Dual-energy CT Imaging Using a Single-energy CT Data via Deep Learning: A Contrast-enhanced CT Study

Zhao, Wei; Lv, Teng; Chen, Y.; Liu, Xing

doi:10.1016/j.ijrobp.2020.07.2154

Cited by 8 publications

(11 citation statements)

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“…DL-based image to image translation to infer DECT image types: The feasibility of generating synth-DECT image types from SECT scan data using DL-based methods is reported throughout the literature [12][13][14][15][16][18][19][20][22][23][24][25][26][27]. These studies demonstrate how DL-based image translation methods can create synth-DECT scans for clinical interpretation.…”

Section: Related Workmentioning

confidence: 84%

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

et al. 2022

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We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans. The trained P2P algorithm then transformed 140 public SECT scans to synth-DECT scans. We split 131 scans into 60% train, 20% tune, and 20% held-out test to train four existing liver segmentation frameworks. The remaining nine low-dose SECT scans tested system generalization. Segmentation accuracy was measured with the dice coefficient (DSC). The DSC per slice was computed to identify sources of error. With synth-DECT (and SECT) scans, an average DSC score of 0.93±0.06 (0.89±0.01) and 0.89±0.01 (0.81±0.02) was achieved on the held-out and generalization test sets. Synth-DECT-trained systems required less data to perform as well as SECT-trained systems. Low DSC scores were primarily observed around the scan margin or due to non-liver tissue or distortions within ground-truth annotations. In general, training with synth-DECT scans resulted in improved segmentation performance with less data.

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Section: Related Workmentioning

confidence: 84%

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

et al. 2022

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“…Zhao et al developed a deep learning model to map low‐ to high‐energy images using a two‐stage CNN. They evaluated the virtual noncontrast (VNC) imaging reconstructed by DECT from the kV‐CT image scanned using single‐energy CT (SECT) 15 . The difference in the monoenergetic CT numbers between the predicted and original high‐energy CT images was below 4.0 HU in the abdominal region.…”

Section: Discussionmentioning

confidence: 99%

“…Here, MAX and MSE are the possible maximum signal intensity and the mean square error (or difference) of the image, respectively. The MI is used as a cross‐modality similarity measure 15 and is calculated as follows:

I (r : t) = \underset{m \in I_{r}}{false\sum} \underset{n \in I_{t}}{false\sum} p (m, n) l o g (\frac{p ()(, m, n)}{p ()(, m) p ()(, n)}) .

…”

Section: Methodsmentioning

confidence: 99%

Image synthesis of monoenergetic CT image in dual‐energy CT using kilovoltage CT with deep convolutional generative adversarial networks

Kawahara

Ozawa

Kimura

et al. 2021

J Applied Clin Med Phys

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Purpose To synthesize a dual‐energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV‐CT) image using a deep convolutional adversarial network. Methods A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kV‐CT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV‐CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). Results The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. Conclusions The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single‐energy CT images.

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“…To reduce such data acquisition burden in data‐domain dual‐energy x‐ray imaging, deep‐learning‐based methods have been actively studied. Researchers have proposed deep neural networks using a single‐energy CT image to synthesize another CT image at a different spectrum 25–27 or estimate material maps directly 28,29 . Deep neural networks estimating bone and soft‐tissue images from a single x‐ray image have also been proposed in chest x‐ray radiography 30,31 …”

Section: Introductionmentioning

confidence: 99%

A feasibility study on deep‐neural‐network‐based dose‐neutral dual‐energy digital breast tomosynthesis

Kim

Lee

et al. 2022

Medical Physics

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Background: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. Purpose: As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. Methods: The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. Results: The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. Conclusions: The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts. K E Y W O R D Sdeep learning, digital breast tomosynthesis, dual-energy x-ray imaging, image reconstruction, material decomposition Hyeongseok Kim and Hoyeon Lee contributed equally to this work.

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Dual-energy CT Imaging Using a Single-energy CT Data via Deep Learning: A Contrast-enhanced CT Study

Cited by 8 publications

References 0 publications

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

Image synthesis of monoenergetic CT image in dual‐energy CT using kilovoltage CT with deep convolutional generative adversarial networks

A feasibility study on deep‐neural‐network‐based dose‐neutral dual‐energy digital breast tomosynthesis

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