  Computational anatomy allows the quantitative analysis of organs in medical
images. However, most analysis is constrained to the millimeter scale because
of the limited resolution of clinical computed tomography (CT). X-ray
microtomography ($\mu$CT) on the other hand allows imaging of ex-vivo tissues
at a resolution of tens of microns. In this work, we use clinical CT to image
lung cancer patients before partial pneumonectomy (resection of pathological
lung tissue). The resected specimen is prepared for $\mu$CT imaging at a voxel
resolution of 50 $\mu$m (0.05 mm). This high-resolution image of the lung
cancer tissue allows further insides into understanding of tumor growth and
categorization. For making full use of this additional information, image
fusion (registration) needs to be performed in order to re-align the $\mu$CT
image with clinical CT. We developed a multi-scale non-rigid registration
approach. After manual initialization using a few landmark points and rigid
alignment, several levels of non-rigid registration between down-sampled (in
the case of $\mu$CT) and up-sampled (in the case of clinical CT)
representations of the image are performed. Any non-lung tissue is ignored
during the computation of the similarity measure used to guide the registration
during optimization. We are able to recover the volume differences introduced
by the resection and preparation of the lung specimen. The average ($\pm$ std.
dev.) minimum surface distance between $\mu$CT and clinical CT at the resected
lung surface is reduced from 3.3 $\pm$ 2.9 (range: [0.1, 15.9]) to 2.3 mm $\pm$
2.8 (range: [0.0, 15.3]) mm. The alignment of clinical CT with $\mu$CT will
allow further registration with even finer resolutions of $\mu$CT (up to 10
$\mu$m resolution) and ultimately with histopathological microscopy images for
further macro to micro image fusion that can aid medical image analysis.
