Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively
accurate positron emission tomography (PET) images. PET AC based on computed …

Generative adversarial networks (GANs) are one powerful type of deep learning models that
have been successfully utilized in numerous fields. They belong to the broader family of …

Medical imaging plays a critical role in various clinical applications. However, due to
multiple considerations such as cost and radiation dose, the acquisition of certain image …

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

Background and objectives Medical image analysis and computer-assisted intervention
problems are increasingly being addressed with deep-learning-based solutions …

Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …

Fusing multi-modality medical images, such as magnetic resonance (MR) imaging and
positron emission tomography (PET), can provide various anatomical and functional …

Magnetic resonance (MR) imaging is a widely used medical imaging protocol that can be
configured to provide different contrasts between the tissues in human body. By setting …

A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …

We propose a multi-input multi-output fully convolutional neural network model for MRI
synthesis. The model is robust to missing data, as it benefits from, but does not require …