Interactive registration of 2D histology and 3D CT data for assessment of radiofrequency ablation treatment

Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging

Classical analysis of biological samples requires the destruction of the tissue's integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies.

Radiofrequency ablation with four electrodes as a building block for matrix radiofrequency ablation: Ex vivo liver experiments and finite element method modelling. Influence of electric and activation mode on coagulation size and geometry

PURPOSE

Radiofrequency ablation (RFA) is increasingly being used to treat unresectable liver tumors. Complete ablation of the tumor and a safety margin is necessary to prevent local recurrence. With current electrodes, size and shape of the ablation zone are highly variable leading to unsatisfactory local recurrence rates, especially for tumors >3 cm. In order to improve predictability, we recently developed a system with four simple electrodes with complete ablation in between the electrodes. This rather small but reliable ablation zone is considered as a building block for matrix radiofrequency ablation (MRFA). In the current study we explored the influence of the electric mode (monopolar or bipolar) and the activation mode (consecutive, simultaneous or switching) on the size and geometry of the ablation zone.

MATERIALS AND METHODS

The four electrode system was applied in ex vivo bovine liver. The electric and the activation mode were changed one by one, using constant power of 50 W in all experiments. Size and geometry of the ablation zone were measured. Finite element method (FEM) modelling of the experiment was performed.

RESULTS

In ex vivo liver, a complete and predictable coagulation zone of a 3 × 2 × 2 cm block was obtained most efficiently in the bipolar simultaneous mode due to the combination of the higher heating efficacy of the bipolar mode and the lower impedance by the simultaneous activation of four electrodes, as supported by the FEM simulation.

CONCLUSIONS

In ex vivo liver, the four electrode system used in a bipolar simultaneous mode offers the best perspectives as building block for MRFA. These results should be confirmed by in vivo experiments.

Multiparametric MR for non-invasive evaluation of tumour tissue histological characteristics after radionuclide therapy

Early non-invasive tumour therapy response assessment requires methods sensitive to biological and physiological tumour characteristics. The aim of this study was to find and evaluate magnetic resonance imaging (MRI) derived tumour tissue parameters that correlate with histological parameters and that reflect effects of radionuclide therapy. Mice bearing a subcutaneous human small-intestine neuroendocrine tumour were i.v. injected with ¹⁷⁷ Lu-octreotate. MRI was performed (7 T Bruker Biospec) on different post-therapy intervals (1 and 13 days) using T2-weighted imaging, mapping of T2* and T1 relaxation time constants, as well as diffusion and dynamic contrast enhancement (DCE-MRI) techniques. After MRI, animals were killed and tumours excised. Four differently stained histological sections of the most central imaged tumour plane were digitized, and segmentation techniques were used to produce maps reflecting fibrotic and vascular density, apoptosis, and proliferation. Histological maps were aligned with MRI-derived parametric maps using landmark-based registration. Correlations and predictive power were evaluated using linear mixed-effects models and cross-validation, respectively. Several MR parameters showed statistically significant correlations with histological parameters. In particular, three DCE-MRI-derived parameters reflecting capillary function additionally showed high predictive power regarding apoptosis (2/3) and proliferation (1/3). T1 could be used to predict vascular density, and perfusion fraction derived from diffusion MRI could predict fibrotic density, although with lower predictive power. This work demonstrates the potential to use multiparametric MRI to retrieve important information on the tumour microenvironment after radiotherapy. The non-invasiveness of the method also allows longitudinal tumour tissue characterization. Further investigation is warranted to evaluate the parameters highlighted in this study longitudinally, in larger studies, and with additional histological methods.

Automatic deformable registration of histological slides to μCT volume data

Localizing a histological section in the three-dimensional dataset of a different imaging modality is a challenging 2D-3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (μCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching μCT slice was off the ground-truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 μm in jaw bone datasets, and the deformable approach improved registration by 33 μm with respect to the median distance error for four histological slides in the cerebellum dataset.

Reconstruction of cochlea based on micro-CT and histological images of the human inner ear

The study of the normal function and pathology of the inner ear has unique difficulties as it is inaccessible during life and, so, conventional techniques of pathologic studies such as biopsy and surgical excision are not feasible, without further impairing function. Mathematical modelling is therefore particularly attractive as a tool in researching the cochlea and its pathology. The first step towards efficient mathematical modelling is the reconstruction of an accurate three dimensional (3D) model of the cochlea that will be presented in this paper. The high quality of the histological images is being exploited in order to extract several sections of the cochlea that are not visible on the micro-CT (mCT) images (i.e., scala media, spiral ligament, and organ of Corti) as well as other important sections (i.e., basilar membrane, Reissner membrane, scala vestibule, and scala tympani). The reconstructed model is being projected in the centerline of the coiled cochlea, extracted from mCT images, and represented in the 3D space. The reconstruction activities are part of the SIFEM project, which will result in the delivery of an infrastructure, semantically interlinking various tools and libraries (i.e., segmentation, reconstruction, and visualization tools) with the clinical knowledge, which is represented by existing data, towards the delivery of a robust multiscale model of the inner ear.