Arterial spin labeling is becoming an increasingly popular method for evaluating the
cerebral blood flow. However, one of the major limitations of arterial spin labeling
(and other perfusion assessment methods like PET) is the partial volume effect, by
which each voxel of the image contains a mixture of tissues due to the low spatial
resolution of ASL. Partial volume correction is required to retrieve the perfusion
contribution of each of these tissues, which is important in the study of
neurodegenerative diseases such as Alzheimer’s disease.
A new partial volume correction method (3D weighted least squares) based on an
existing state-of-the-art method (Asllani’s algorithm) is presented in this work. The
new algorithm improves the previous algorithm by operating in a 3D way instead of a
2D way and including a weighting to the regression problem as a function of the
distance between the voxels.The new method was tested over simulated cerebral
perfusion images, giving better results than the Asllani’s algorithm.
The algorithm was also implemented as a graphical user interface extension for the
open source platform 3DSlicer. This extension automates all the correction process
and allows the researchers processing the ASL images rapidly and easily.
Using this extension, a real perfusion study was conducted to compare the cerebral
perfusion between Alzheimer and control groups in resting state. Alzheimer group
showed a significantly lower perfusion in the thalamus, caudate nucleus, hippocampus
and cuneus. These regions have been reported in the literature to present atrophies in
Alzheimer subjects and are involved in cognitive functions that are negatively affected
by the disease. These results provide further validation for the 3DWLS as a suitable
correction method and for the extension as a useful research tool.Ingeniería Biomédic
