DLR images with DE appeared less natural than the DLR images with SE mode. Greffier et al 24 . did not include SE mode measurements so we cannot readily compare this finding to previous results.…”

Section: Discussionmentioning

confidence: 59%

“…To our knowledge, only one group, Greffier et al, 24 have performed similar phantom evaluations for DE imaging and the TF algorithm. Overall, there is good agreement between our findings, with the main difference being larger relative improvements in noise and d' for Greffier 24 and less improvement in d' with decreased keV, possibly due to their larger phantom size. 30 Greffier et al 24 presented approximately (−56%, −8%, −7%, −95%) for (noise, f av , f 50 , d') for DE-70 keV and TF-M (relative to AR0%).…”

Section: F I G U R Ementioning

confidence: 99%

“…[19][20][21][22][23] Since then, the DLR algorithm has become clinically available to use with DE scanning and studies evaluating task-based performance metrics have begun to emerge. 24 The aim of this phantom study was to compare the image quality of a DLR algorithm to that of IR for DE scanning and different mono-energetic reconstructions. A typical phantom size and scan settings for an abdominal examination with iodine contrast media were used.…”

Section: Introductionmentioning

confidence: 99%

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Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual‐energy CT of the abdomen

2023

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Background Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual‐energy CT. Purpose To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual‐energy CT of the abdomen and compare it to single‐energy (SE) CT. Methods An oval 25 cm x 35 cm custom‐made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5 mGy) and two iodine concentrations (8 and 2 mg/mL), using three typical abdominal scan protocols: dual‐energy (DE), SE 80 kV (SE‐80 kV) and SE 120 kV (SE‐120 kV). Reconstructions were performed with three strengths of IR (ASiR‐V: AR0%, AR50%, AR100%) and three strengths of DLR (TrueFidelity: low, medium, high). The DE acquisitions were reconstructed as mono‐energetic images between 40 and 80 keV. The noise power spectrum (NPS), task transfer function (TTF), and detectability index (d’) were determined for the reconstructions following the recommendations of AAPM Task Group 233. Results Noise magnitude reductions (relative to AR0%) for the SE protocols were on average (−29%, −21%) for (AR50%, TF‐M), while for DE‐70 keV were (−28%, −43%). There was less reduction in mean frequency (fav) for DLR than for IR, with similar results for SE and DE imaging. There was, however, a substantial change in the NPS shape when using DE with DLR, quantifiable by a marked reduction in the peak frequency (fpeak) that was absent in SE mode. All protocols and reconstructions (including AR0%) exhibited slight to moderate shifts towards lower spatial frequencies at the lower dose (<12% in fav). Spatial resolution was consistently superior for DLR compared to IR for SE but not for DE. All protocols and reconstructions (including AR0%) showed decreased resolution with reduced dose and iodine concentration, with less decrease for DLR compared to IR. DLR displayed a higher d’ than IR. The effect of energy was large: d’ increased with lower keV, and SE‐80 kV had higher d’ than SE‐120 kV. Using DE with DLR could provide higher d’ than SE‐80 kV at the higher dose but not at lower dose. Conclusions DE imaging with DLR maintained spatial resolution and reduced noise magnitude while displaying less change in noise texture than IR. The d’ was also higher with DLR than IR, suggesting superiority in detectability of iodinated contrast. Despite these trends being consistent with those previously established for SE imaging, there were some noteworthy differences. For DE imaging there was no improvement in resolution compared to IR and a change in noise texture. DE imaging with low...

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“…In addition, deep learning (DL) image reconstruction algorithms were recently developed for conventional SECT [27][28][29][30] and more recently also for DECT platforms [26,31,32]. For the Canon Medical DECT platforms, a DL-based spectral reconstruction (DLSR) is available, based on the creation of deep learning views generated by the trained neural network for opposite and for the same energy views [31,33].…”

Section: Introductionmentioning

confidence: 99%

Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study

et al. 2023

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Background To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform. Methods Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF50) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL. Results The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF50 was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40−60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1. Conclusions The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.

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Phantom task‐based image quality assessment of three generations of rapid kV‐switching dual‐energy CT systems on virtual monoenergetic images

Cited by 19 publications

References 32 publications

Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual‐energy CT of the abdomen

Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study

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