Decision making based on quantitative data
Automatic segmentation of brain tissue based on SyMaps™, parametric T1, T2 and PD maps, enables efficient analysis and objective decision making based on quantitative data.
Automatic segmentation of brain tissue
SyMRI NEURO automatically characterizes and measures cerebrospinal fluid (CSF), white matter (WM), grey matter (GM), and remaining tissue.
The intracranial cavity and the tissue maps are segmented in a few seconds.
Volumetric information is provided for the complete intracranial volume (ICV), per slice and per region of interest.
Automatic measurement of BPF
The Brain Parenchymal fraction (BPF) is a ratio that is calculated based on automatic identification of ICV, brain tissue and CSF.
BPF is a valuable and clinically used measurement for brain atrophy in patients with neurodegenerative diseases such as MS and dementia.
User defined segmentation
SyMRI NEURO includes a tool for measuring tissue volume in a region of interest (ROI) defined by the user.
This can be used to measure MS lesion load, tumor volume, ventricle volume or other volumes for improved diagnostic capability.
Quantitative Multiple Sclerosis (MS) images, Karolinska University Hospital, Sweden
“Synthetic MR imaging can be an alternative to conventional MR imaging for generating diagnostic proton-density–, T1-, and T2-weighted images in patients with MS
…while additionally delivering fast and robust volumetric measurements…”
Source: Granberg T et al. AJNR 2016
Measuring brain atrophy in MS patients, Umeå University Hospital, Sweden
“[SyMRI] is a valid and reproducible method for determining BPF in MS within a clinically acceptable scan time and post-processing time.”
Source: Vågberg et al. AJNR 2013
Monitoring hydrocephalus, Uppsala University Hospital, Sweden
“Brain parenchymal fraction (BPF) is provided rapidly and fully automatically with Synthetic MRI and can be used to monitor ventricular volume changes.
The method may be useful for objective clinical monitoring of hydrocephalus.”
Source: Virhammar J et al. AJNR 2016.