Automated analysis of small animal PET studies through deformable registration to an atlas

Abstract

Purpose: This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.

Methods: A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.

Results: The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered.

Conclusion: The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.

Fig. 1

Spatially registered multimodality images for a coronal slice through the Digimouse model. From left to right: X-ray CT, PET, cryosection, segmented atlas and overlay of the atlas onto cryosection

Fig. 2

Example of coronal and sagittal slices of PET/CT studies (a-c) and overlay of the atlas onto CT images (d-f) of the experimental mouse studies acquired using ¹⁸F-FDG (left), ¹⁸F-NaF (middle) and bispecific antibody labelled with ⁶⁸Ga (right) radiotracers

Fig. 3

Coronal views of a voxel-based mouse model generated using the Moby software for simulation of an ¹⁸F-FDG study showing from left to right: activity map, attenuation map, segmented image, simulated X-ray projection image, simulated CT image and corresponding simulated PET image

Fig. 4

Illustration of the best deformable registration example between the Digimouse and one of the experimental mouse studies (mouse 4) showing: a overlay of the Digimouse atlas onto corresponding CT images, b actual ¹⁸F-FDG PET/CT mouse study, c mouse study shown in b with overlay of the segmentation onto CT image (seven organs), d CT to CT registration of the Digimouse and actual mouse study shown in c, e overlay of the transformed segmentation (seven organs) using registration parameters obtained in d onto CT image and f transformed PET/CT study using registration parameters obtained in d

Fig. 5

Same as Fig. 4 for the worst deformable registration example between the Digimouse and one of the experimental mouse PET/CT studies (mouse 6) acquired using ⁶⁸Ga-labelled ethylenediaminetetraacetic acid (EDTA)

Fig. 6

Box-and-whisker plots of image registration metrics (DC and HD) and automated quantitative analysis (NMA) results for the 8 experimental and 17 simulated mice studies for the 3 and 8 segmented regions, respectively. a Dice coefficients, b Hausdorff distance and c normalized mean activity (NMA) relative difference