Location registration and recognition (LRR) for longitudinal evaluation of corresponding regions in CT volumes.
The algorithm described in this paper takes (a) two temporally-separated CT scans, I1 and I2, and (b) a series of locations in I1, and it produces, for each location, an affine transformation mapping the locations and their immediate neighborhood from I1 to I2. It does this without deformable registration by using a combination of feature extraction, indexing, refinement and decision processes. Together these essentially "recognize" the neighborhoods. We show on lung CT scans that this works at near interactive speeds, and is at least as accurate as the Diffeomorphic Demons algorithm. The algorithm may be used both for diagnosis and treatment monitoring.
Sofka
Michal
M
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA. sofka@cs.rpi.edu
Stewart
Charles V
CV
D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*