Simulating Ultrasound images from CT Scans
Almahfouz Nasser, S.; Sethi, A.
Show abstract
Anatomical information in ultrasound (US) imaging has not been exploited fully because its wave interference pattern (WIP) has been viewed as speckle noise. We tested the idea that more information can be retrieved by disentangling the WIP rather than discarding it as noise. We numerically solved the forward model of generating US images from computed tomography (CT) images by solving wave-equations using the Stride library. By doing so, we have paved the way for using deep neural networks to be trained on the data generated by the forward model to simulate the solution of the inverse problem, which is generating the CT-style and CT-quality images from a real US image. We demonstrate qualitative features of the generated images that are rich in anatomical details and realism.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Model for Deformation of Cells from External Electric Fields at or Near Resonant Frequencies 90%
- SimBSI: An open-source Simulink library for developingclosed-loop brain signal interfaces in animals and humans 88%
- Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches 88%
Similar papers in this journal
- Development of a Coupled Simulation Toolkit for Computational Radiation Biology Based on Geant4 and CompuCell3D 92%
- Impact of Focused Ultrasound on the Cellular Network of Liver Tissue: A New Perspective for Thermal Lesion Detection 92%
- Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach 91%
Similar papers in this journal
- Fully Automated Explainable Abdominal CT Contrast Media Phase Classification Using Organ Segmentation and Machine Learning 93%
- Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography 92%
- Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling 90%
Similar papers in this journal
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 93%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 93%
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.