Sigmoid function based intensity transformation for parameter initialization in MRI-PET Registration Tool for Preclinical Studies

TL;DR

This paper presents a semi-automatic MRI-PET registration tool for small animal imaging, utilizing a sigmoid-based intensity transformation to enhance contrast and improve registration accuracy in preclinical studies.

Contribution

It introduces a novel non-uniform intensity transformation based on sigmoid functions to improve initial parameter estimation in MRI-PET registration.

Findings

Enhanced registration accuracy with the proposed intensity transformation.

Validated effectiveness on diverse abdominal and brain datasets.

Supported by GUI for user-friendly interaction.

Abstract

Images from Positron Emission Tomography (PET) deliver functional data such as perfusion and metabolism. On the other hand, images from Magnetic Resonance Imaging (MRI) provide information describing anatomical structures. Fusing the complementary information from the two modalities is helpful in oncology. In this project, we implemented a complete tool allowing semi-automatic MRI-PET registration for small animal imaging in the preclinical studies. A two-stage hierarchical registration approach is proposed. First, a global affine registration is applied. For robust and fast registration, principal component analysis (PCA) is used to compute the initial parameters for the global affine registration. Since, only the low intensities in the PET volume reveal the anatomic information on the MRI scan, we proposed a non-uniform intensity transformation to the PET volume to enhance the contrast of the low intensity. This helps to improve the computation of the centroid and principal axis by increasing the contribution of the low intensities. Then, the globally registered image is given as input to the second stage which is a local deformable registration (B-spline registration). Mutual information is used as metric function for the optimization. A multi-resolution approach is used in both stages. The registration algorithm is supported by graphical user interface (GUI) and visualization methods so that the user can interact easily with the process. The performance of the registration algorithm is validated by two medical experts on seven different datasets on abdominal and brain areas including noisy and difficult image volumes.