Using mutual information (MI) for automated 3D registration in the pelvis and thorax region for radiotherapy treatment planning

We investigated the use of MI to register images of the pelvis and thorax regions which is a complex problem compared to the head since changes occur in soft tissue while the bony anatomy stays stable. We focused on the bony anatomy eliminating the soft tissue data by applying the MI for bone intensities. Instead of linear binning the whole spectrum of CT intensities, we bin the intensities chosen by the user as corresponding to the bone. We truncated the data spatially by choosing a region, because some bony anatomy might move from scan to scan relative to the stable parts i.e. ribs in the lung region might move with breathing or legs in the pelvis. We compare the effects of using our intensity-dependent-regional MI to the original MI for 9 pairs of CT-CT pelvis, 3 pairs of CT-CT lung and 5 pairs of CT-PET patient studies. With the original algorithm, the root-mean-square registration error can be as high 2 cm for CT-CT. The registration error with the intensity-dependent-regional algorithm, however, was on the average 2 mm for CT-CT pelvic and 4 mm for CT-CT lung studies. Using just the bone intensities and a specific region, we have a smaller sample size, which decreased our calculation time up to 8 times to less than 15 seconds. For CT-PET studies the average registration error is reduced from 3.2 cm to 0.5 cm.