Dual-Energy CT: Applications in Abdominal Imaging

Improved GAN: Using a transformer module generator approach for material decomposition

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

Quantification of contrast agent materials using a new image- domain multi material decomposition algorithm based on dual energy CT

OBJECTIVE

Dual-Energy CT (DECT) is an imaging modality in which the objects are scanned by two different energy spectra. Using these two measurements, two type of materials can be separated and density image pairs can be generated as well. Decomposing more than two materials is necessary in both clinical and industrial CT applications.

METHODS

In our MMD, barycentric coordinates were chosen using an innovative local clustering method. Local clustering increases precision in the barycentric coordinates assignment by decreasing search domain. Therefore the algorithm can be run in parallel. For optimizing coordinates selection, a fast bi-directional Hausdorff distance measurement is used. To deal with the significant obstacle of noise, we used Doubly Local Wiener Filter Directional Window (DLWFDW) algorithm.

RESULTS

Briefly, the proposed algorithm separates blood and fat ROIs with errors of less than 2 and 9 % respectively on the clinical images. Also, the ability to decompose different materials with different concentrations is evaluated employing the phantom data. The highest accuracy obtained in separating different materials with different concentrations was 93 % (for calcium plaque) and 97.1 % (for iodine contrast agent) respectively. The obtained results discussed in detail in the following results section.

CONCLUSION

In this study, we propose a new material decomposition algorithm. It improves the MMD work flow by employing tools which are easy to implement. Furthermore, in this study, an effort has been made to turn the MMD algorithm into a semi-automatic algorithm by employing clustering concept in material coordinate's assignment. The performance of the proposed method is comparable to existing methods from qualitative and quantitative aspects.

ADVANCES IN KNOWLEDGE

All decomposition methods have their own specific problems. Image- domain decomposition also has barriers and problems, including the need for a predetermined table for the separation of different materials with specified coordinates. In the present study, it attempts to solve this problem by using clustering methods and relying on the intervals between different materials in the attenuation domain.

Advanced abdominal imaging with dual energy CT is feasible without increasing radiation dose

Background

Dual energy CT (DECT) has proven its potential in oncological imaging. Considering the repeated follow-up examinations, radiation dose should not exceed conventional single energy CT (SECT). Comparison studies on the same scanner with a large number of patients, considering patient geometries and image quality, and exploiting full potential of SECT dose reduction are rare. Purpose of this retrospective study was to compare dose of dual source DECT versus dose-optimized SECT abdominal imaging in clinical routine.

Methods

One hundred patients (62y (±14)) had either contrast-enhanced SECT including automatic voltage control (44) or DECT (56). CT dose index (CTDIvol), size-specific dose-estimate (SSDE) and dose-length product (DLP) were reported. Image noise (SD) was recorded as mean of three ROIs placed in subcutaneous fat and normalized to dose by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ SDn=SD\times \sqrt{CDTIvol} $$\end{document}SDn=SD×CDTIvol. For dose-normalized contrast-to-noise ratio (CNRD), mean attenuation of psoas muscle (CT_muscle) and subcutaneous fat (CT_fat) were compared by CNRD = (CTmuscle − CTfat)/SDn. Statistical significance was tested with two-sided t-test (α = 0.05).

Results

There was no significant difference (p < 0.05) between DECT and SECT: Mean CTDIvol was 14.2 mGy (±3.9) (DECT) and 14.3 mGy (±4.5) (SECT). Mean DLP was 680 mGy*cm (±220) (DECT) and 665 mGy*cm (±231) (SECT). Mean SSDE was 15.7 mGy (±1.9) (DECT) and 16.1 mGy (±2.5) (SECT). Mean SDn was 42.2 (±13.9) HU \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ *\sqrt{\mathrm{mGy}} $$\end{document}*mGy (DECT) and 47.8 (±14.9) HU \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ *\sqrt{\mathrm{mGy}} $$\end{document}*mGy (SECT). Mean CNRD was 3.9 (±1.3) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{mGy}}^{-\frac{1}{2}} $$\end{document}mGy−12. (DECT) and 4.0 (±1.3) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{mGy}}^{-\frac{1}{2}} $$\end{document}mGy−12 (SECT).

Conclusion

Abdominal DECT is feasible without increasing radiation dose or deteriorating image quality, even compared to dose-optimized SECT including automatic voltage control. Thus DECT can contribute to sophisticated oncological imaging without dose penalty.