MRI in the fast lane: MR-STAT

Recruiting

Conditions: one of the following neurological diseases: primary brain tumour, epilepsy, MS or ischemic stroke

Registration Number: NL-OMON26690

Lead Sponsor: MC Utrecht and NWO

Brief Summary: 1) Sbrizzi, A., van der Heide, O., Cloos, M., van der Toorn, A., Hoogduin, H., Luijten, P. R., & van den Berg, C. A. (2018). Fast quantitative MRI as a nonlinear tomography problem. Magnetic resonance imaging, 46, 56-63. 2) van der Heide, O., Sbrizzi, A., Luijten, P. R., & van den Berg, C. A. (2019). High resolution in-vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm. NMR Biomed, In press

Detailed Description: Not available

Recruitment & Eligibility

Status: Recruiting

Sex: Not specified

Target Recruitment: 50

Inclusion Criteria

In order to be eligible to participate in this study, a patient must meet the following criteria:
1) Age = 18 years
2) Diagnosed with one of the following neurological diseases: primary brain tumour, epilepsy, MS or ischemic stroke
3) Previous imaging findings characteristic of particular neurological disease
4) Ability to lie supine in the MRI scanner for 45 minutes

Exclusion Criteria

Patients will be excluded when meeting one of the following criteria:
a) Atypical imaging findings not characteristic for the neurological diagnosis
Exclusion criteria for the healthy volunteers are as follows:
b) History of any neurological disease
c) Refusal to be informed of clinically relevant incidental findings

Study & Design

Study Type: Interventional

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The primary objective of this clinical study is to assess image quality of MR-STAT-generated synthetic image data sets (e.g. synthetic T1-, T2-, PD-weighted, FLAIR) in patients with neurological diseases. Although preliminary results in healthy volunteers have shown MR-STAT is able to generate good quality MR images with clinically desired image contrast weightings, due to a lack of pathology these images have only been assessed for general quality, i.e. image contrast, (lack of) artefacts, etc. Patients in the current study will have a variety of recognizable pathology on standard MRI, which enables not only general quality assessment but also assessment of the discriminative power of the (MR-STAT) synthetic data sets to differentiate pathology from healthy tissue.

Secondary Outcome Measures

Name	Time	Method
The secondary objective of this study is to compare the MR-STAT-generated synthetic image data sets with those acquired individually according to standard clinical protocol – i.e. standard T1-weighted vs. synthetic T1-weighted images and so forth – to assess image quality of the synthetic images compared with gold standard.