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Biology subjects

Russmann, C.

Publications and source records attributed to Russmann, C..

2 recordsLinked to original sources

X-Ray contrast-adjustable 3D printing for multimodal fusion of microCT and histology

ObjectPhantoms and reference structures are essential tools for calibration and correlative imaging in pre-clinical and research applications of X-Ray-based imaging. They serve as reference standards, ensuring consistency and accuracy in imaging results. However, generating individual phantoms often involves a complex creation process, high production costs, and significant time investment. MethodConic reference structures were 3D printed using a mixture of UV-curable resin and X-Ray contrast agents. These structures were then embedded together with lung specimens of SARS-CoV-2-infected rhesus macaques in a methyl methacrylate-based solution. The polymerized blocks were scanned using propagation-based phase-contrast microCT, a method chosen for its superior ability to enhance contrast especially in low-absorbing biological samples. Utilizing the conic reference structures, subsequently performed histological sections were co-registered into the 3D context of the microCT data sets. ResultThe produced 3D-printing models were highly visible in terms of contrast and detail in both imaging methods, allowing for a precise co-registration of microCT and histological imaging. ConclusionThe novel methodology of using contrast agents and resin in 3D-printing enables the generation of customizable, contrast-specific phantoms and reference structures. These can be straightforwardly segmented from the embedding material, significantly simplifying and enhancing the workflow of multimodal imaging processes. In this study, 3D printed conic reference structures were effectively used to automate and streamline the precise multimodal fusion of microCT and histological imaging.

bioengineering↗

Registration-based 3D Light Sheet Fluorescence Microscopy and 2D histology image fusion tool for pathological specimen

BackgroundHistological analysis traditionally relies on thin tissue sections, providing inherently two-dimensional (2D) information. However, this approach captures only a fraction of the entire sample and lacks the spatial context nec-essary for comprehensive tissue assessment. Recent advancements in multimodal imaging have introduced the fusion of histological data with three-dimensional (3D) imaging techniques, such as Light Sheet Fluorescence Microscopy (LSFM), to enhance tissue analysis by integrating complementary spatial information. A key challenge in this fusion process is the accurate alignment of corresponding structures across modalities, which is complicated by differences in resolution, sectioning-induced deformations, and varying imaging orientations. Existing solu-tions often require manual selection of image pairs or technical expertise, limiting accessibility to non-specialist users. MethodsTo address these limitations, we introduce LitSHi (Light Sheet meets Histology), a novel registration tool that enables the automated and precise align-ment of LSFM and histological images. LitSHi allows multimodal image fusion to be performed fully automatically, which significantly reduces the need for manual intervention. ResultsWe evaluated LitSHi on testicular tumor specimens, demonstrating its ability to achieve enhanced structural correspondence between LSFM and histological images. The automated registration process significantly improved efficiency and alignment accuracy compared to traditional manual or semi-automated approaches. ConclusionLitSHi could improve digital pathology by optimizing multimodal tissue analysis and supporting future developments in computational pathology and AI-driven diagnostics.

bioengineering↗