Accurate quantification in PET and SPECT requires correction for a number of physical
factors, such as photon attenuation, Compton scattering and random coincidences (in PET) …

This is a review of recent mathematical and computational advances in optical tomography.
We discuss the physical foundations of forward models for light propagation on microscopic …

The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …

Recently, deep neural networks have been widely and successfully applied in computer
vision tasks and have attracted growing interest in medical imaging. One barrier for the …

Image processing plays a crucial role in maximising diagnostic quality of positron emission
tomography (PET) images. Recently, deep learning methods developed across many fields …

Images of the inside of the human body can be obtained noninvasively using tomographic
acquisition and processing techniques. In particular, these techniques are commonly used …

In emission tomography statistically based iterative methods can improve image quality
relative to analytic image reconstruction through more accurate physical and statistical …

Image reconstruction from low-count positron emission tomography (PET) projection data is
challenging because the inverse problem is ill-posed. Prior information can be used to …

We present a new approach to the recovering of dipole magnitudes in a distributed source
model for magnetoencephalographic (MEG) and electroencephalographic (EEG) imaging …

PET is a medical imaging modality with proven clinical value for disease diagnosis and
treatment monitoring. The integration of PET and CT on modern scanners provides a …