The three-dimensional visualization of medical patient images, obtained in particular by means of computed tomography (CT) or magnetic resonance imaging (MRI), is increasingly facilitating orientation for physicians, e. g. in diagnostics or preoperatively. Today, modern volume rendering methods are used for this purpose, with the help of which 3D images are generated from the medical image data. A novel approach in this context is cinematic rendering (CR), a physically based volume rendering method. Here, the light paths of photons on the different tissue types present in the CT or MRI image are calculated with emission and absorption by the so-called Monte Carlo method [1]. This results in the formation of photorealistic images, which can subsequently be adjusted and enhanced by different transfer functions and illuminations to highlight the different tissue character (tissue radio densities) (Fig. 1) [2—5]. The exact modes of operation are detailed in [6].

Fig. 1. CR window leveling through the thorax of a 91-year-old body donor.

a — the postoperative persistent cerclages of the sternum are clearly visible after digital removal of the upper skin and muscle layer (green arrows); b — the artificial aortic valve replacement (upper blue arrow) is clearly visible in the patient’s heart. The lower blue arrow indicates the sclerotic changes within the coronary artery.

Initial studies on medical students have already been able to show that Cinemtatic Rendering is not only beneficial for medical professionals in diagnostics and therapy such as surgeons, who were able to understand and penetrate the clinical anatomy of the patient faster and better using CR visualization in contrast to pure CT imaging [7], but is also perceived to be beneficial in the education of dental and human medical students [6, 8]. However, it must also be said in a limiting manner that the technology is not yet fully mature. For example, it is difficult to visualize nerve structures well, which have little imaging capability in the tissues. In contrast, pathological changes, such as those triggered by the SARS-CoV-2 virus in the patient’s lung, can be visualized excellently [9]. In the future, a great deal can be expected for clinical anatomy from this new methodology, which was developed by Siemens and is now also being marketed commercially by Siemens [10].

The second new 3D visualization method comes from the histological field. It should generally be noted that the preparation of tissue for histology requires extensive chemical and mechanical pretreatment. If the sections are subsequently available and digitized by means of a scanner, they consequently exhibit many artifacts. These include, for example, color intensity inhomogeneities, low contrast, and nonlinear tissue deformations due to the cutting process on the microtome [11]. All of these have a negative impact on the digital reconstruction result. Therefore, a simple stacking of the images does not lead to the desired result. For this reason, various preprocessing steps must be performed to minimize artifacts as much as possible and to finally lead to a digital reconstruction of the true morphology of the original tissue (tissue block). For this purpose, intensity artifacts are first undone by histogram matching methods suitable for image sequences, resulting in standardized intensity images. Then, the tissue slices in each individual slice sequence are aligned using an automated software program called rigid registration. This is important to match the different orientation of the tissue within each slice. Then, nonlinear tissue deformations caused by sectioning and further processing for histology are reversed using nonrigid registration procedures. In this process, the procedures calculate translation vectors for each image pixel so that it ideally matches the corresponding image pixels of the two neighboring images (neighbor slices). The histological images thus corrected are then processed and superimposed, and can also be annotated with the software to obtain a final volume image of the annotated structure or a 3D image of the original tissue. This can also be visualized by volume rendering. All described steps of the reconstruction process are summarized in the 3D reconstruction application (software) HiD = HistoDigital. Finally, the spatial relationships are precisely completed by the annotation tool of the Chimaera AI-B2 client (Fig. 2). Examples of tissue blocks reconstructed with HiD have already been published by [12, 13]. However, HiD software is actually not yet available, but it is planned to release the application in spring this year and make it commercially available to a broad user community. HiD will not only be able to 3D reconstruct histological sections but also immunohistochemically antibody-labeled structures (e.g., with a fluorescence-coupled antibody against lymphatic vessels) within a tissue block and thus to determine conclusions about the volumetric distribution of a desired (in this case, lymphatic vessels labeled with an antibody against VEGF-C) structure.

Fig. 2. Histological 2D section of a fetal kidney (а) and digital 3D reconstruction of serial sections of the entire kidney and surrounding tissue (b).

The fact that the entire procedure is not trivial is shown by the fact that there is a commercially available 3D reconstruction software for histological sections so far [14], but it is hardly used because the problems described above in the creation of histological 3D reconstructions are only insufficiently solved here. HiD overcomes this problem and in the future will not only be an option for scientific questions but will also provide further options for histology and pathology teaching (virtual 3D histology/pathology) and routine pathology (e.g. in the context of checking resection margins in surgical tumor removal).

The authors declare no conflicts of interest.