  We propose a method to match anatomical locations between pairs of medical
images in longitudinal comparisons. The matching is made possible by computing
a descriptor of the query point in a source image based on a hierarchical
sparse sampling of image intensities that encode the location information.
Then, a hierarchical search operation finds the corresponding point with the
most similar descriptor in the target image. This simple yet powerful strategy
reduces the computational time of mapping points to a millisecond scale on a
single CPU. Thus, radiologists can compare similar anatomical locations in near
real-time without requiring extra architectural costs for precomputing or
storing deformation fields from registrations. Our algorithm does not require
prior training, resampling, segmentation, or affine transformation steps. We
have tested our algorithm on the recently published Deep Lesion Tracking
dataset annotations. We observed more accurate matching compared to Deep Lesion
Tracker while being 24 times faster than the most precise algorithm reported
therein. We also investigated the matching accuracy on CT and MR modalities and
compared the proposed algorithm's accuracy against ground truth consolidated
from multiple radiologists.
