<title>Nonrigid multimodality image registration</title>

TLDR

Differences in imaging protocols cause significant non‑linear motion between PET and CT acquisitions. The algorithm models local motion with cubic B‑splines on a regular grid, uses spline‑based image representation and Parzen histograms to compute closed‑form mutual‑information gradients, and optimizes the deformation with a limited‑memory quasi‑Newton method in a multiresolution hierarchy, validated on 27 lung‑cancer screening scans. The method achieves 0–6 mm registration error with an average runtime of 100 minutes.

Abstract

We have designed, implemented, and validated an algorithm capable of 3D PET-CT registration in the chest, using mutual information as a similarity criterion. Inherent differences in the imaging protocols produce significant non-linear motion between the two acquisitions. To recover this motion, local deformations modeled with cubic B-splines are incorporated into the transformation. 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, the deformation model allows for closed-form expressions of the criterion and its gradient. A limited-memory quasi-Newton optimization package is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the algorithm, 27 scans from patients involved in routine lung cancer screening were used in a validation study. The registrations were assessed visually by two observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0-6mm range and the average computation time is 100 minutes.