In recent years, bio-medical research has made tremendous progress regarding imaging modalities and techniques for accurate patient diagnostics. On the other hand, these staggering amounts of imaging data from various modalities pose new challenges. Additionally, the underlying biological mechanisms that bring forth macroscopic image changes remain uncertain. Patient biopsies or autopsies can offer valuable insights, but remain rare and precious. A way to bridge this knowledge gap is to establish a preclinical model for direct correlation of biological tissue properties and image features. To exploit the full potential of such experiments, methods are required for the fusion of multi-modal bio-medical imaging data, i.e. computed tomography (CT), magnetic resonance imaging (MRI), simulated dose distributions, and anatomical atlas data with histology. We irradiated mouse brain subvolumes with different doses and investigated the radiobiological response. All animals received a CT prior to radiation, which was used for a Monte-Carlo beam transport simulation to obtain the spatial dose-distribution. A brain atlas was co-aligned with the CT to resolve regional anatomical variances. Brains were excised after follow-up for histological evaluation; depending on the specific research question different stainings were applied such as H&E (general morphology), DNA damage (γH2AX), and cell-type specific markers (e.g. NeuN for neurons). To enable analysis with high spatial resolution, 30 – 40 planes were acquired per brain using a Zeiss AxioScan slide scanner for high throughput imaging. We established and implemented a robust and easy-to-use registration work-flow to fuse the generated data within reasonable time. Our tandem talk will focus on the recent progress of this project. First, we had to accomplish high-precision irradiation of mouse brain subvolumes. This was achieved with the in-house developed software RadiAIDD (github.com/jo-mueller/RadiAIDD), which combines different imaging data for optimal positioning , . To analyze the biological radiation effect, we developed a fast and reliable algorithm for cell counting based on a spot detection (github.com/Theresa-S/Cell-ratio-detection)  and created a pipeline to align stacked 2D histological images, 3D imaging data (CT, MRI; github.com/jo-mueller/Slice2Volume), dose simulations, and volumetric atlases. With this platform, we can unravel the missing dimension from clinical data and apply this knowledge to improve patient care.
 J. Müller et al., “Proton radiography for inline treatment planning and positioning verification of small animals,” Acta Oncol. (Madr)., vol. 56, no. 11, pp. 1399–1405, 2017, doi: 10.1080/0284186X.2017.1352102.