Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

Attenuation correction is an essential component of the long chain of data correction
techniques required to achieve the full potential of quantitative positron emission …

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic
resonance (MR) imaging–based attenuation correction (AC)(termed deep MRAC) in brain …

Synthesizing a CT image from an available MR image has recently emerged as a key goal
in radiotherapy treatment planning for cancer patients. CycleGANs have achieved promising …

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 …

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and
radiotherapy treatment planning. Since CT image intensities are directly related to positron …

Aim To accurately quantify the radioactivity concentration measured by PET, emission data
need to be corrected for photon attenuation; however, the MRI signal cannot easily be …

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …

By choosing different pulse sequences and their parameters, magnetic resonance imaging
(MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield …

In magnetic resonance imaging (MRI), several images can be obtained using different
imaging settings (eg T1, T2, DWI, and Flair). These images have similar anatomical …