Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by m...

PURPOSE In brain CT perfusion (CTP), the arterial contrast bolus is scaled to have the same area under the curve (AUC) as the venous outflow to correct for partial volume effects (PVE). This scaling is based on the assumption that large veins are unaffected by PVE. Measurement of the internal carotid artery (ICA), usually unaffected by PVE due to its large diameter, may avoid the need for parti...

UNLABELLED The limited spatial resolution of SPECT scanners does not allow an exact measurement of the local radiotracer concentration in brain tissue because partial-volume effects (PVEs) underestimate concentration in small structures of the brain. The aim of this study was to determine which brain structures show greater influence of PVEs in SPECT studies on healthy volunteers and to investi...

Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA Our goal in this study was to characterize the combined effect of MR-assisted motion correction (MC) and partial volume effects correction (PVEC) on the estimation of [C]NNC112 binding potential (BP) in healthy volunteers. 29 subjects were...

Partial volume effects (PVE) are a consequence of limited spatial resolution in brain imaging. In arterial spin labeling (ASL) MRI, the problem is exacerbated by the nonlinear dependency of the ASL signal on magnetization contributions from each tissue within an imaged voxel. We have developed an algorithm that corrects for PVE in ASL imaging. The algorithm is based on a model that represents t...

A method for partial volume correction of positron emission tomography (PET) images is proposed. The method is based on combining information from PET and high resolution anatomical images. Two different algorithms fast wavelet transform and à trous were implemented in order to decompose the PET and high resolution images. Results obtained using several simulated images show a significant parti...