The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Mar. 06, 2012
Filed:
May. 19, 2006
Matthias Gunther, Bruchsal, DE;
David Feinberg, Sebastapol, CA (US);
Matthias Gunther, Bruchsal, DE;
David Feinberg, Sebastapol, CA (US);
Advanced MRI Technologies, LLC, Sebastopol, CA (US);
Abstract
Regional arterial spin labeling (regASL) speeds up acquisition without sacrificing the signal-to-noise ratio (SNR) of the resulting perfusion images by using the same control image (i.e. acquired without labeling of blood in a vessel) for two or more vascular territory measurements. This regional ASL is accomplished by creating prepared spin magnetization (e.g. inverted or saturated) in a specific blood vessel, instead of preparing spin magnetization in all feeding blood vessels. As in conventional ASL, two data sets are typically acquired in a downstream position: one with (label image) and one without preparation (control image) in one particular vessel. When regASL is extended to repetitive time series of ASL images to identify perfusion changes for functional MRI (fMRI), the speed of the time series of ASL also is increased because only a single C data set can be used redundantly for all time points of ASL measurement, to reduce the amount of data acquired by nearly one half, with no change in the signal to noise ratio in the ASL images. To account for possible movement of the person causing misregistrations in the time series, the C data can be obtained two or more times throughout the time series while still at lower frequency than in prior techniques with alternating L and C data sets.