Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks.

Under-sampling in diffusion-weighted imaging (DWI) decreases the scan time that helps to reduce off-resonance effects, geometric distortions, and susceptibility artifacts; however, it leads to under-sampling artifacts. In this paper, diffusion-weighted MR image (DWI-MR) reconstruction using deep learning (DWI U-Net) is proposed to recover artifact-free DW images from variable density highly under-sampled k-space data. Additionally, different optimizers, i.

COVID-19 Repercussions: Office and Residential Emissions in Pakistan.

The purpose of this study is to find empirical evidence on whether work from home or residential emissions reduces office emissions. Based on existing research the study supports that there are short-term effects on office emissions, i.e.

Transfer learning in deep neural network-based receiver coil sensitivity map estimation.

Introduction: The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming.

Transfer learning in deep neural network based under-sampled MR image reconstruction.

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data.

Use of fine-needle aspiration in the evaluation of breast lumps.

Background. A study was designed to see the role of fine needle aspiration cytology (FNAC) in palpable breast lumps. Materials and Methods.