Virtual Monoenergetic CT Imaging via Deep Learning

Cong, Wenxiang; Xi, Yan; Fitzgerald, Peter; Man, Bruno De; Wang, Ge

doi:10.1016/j.patter.2020.100128

Cited by 33 publications

(16 citation statements)

References 37 publications

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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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“…Polychromatic CT images from the Visible Human Project were used to generate realistic medical images, courtesy of the U.S. National Library of Medicine 27 . From these images, corresponding attenuation mappings between 69 and 80 keV as well as electron density maps were created using the deep‐learning‐based virtual monoenergetic estimation and material decomposition methods 28 . A total of 400 images throughout the chest and abdomen region were used for training and testing.…”

Section: Methodsmentioning

confidence: 99%

“…27 From these images, corresponding attenuation mappings between 69 and 80 keV as well as electron density maps were created using the deep-learning-based virtual monoenergetic estimation and material decomposition methods. 28 A total of 400 images throughout the chest and abdomen region were used for training and testing. Forty additional images were created with a simulated liver tumor to evaluate detection of clinically relevant features.…”

Section: Medical Image Generationmentioning

confidence: 99%

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

2021

Self Cite

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Creating a viable reconstruction method for Compton scatter tomography remains challenging. Accounting for scatter attenuation when the underlying attenuation map is not known is particularly challenging, and current mathematical approaches to this vary widely. This work explores a novel approach to joint scatter and attenuation image reconstruction, which leverages the underlying structural similarity between the two images and incorporates a deep learning model in an alternating iterative reconstruction scheme. Methods: A single-view computed tomography (CT) imaging procedure for recording Compton scatter is first described. A joint reconstruction model, which iterates between algebraically reconstructing scatter images and estimating the attenuation via deep learning, is then proposed. This model is tested on both a generated dataset of 2D phantom images designed to mimic human tissues as well as a realistically simulated dataset based on real CT images. Results: Testing results yield convergence of the model and decent reconstruction quality to distinguish crucial features such as tumors and lesions, demonstrating the potential principled utilities of this configuration and deep learning approach. The model achieved a structural similarity index measure of at least 0.82 for scatter and 0.88 for attenuation reconstructions with the realistically simulated dataset. Conclusion:The iterative, deep learning approach outlined in this work shows potential for future efficient medical imaging procedures, reconstructing images with limited scatter information.

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“…Finally, we note a trend in recent literature: estimating missing spectral information with DL priors. This has been demonstrated in the context of spectral extrapolation for dual-energy field-of-view extension [113], estimation of virtual monoenergetic images from single-energy data [114], and estimation of material maps form single-energy data [115]. Success in these applications speaks to the power of DL to model and enforce underlying relationships between image features and spectral contrast; however, significant work remains to understand the limitations and uncertainty inherent in these methods, with particular regard to unique or pathological data which may poorly represented.…”

Section: Spectral Processingmentioning

confidence: 96%

Advances in micro-CT imaging of small animals

Clark

Badea

2021

Physica Medica

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Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research. Methods: Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-theart hardware and ongoing challenges and promising research directions in the field. Results: Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT. Conclusions: All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

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Virtual Monoenergetic CT Imaging via Deep Learning

Cited by 33 publications

References 37 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

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

Advances in micro-CT imaging of small animals

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