Dual-Energy CT Images: Pearls and Pitfalls

Parakh, Anushri; Lennartz, Simon; An, Chansik; Rajiah, Prabhakar; Yeh, Benjamin M.; Simeone, F. A.; Sahani, Dushyant V.; Kambadakone, Avinash

doi:10.1148/rg.2021200102

Cited by 80 publications

(56 citation statements)

References 72 publications

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“…The single-center study published in this issue, “Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis, in Obese Patients with Deep Learning Image Reconstruction” by Fair et al, evaluated a single-vendor deep learning (DL) image reconstruction technique in a group of obese patients (body mass index ≥ 30) who underwent dual-energy computed tomography (CT) of the chest, abdomen, and pelvis. The authors are commended for investigating this important topic given prior reports detailing limitations that can occur with dual energy because of image noise 1 . In agreement with studies using single energy, the authors found that DL improved reader perceived image quality and figures of merit (eg, contrast-to-noise ratio) when compared with a hybrid iterative reconstruction.…”

supporting

confidence: 55%

Commentary On: Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction

Jensen

2022

J Comput Assist Tomogr

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T he single-center study published in this issue, "Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis, in Obese Patients with Deep Learning Image Reconstruction" by Fair et al., evaluated a single-vendor deep learning (DL) image reconstruction technique in a group of obese patients (body mass index ≥ 30) who underwent dual-energy computed tomography (CT) of the chest, abdomen, and pelvis. The authors are commended for investigating this important topic given prior reports detailing limitations that can occur with dual energy because of image noise. 1 In agreement with studies using single energy, the authors found that DL improved reader perceived image quality and figures of merit (eg, contrast-to-noise ratio) when compared with a hybrid iterative reconstruction. While a prior study mentioned subtle liver lesion and small vessel blurring with DL 2 when compared with Adaptive Statistical Iterative Reconstruction V (ASIR-V) 30%, Fair et al indicated improved sharpness with DL compared with ASIR-V 40% to 70%; this discordance is at least partly related to differences in ASIR-V % usage between the 2 studies, but other confounding factors exist between the studies (eg, participant body habitus, radiation doses, single vs dual energy). Increasing evidence for DL indicates overall improvement compared with filtered back projection and iterative reconstructions. 3 However, a recent DL liver lesion study noted that caution was still needed when evaluating potential low-contrast liver lesions; this study demonstrated that DL did not maintain observer performance for small colorectal hepatic metastases (≤5 mm) at 65% radiation dose reduction. 4 Although phantom studies demonstrate dramatic noise magnitude reduction, maintained or improved spatial resolution, and improved detectability with DL, some undesirable leftward shift of the noise power spectrum still occurs with DL. 5,6 Further task-specific studies will be necessary to determine whether perhaps this mild blurring on singleenergy technique is less apparent with DL on dual energy and how various dose levels and patient sizes may influence this effect.These last 2 points remind us of 2 issues that need to be addressed in CT imaging: reliance on intensive clinical trials and limited radiation dose index registries. To adequately answer task-specific questions regarding image quality in vivo, it requires a slow and resource-intensive process of clinical trials, which typically result in addressing just a limited part of the clinical question because of inherent limitation in resources, both financial and workforce related (eg, only a few dose levels, select reconstructions, and limited lesion types are evaluated). One potential solution may be the development of virtual trials where stored image data (virtual patients) can be virtually imaged under different dose and reconstruction conditions with resulting virtual interpretation by the system. 7 Virtual trials not only present potential time and cost savings they will also allow for many more ...

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confidence: 55%

Commentary On: Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction

Jensen

2022

J Comput Assist Tomogr

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“…The intra-scanner reproducibility analysis indicated that SECT 120-kVp images and DECT 120 kVp-like VMIs were far from alike from the radiomics features point of view. The images generated from various DECT scanners differed from those from conventional SECT because of differences in their acquisition techniques, material decomposition methods, image reconstruction algorithms, and postprocessing methods [ 25 ]. Although SECT-like images were generated in DECT to mimic the SECT images, the intra-scanner reproducibility of radiomics features was low between SECT images and corresponding SECT-like images in DECT.…”

Section: Discussionmentioning

confidence: 99%

“…The best energy level for VMI reconstruction to match the SECT image differs among vendors. Therefore, corresponding DECT images have different imaging appearances, texture features, and quantitative capabilities [ 25 ]. Further, different technical approaches to realize DECT, namely dual-source DECT, dual-layer detector DECT, and rapid kV-switching DECT, might potentially be unique sources of variability in our study [ 11 , 25 ], resulting in low inter-scanner reproducibility of radiomics features.…”

Section: Discussionmentioning

confidence: 99%

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT

et al. 2022

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Objectives To evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT). Methods A standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Results The test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively. Conclusions The majority of radiomics features were non-reproducible within and between SECT and DECT. Key Points • Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process.

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“…Raw data-based analysis elicits lower beam-hardening effects and fewer artifacts related to the CT reconstruction kernel [14][15][16]. This results in more accurate CT number measurements in the scanned object.…”

Section: Advantages Of Raw Data-over Image-based Analysismentioning

confidence: 99%

Dual-energy CT: minimal essentials for radiologists

et al. 2022

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Dual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.

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Dual-Energy CT Images: Pearls and Pitfalls

Cited by 80 publications

References 72 publications

Commentary On: Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction

Commentary On: Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT

Dual-energy CT: minimal essentials for radiologists

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