Leveraging deep generative model for direct energy-resolving CT imaging via existing energy-integrating CT images

Yao, Lisha; Li, Sui; Li, Danyang; Zhu, Manman; Gao, Qi; Zhang, Shanli; Bian, Zhaoying; Huang, Jing; Ma, Jianhua

doi:10.1117/12.2548992

Cited by 6 publications

(8 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: 85%

“…However, the expensive cost of DECT capable scanners has limited their availability to academic medical centers [9,10]. Recent research efforts aim to broaden access to DECT technology by training artificially intelligent (AI) image-to-image translation systems to convert SECT scans into synthetic DECT (synth-DECT) image types that can then be used clinically by radiologists or medical centers that do not have dedicated DECT scanners [11][12][13][14][15][16][17][18][19][20]. The goals of the current image-to-image translation approaches are to infer DECT image types that radiologists can use for diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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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: 85%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“… 28 , 29 , 30 Recently, a deep generative model was developed to generate energy-resolved CT images in multiple energy bins from given energy-integrating CT images using a generative adversarial network framework. 31 These studies all suggest the feasibility of synthesized X-ray VM imaging using DL/ML methods. However, an effective and efficient solution has been missing for directly mapping clinical single-spectrum CT images to VM CT images.…”

Section: Introductionmentioning

confidence: 80%

Virtual Monoenergetic CT Imaging via Deep Learning

Cong

Xi²,

Fitzgerald

et al. 2020

Patterns

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Summary Conventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.

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“…This is an ideal scenario for which existing DL methods and currently available computational power can be leveraged, as shown in recent investigations. [27][28][29][30][31] Since the reconstructed single-kV CT image represents a spatial map of attenuation coefficients at average photon energy, those above synthetic monochromatic CT images can also be considered a representation of images from another single-kV scan. This also greatly facilitates the implementation of a DL framework: one can use paired training data to train a DL model to convert one single-kV CT image into another single-kV CT image, as recently shown by different investigators.…”

Section: Introductionmentioning

confidence: 99%

“…The practical implementation and accuracy of this method depend on how well one can properly assign energy‐dependent attenuation coefficients to each tissue class. This is an ideal scenario for which existing DL methods and currently available computational power can be leveraged, as shown in recent investigations 27–31 . Since the reconstructed single‐kV CT image represents a spatial map of attenuation coefficients at average photon energy, those above synthetic monochromatic CT images can also be considered a representation of images from another single‐kV scan.…”

Section: Introductionmentioning

confidence: 99%

A quality‐checked and physics‐constrained deep learning method to estimate material basis images from single‐kV contrast‐enhanced chest CT scans

Tie

et al. 2023

Medical Physics

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Background: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis. Purpose: To develop a quality-checked and physics-constrained deep learning (DL) method to estimate material basis images from single-kV CT data without resorting to dual-energy CT acquisition schemes. Methods: Single-kV CT images are decomposed into two material basis images using a deep neural network. The role of this network is to generate a feature space with 64 template features with the same matrix dimensions of the input single-kV CT image. These 64 template image features are then combined to generate the desired material basis images with different sets of combination coefficients, one for each material basis image. Dual-energy CT image acquisitions with two separate kVs were curated to generate paired training data between a single-kV CT image and the corresponding two material basis images. To ensure the obtained two material basis images are consistent with the encoded spectral information in the actual projection data, two physics constraints, that is, (1) effective energy of each measured projection datum that characterizes the beam hardening in data acquisitions and (2) physical factors of scanners such as detector and tube characteristics, are incorporated into the end-to-end training. The entire architecture is referred to as Deep-En-Chroma in this paper. In the application stage, the generated material basis images are sent to a deep quality check (Deep-QC) network to assess the quality of estimated images and to report the pixel-wise estimation errors for users.The models were developed using 5592 training and validation pairs generated from 48 clinical cases. Additional 1526 CT images from another 13 patients were used to evaluate the quantitative accuracy of water and iodine basis images estimated by Deep-En-Chroma. Results: For the iodine basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is −0.25 mg/mL, and the agreement limits are [−0.75 mg/mL, +0.24 mg/mL]. For the water basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is 0.0 g/mL, and the agreement limits are [−0.01 g/mL, 0.01 g/mL]. Across the test cohort, the median [25th, 75th percentiles] root mean square errors between the Deep-En-Chroma and dual-energy material images are 14 [12,16] mg/mL for the water images and 0.73 [0.64, 0.80] mg/mL for the iodine images. When

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Leveraging deep generative model for direct energy-resolving CT imaging via existing energy-integrating CT images

Cited by 6 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

Virtual Monoenergetic CT Imaging via Deep Learning

A quality‐checked and physics‐constrained deep learning method to estimate material basis images from single‐kV contrast‐enhanced chest CT scans

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