We present a method to automate acquisition of MR brain scans to allow consistent alignment of diagnostic images for
patient follow-up, and to depict standardized anatomy for all patients. The algorithm takes as input a low-resolution
acquisition that depicts the patient position within the scanner. The mid-sagittal plane dividing the brain hemispheres is
automatically detected, as are bony landmarks at the front and back of the skull. The orientation and position of a
subsequent diagnostic, high resolution scan is then aligned based on these landmarks. The method was tested on 91 data
sets, and was completely successful in 93.4% of cases, performed acceptably in 4.4% of cases, and failed for 1.1%. We
conclude that the method is suitable for clinical use and should prove valuable for improving consistency of acquisitions.
Magnetic resonance diffusion tensor imaging (DTI) is being widely used to reconstruct brain white matter (WM) fiber
tracts. For further characterization of the tracts, the fibers with similar courses often need to be grouped into a fiber
bundle that corresponds to certain underlying WM anatomic structure. In addition, the alignments of fibers from different
studies are often desirable for bundle comparisons and group analysis.
In this work, a novel registration algorithm based on fiber-to-bundle matching was proposed to address the above two
needs. Using an Expectation Maximization (EM) algorithm, the proposed method is capable of estimating a Thin-Plate-
Spline transformation that optimally aligns whole-brain target fiber sets with a reference bundle model. Based on the
resulting transformations, the fibers from different target datasets can all be warped into the reference coordinate system
for comparisons and group analysis. The fibers can be further automatically labeled according to their similarity to the
reference model.
The algorithm was evaluated with eight human brain DTI data volumes acquired in vivo at 3T. After registration, the
warped target bundles exhibit good similarity to the reference bundles. Quantitative experiments further demonstrated
that the detected target bundles agree with ground truth obtained by manual segmentation at a sub-voxel accuracy.
In most magnetic resonance imaging (MRI) clinical examinations, the orientation and position of diagnostic scans are
manually defined by MRI operators. To accelerate the workflow, algorithms have been proposed to automate the
definition of the MRI scanning planes. A mid-sagittal plane (MSP), which separates the two cerebral hemispheres, is
commonly used to align MRI neurological scans, since it standardizes the visualization of important anatomy. We
propose an algorithm to define the MSP automatically based on lines separating the cerebral hemispheres in 2D coronal
and transverse images. Challenges to the automatic definition of separation lines are disturbances from the inclusion of
the shoulder, and the asymmetry of the brain. The proposed algorithm first detects the position of the head by fitting an
ellipse that maximizes the image gradient magnitude in the boundary region of the ellipse. A symmetrical axis is then
established which minimizes the difference between the image on either side of the axis. The pixels at the space between
the hemispheres are located in the adjacent area of the symmetrical axis, and a linear regression with robust weights
defines a line that best separates the two hemispheres. The geometry of MSP is calculated based on the separation lines
in the coronal and transverse views. Experiments on 100 images indicate that the result of the proposed algorithm is
consistent with the results obtained by domain experts and is significantly faster.
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