Adaptive boundary conditions for physically based follow-up breast MR image registration.
This paper presents an algorithm for non-rigid registration of breast MRI follow-up images that compensates for differences in patient positioning while maintaining real anatomical and pathological changes. The proposed method uses a biomechanical model to constrain the deformation of the internal breast tissue according to elastic continuum mechanics, which is driven by suitable boundary conditions that align the breast surfaces in the images to be registered. Typically, such boundary conditions impose one-to-one surface point correspondences that are established a priori. We investigate alternative, more flexible boundary conditions that do not depend on fixed point correspondences and do not assume completely accurate breast surface segmentation in both images. More specifically, we allow for sliding motion of one surface over the other during deformation as well as for restricted motion perpendicular to the initially segmented boundary surface, based on the internal elastic forces and local intensity information. We evaluate the impact of different boundary conditions on registration quality from the subtraction images obtained for repeated scans of healthy volunteers with intermediate repositioning, using rigid body and free form whole volume intensity based registration for comparison, and also present initial results for actual patient data. Our results demonstrate a drastic reduction in subtraction artifacts using our model, without compromising the biomechanical validity of the deformation field such as unrealistically large local volume changes as with traditional voxel intensity based registration.
Roose
Liesbet
L
Katholieke Universiteit Leuven, Faculty of Medicine, Medical Image Computing (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium. liesbet.roose@uz.kuleuven.ac.be
Loeckx
Dirk
D
Mollemans
Wouter
W
Maest
Frederik
F
Suetens
Paul
P
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D005500:Follow-Up Studies; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique