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Found 14 papers, 1 papers with code
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions. To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR.
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation.
PET image denoising based on denoising diffusion probabilistic models
Regional and surface quantification shows that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference can achieve the best performance.
Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising
In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs.
A Noise-level-aware Framework for PET Image Denoising
Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.
Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction
In this paper, we propose an implicit regularization for the kernel method by using a deep coefficient prior, which represents the kernel coefficient image in the PET forward model using a convolutional neural-network.
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework.
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network
After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.
Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Positron emission tomography (PET) is widely used for clinical diagnosis.
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
The representation is expressed using a deep neural network with the patient's prior images as network input.
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods.
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool.
