PURPOSE: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging.
Neural networks : the official journal of the International Neural Network Society
Feb 23, 2023
In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalized image outpainting problems. Different from most present image outpainting methods conducting horizontal extrapolation, our ...
Fiber-bundle endomicroscopy has several recognized drawbacks, the most prominent being the honeycomb effect. We developed a multi-frame super-resolution algorithm exploiting bundle rotation to extract features and reconstruct underlying tissue. Simul...
Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC) each image volume is explored slice-by-slice from different orientati...
AJR. American journal of roentgenology
Feb 22, 2023
Clinically usable artificial intelligence (AI) tools analyzing imaging studies should be robust to expected variations in study parameters. The purposes of this study were to assess the technical adequacy of a set of automated AI abdominal CT body ...
PURPOSE: To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5 mm slice thickness gra...
Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation ...
PURPOSE: Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in im...
Fast and accurate auto-segmentation is essential for magnetic resonance-guided adaptive radiation therapy (MRgART). Deep learning auto-segmentation (DLAS) is not always clinically acceptable, particularly for complex abdominal organs. We previously r...
OBJECTIVES: To evaluate the diagnostic performance of an automated reconstruction algorithm combining MR imaging acquired using compressed SENSE (CS) with deep learning (DL) in order to reconstruct denoised high-quality images from undersampled MR im...
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