We propose a new metric, local uncertainty (LU) for the evaluation of deformable image registration (DIR) for dose
accumulation in radiotherapy. LU measures the uncertainty of placement of each voxel in an image set after a DIR. The
underlying concept of LU is that the distance between a focused voxel and a surrounding voxel on an image feature such
as an edge is unchanged locally when the organ that includes these voxels is deformed. A candidate for the focused voxel
after DIR can be calculated from three surrounding voxels and their distances. The positions of the candidates of the
focused voxel calculated from several groups of any three surrounding voxels would vary. The variation of candidate
positions indicates uncertainty in the focused voxel position. Thus, the standard deviation of candidate positions is treated
as an LU value. The LU can be calculated in uniform signal regions. Assessment of DIR results in such regions is important
for dose accumulation. The LU calculation was applied to a pair of computed tomography (CT) head and neck
examinations after DIR. These CT examinations were for initial radiotherapy planning, and re-planning for a treatment
course where the tumor underwent shrinkage during treatment. We generated an LU image showing high LU values in the
shrinking tumor region and low LU values in undeformable bone. We have proposed the LU as a new metric for DIR.
To achieve sufficient accuracy and robustness, 2D/3D registration methods between DSA and MRA of the cerebral artery require an automatic extraction method that can isolate wanted segments from the cerebral artery tree.
Here, we described an automatic segmentation method that divides the cerebral artery tree in time-of-flight magnetic resonance angiography (TOF-MRA) into each artery. This method requires a 3D dataset of the cerebral artery tree obtained by TOF-MRA. The processes of this method are: 1) every branch in the cerebral artery tree is labeled with a unique index number, 2) the 3D center of the Circle of Willis is determined using 2D and 3D templates, and 3) the labeled branches are classified with reference to the 3D territory map of cerebral arteries centered on the Circle of Willis. This method classifies all branches into internal carotid arteries (ICA), basilar artery (BA), middle cerebral artery (MCA), a1 segment of anterior cerebral artery (ACA(A1)), other segments of the anterior cerebral artery (ACA), posterior communication artery (PcomA), and posterior cerebral artery (PCA). In the eleven cases examined, the numbers of correctly segmented pixels in each branch were counted and the percentages based on the total number of pixels of the artery were calculated. Manually classified arteries of each case were used as references. Mean percentages were: ACA, 87.6%; R-ACA(A1), 44.9%; L-ACA(A1), 30.4%; R-MC, 82.4%; L-MC, 79.0%; R-PcomA, 0.5%; L-PcomA, 0.0%; R-PCA, 77.2%; L-PCA, 80.0%; R-ICA, 78.6%; L-ICA, 93.05; BA, 77.1%; and total arteries, 78.9%.