Maximum likelihood estimation of cerebral blood flow in dynamic susceptibility contrast MRI

✍ Scribed by Evert-jan P.A. Vonken; Freek J. Beekman; Chris J.G. Bakker; Max A. Viergever

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 269 KB
• Volume: 41
• Category: Article
• ISSN: 0740-3194
• DOI: 10.1002/(sici)1522-2594(199902)41:2<343::aid-mrm19>3.0.co;2-t

✦ Synopsis

For quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI), knowledge of the tissue response function is necessary. To obtain this, the tissue contrast passage measurement must be corrected for the arterial input. This study proposes an iterative maximum likelihood expectation maximization (ML-EM) algorithm for this correction, which takes into account the noise in T 2 -or T* 2 -weighted image sequences. The ML-EM algorithm does not assume a priori knowledge of the shape of the response function; it automatically corrects for arrival time offsets and inherently yields positive response values. The results on synthetic image sequences are presented, for which the recovered flow values and the response functions are in good agreement with their expectation values. The method is illustrated by calculating the gray and white matter flow in a clinical example.