Image Processing Reference
In-Depth Information
activation [88,89]. However, direct application of MRI to capture functional
activity remains limited due to a low signal-to-noise ratio (SNR), which is why
MRI is often labeled anatomical. The next subsection briefly describes the anal-
ysis of acquired high-resolution 3-D images of the brain and how obtained
structural information can be used to analyze data collected from other modalities.
8.4.1.1
Registration of EEG and MEG to MRI
If an EEG experiment is performed inside the magnet, it is possible to “mark”
[90] the location of the EEG sensors to make them distinguishable on the ana-
tomical MRI. Coordinates for these locations can then be found either manually
or automatically [91] and will lie in the MRI coordinate system. In the case when
MR and E/MEG data are acquired in separate sessions, spatial registration
between E/MEG and MRI coordinate systems must be performed before any
anatomical information can be introduced into the analysis of E/MEG data. There
are two general possible ways of performing registration between MRI and
E/MEG data: (a) registering a limited set of fiducial points or (b) aligning scalp
surfaces obtained during MRI with a digitization of the scalp during E/MEG.
Methods based on the alignment of the scalp surfaces (or point clouds) considered
to perform better than those using fiducial points [92-95], but are more computa-
tionally demanding and rely on iterative optimization. In addition, it can be time
consuming to obtain the dense digitization of the subject's head using a single-
point 3-D digitizer. For these reasons the fiducial points approach remains the
preferred E/MEG/MRI registration method (for instance, 90,96). The fiducial points
method involves the alignment of a limited set of points, which have a strict known
correspondence between the two spaces, so that each fiducial point in E/MEG space
with coordinates ( ) has a corresponding known point ( ) in MRI space. Such
coupling removes the possibility of being trapped in the local minima of the iterative
surface-aligning methods and makes registration simple and fast. The precision of
the derived transformation can be increased by adding more pairs of corresponding
E/MEG and MRI points. A more detailed description of the registration method
using fiducial points follows.
Locations of the fiducial points (e.g., anatomical points: nasion, inion,
preauricular points or tragus of the left and right earlobes, vertex; MRI-visible
capsules or even bite-bar points [97,98]) are captured together with the loca-
tions of E/MEG sensors using a 3-D digitizer and then matched to the locations
of corresponding fiducial points obtained from the analysis of the MRI for
the same subject. A 3-D rigid transformation of the points from the E/MEG
( ) to the MRI coordinate system ( ) can be defined by the rotation
matrix and translation vector , so that . Commonly, the
quadratic misregistration error measure is the subject to minimization
x
i
E
x
i
M
x
E
x
EM
→
R
v
x
EM
→
=
x
i
E
+
v
=
where is the number of the points. Solutions can be found
with simplified geometrical formulations [99] or iterative search optimization
using Powell's algorithm [97]. Such simplifications or complications are not
necessary because the analytical form solutions have been derived in other fields
ε
(
R
,
v
)
p
Σ
i
(
xx
ME
−
→
M
)
2
,
P
i
Search WWH ::
Custom Search