PET-CT image registration in the chest using free-form deformations

TLDR

Inherent differences between PET and CT imaging protocols produce significant nonrigid motion in the chest. The algorithm combines a rigid body transform with localized cubic B‑splines on a regular grid, uses spline‑based image representation and Parzen histogram estimates to compute mutual‑information gradients, and applies a limited‑memory quasi‑Newton optimizer in a hierarchical multiresolution framework to automatically align PET and CT scans. Validated on 27 lung‑cancer staging scans, the method achieved visually reported errors of 0–6 mm and an average computation time of 100 min on a moderate‑performance workstation.

Abstract

We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.