Framework for 3D histologic reconstruction and fusion with in vivo MRI

TitleFramework for 3D histologic reconstruction and fusion with in vivo MRI
Publication TypeJournal Article
Year of Publication2015
AuthorsRusu, M, Golden T, Wang H, Gow A, Madabhushi A
JournalMedical Physics
Date Published08/2015

Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation . Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, and imaging to enable the mapping of the extent of disease from histology onto imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on imaging. The authors’ framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on MRI associated with chronic pulmonary inflammation.

The authors’ image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto MRI via coregistration with 3D histology using the lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate imaging signatures of disease presence and extent.

The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology—MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors’ evaluation considered three mice, two with an inflammation phenotype and one control. The authors’ iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the 3D histology and MRI had been achieved. The 3D histology- MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI.

The authors’ 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with MRI to create a ground truth map for inflammation on MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.


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