PubMed · 5714625
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W D Heiss, P Prosenz, A Roszuczky, H Tschabitscher. 1968. A quantitative gamma-camera technique.. https://doi.org/10.3109/00365516809169038
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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 T2- or T2*-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.
A mathematical model is presented to study the generation of the early response phenomenon in fMRI. Initially, we demonstrate that a simple combination of the changes taking place in cerebral blood volume and flow could create the transient early signal decrease, by analyzing their effects on total per voxel deoxyhemoglobin content. Also, we examine the traditional paradigm for the early response: that it may be caused by an early burst of oxidative metabolism and conclude that such changes also explain the early transient response. We suggest that the volume effect may play a role in the generation of the early response phenomenon along with an early upregulation of oxidative metabolism, and that this role may be important if the early response phenomenon is shown to occur at the level of the venous blood pool, and not just at the level of the capillary bed.