Reliable Fusion of Knee Bone Laser Scans to Establish Ground Truth for Cartilage Thickness Measurement

摘要

We are interested in establishing ground truth data for validating morphology measurements of human knee cartilage from MR imaging. One promising approach is to compare the high-accuracy 3D laser scans of dissected cadaver knees before and after the dissolution of their cartilage. This requires an accurate and reliable method to fuse the individual laser scans from multiple views of the cadaver knees. Unfortunately existing methods using Iterative Closest Point (ICP) algorithm from off-the-shell packages often yield unreliable fusion results. We identify two major sources of variation: (i) the noise in depth measurements of the laser scans is significantly high and (ii) the use of point-to-point correspondence in ICP is not suitable due to sampling variation in the laser scans. We resolve the first problem by performing adaptive Gaussian smoothing on each individual laser scans prior to the fusion. For the second problem, we construct a surface mesh from the point cloud of each scan and adopt a point-to-mesh ICP scheme for pairwise alignment. The complete surface mesh is constructed by fusing all the scans in the order maximizing mutual overlaps. In experiments on 6 repeated scanning trials of a cadaver knee, our approach reduced the alignment error of point-to-point ICP by 30% and reduced coefficient of variation (CV) of cartilage thickness measurements from 5% down to 1.4%, significantly improving the method's repeatability.