Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology

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 (μCT) 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 μCT imaging at a voxel resolution of 50 μm (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 μCT image with clinical CT. We developed a multiscale 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 μCT) 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 (± std. dev.) minimum surface distance between μCT and clinical CT at the resected lung surface is reduced from 3.3 ± 2.9 (range: [0.1, 15.9]) to 2.3 mm ± 2.8 (range: [0.0, 15.3]) mm. This is a significant improvement with p < 0.001 (Wilcoxon Signed Rank Test). The alignment of clinical CT with μCT will allow further registration with even finer resolutions of μCT (up to 10 μm resolution) and ultimately with histopathological microscopy images for further macro to micro image fusion that can aid medical image analysis.