Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of XXXX in a @entity19 model.

@entity1 @entity32 @entity55 @entity19 @entity358
PURPOSE: @entity55 is associated with a variety of diseases. @entity55 on in vivo imaging may facilitate the early detection and treatment of @entity358 . Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize @entity32 in vivo. 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 @entity32 in @entity1 , a rigorous and quantitative characterization of the utility of MRI to identify @entity32 is required. Such characterization may be achieved by considering ex vivo histology as the ground truth, since it enables the definitive spatial assessment of @entity32 . In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors' framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with @entity55 . METHODS: 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 in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. RESULTS: 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 @entity32 in a @entity19 model. The authors' evaluation considered three @entity19 , two with an @entity32 phenotype and one control. The authors' iterative 3D histology reconstruction yielded a 70.1%    2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85    0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7%    0.9%. Preliminary computerized feature analysis was performed on an additional four control @entity19 , for a total of seven @entity19 considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. CONCLUSIONS: The authors' 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for @entity32 on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for @entity55 on MRI in a large prospective cohort.
