Applications of dual energy computed tomography in abdominal imaging

Lestra, T.; Mulé, Sebastien; Millet, Ingrid; Carsin-Vu, Aline; Taourel, Patrice; Hoeffel, Christine

doi:10.1016/j.diii.2015.11.018

Cited by 35 publications

(25 citation statements)

References 42 publications

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“…Although the past study reported that the iodine map improved the detection of these lesions in the pancreatic cancer, it could not eliminate the bone region. 8 In the current study, we investigated the correlation between the rED and CT number, and we created the contrast medium extraction method using rED using sinogram data and CT number from DECT. Figure 1 shows the two phantoms used in this study.…”

Section: Discussionmentioning

confidence: 99%

Automatic contrast medium extraction system using electron density data with dual-energy CT

Kawahara

Ozawa

Tanaka

et al. 2018

BJR

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Section: Discussionmentioning

confidence: 99%

Automatic contrast medium extraction system using electron density data with dual-energy CT

Kawahara

Ozawa

Tanaka

et al. 2018

BJR

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“…Recently, dual-energy CT imaging has become available to reduce the beam hardening effect contributing to the artifacts caused by the contrast agent [11–13]. Approaches such as image acquisition with different beam energy or using energy-resolved photon-counting detectors are used to perform the dual-energy CT imaging.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network for reduction of contrast-enhanced region on CT images

Sumida

Magome

Kitamori

et al. 2019

Journal of Radiation Research

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This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The transverse images were divided into 64 × 64 pixels. This resulted in 14 723 patches in total for both non-contrast and contrast-enhanced CT image pairs. The proposed CNN model comprises five two-dimensional (2D) convolution layers with one shortcut path. For comparison, the U-net model, which comprises five 2D convolution layers interleaved with pooling and unpooling layers, was used. Training was performed in 24 patients and, for testing of trained models, another 5 patients were used. For quantitative evaluation, 50 regions of interest (ROIs) were selected on the reference contrast-enhanced image of the test data, and the mean pixel value of the ROIs was calculated. The mean pixel values of the ROIs at the same location on the reference non-contrast image and the predicted non-contrast image were calculated and those values were compared. Regarding the quantitative analysis, the difference in mean pixel value between the reference contrast-enhanced image and the predicted non-contrast image was significant (P < 0.0001) for both models. Significant differences in pixels (P < 0.0001) were found using the U-net model; in contrast, there was no significant difference using the proposed CNN model when comparing the reference non-contrast images and the predicted non-contrast images. Using the proposed CNN model, the contrast-enhanced region was satisfactorily reduced.

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“…Iodine mapping is among the most frequently reported clinical applications of dual energy computed tomography (DECT) [ 1 – 6 ]. Iodine maps display the local concentration of iodine from iodinated contrast agent, and can serve as an indicator of local perfusion.…”

Section: Introductionmentioning

confidence: 99%

Comparing dual energy CT and subtraction CT on a phantom: which one provides the best contrast in iodine maps for sub-centimetre details?

et al. 2018

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ObjectivesTo compare contrast-to-noise ratios (CNRs) and iodine discrimination thresholds on iodine maps derived from dual energy CT (DECT) and subtraction CT (SCT).MethodsA contrast-detail phantom experiment was performed with 2 to 15 mm diameter tubes containing water or iodinated contrast concentrations ranging from 0.5 mg/mL to 20 mg/mL. DECT scans were acquired at 100 kVp and at 140 kVp+Sn filtration. SCT scans were acquired at 100 kVp. Iodine maps were created by material decomposition (DECT) or by subtraction of water scans from iodine scans (SCT). Matched exposure levels varied from 8 to 15 mGy. Iodine discrimination thresholds (Cr) and response times were determined by eight observers.ResultsThe adjusted mean CNR was 1.9 times higher for SCT than for DECT. Exposure level had no effect on CNR. All observers discriminated all details ≥10 mm at 12 and 15 mGy. For sub-centimetre details, the lowest calculated Cr was ≤ 0.50 mg/mL for SCT and 0.64 mg/mL for DECT. The smallest detail was discriminated at ≥4.4 mg/mL with SCT and at ≥7.4 mg/mL with DECT. Response times were lower for SCT than DECT.ConclusionsSCT results in higher CNR and reduced iodine discrimination thresholds compared to DECT for sub-centimetre details.Key Points • Subtraction CT iodine maps exhibit higher CNR than dual-energy iodine maps • Lower iodine concentrations can be discriminated for sub-cm details with SCT • Response times are lower using SCT compared to dual-energy CT Electronic supplementary materialThe online version of this article (10.1007/s00330-018-5496-x) contains supplementary material, which is available to authorized users.

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Applications of dual energy computed tomography in abdominal imaging

Cited by 35 publications

References 42 publications

Automatic contrast medium extraction system using electron density data with dual-energy CT

Automatic contrast medium extraction system using electron density data with dual-energy CT

Deep convolutional neural network for reduction of contrast-enhanced region on CT images

Comparing dual energy CT and subtraction CT on a phantom: which one provides the best contrast in iodine maps for sub-centimetre details?

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