Hesser, J. | Search bioRxiv

bioRxiv2026

From whole-slide histology to ADC maps: Fast diffusion MRI simulation with neural operators

Background and ObjectiveSimulation of diffusion MRI signals from tissue microstructure is a fundamental problem in quantitative imaging, as it enables controlled study of how cellular architecture influences measured signals. However, physics-based simulations at clinically relevant scales are challenging due to a scale mismatch between imaging and histology: clinical diffusion MRI spans centimeter-scale fields of view with millimeter-scale voxels, whereas histology resolves structure at micrometer scales. Capturing voxel-wise signal formation therefore requires repeated simulations over heterogeneous microstructure, which becomes computationally and memory intensive in classical solvers. We propose a neural operator framework that amortizes this cost by learning local microstruc-ture-signal mappings once and applying them across large tissue regions. MethodsWe train a Fourier Neural Operator on finite-element simulations of histology-derived cell segmentations to predict magnetization fields from diffusivity and permeability maps. The model is embedded in a subdomain tiling strategy that enables scalable inference over whole-slide histology images. Unlike most conventional simulation pipelines, inference operates directly on regular grids derived from cell segmentations and does not require meshing. ResultsThe proposed framework enables simulation of apparent diffusion coefficient maps over 2D liver histology spanning 28.224 mm x 18.144 mm. It achieves over 2,600-fold acceleration compared with CPU-based finite-element simulation, reducing runtime from an estimated 217 days to under 2 hours. The network yields mean relative signal errors of 0.34%-0.43% at high diffusion weighting and 0.03% at low diffusion weighting, with maximum errors below 5%. On manually segmented datasets with greater morphological variability, mean errors increased slightly to 1.37%-1.79%. ConclusionsNeural operators enable computationally practical, mesh-free diffusion MRI simulation by amortizing expensive physics-based computation into a reusable operator applied across local sub-domains. This makes large-scale histology-based diffusion MRI modeling feasible while preserving high accuracy.

biophysics↗

bioRxiv2026

Large-domain histology-based diffusion MRI simulation via independent local simulations

Diffusion MRI simulations based on realistic tissue microstructure provide a means to validate biophysical models and optimize acquisition protocols, but their computational cost restricts most studies to domains far smaller than a clinical voxel. The objective of this study was to develop an automated and scalable framework that converts whole-slide histology into diffusion MRI simulations at clinically relevant spatial scales while remaining feasible on standard workstation hardware. We present an end-to-end pipeline integrating two-dimensional whole-slide cell segmentation, mesh generation, and finite element Bloch-Torrey simulation. To enable simulations at large spatial scales without prohibitive memory growth, we introduce a subdomain tiling strategy in which the tissue domain is partitioned into extended subdomains simulated independently under no-flux boundary conditions. Signals are aggregated only from the central regions of each subdomain to minimize boundary artifacts. For an 800 {micro}m x 800 {micro}m histology-based domain, the aggregated signal differed by 0.07% from the corresponding full-domain finite element simulation while reducing wall-clock time from several days to hours and maintaining bounded memory usage independent of global domain size. When applied to a 2016 {micro}m x 2016 {micro}m heterogeneous region approximating the in-plane dimensions of a clinical voxel, the apparent diffusion coefficient obtained from the full domain differed from values computed in smaller dense and sparse subregions, demonstrating the influence of structural heterogeneity at clinically relevant scales on derived diffusion metrics. The proposed framework establishes an automated and memory-stable approach for generating diffusion MRI simulations directly from routine histology.

biophysics↗

bioRxiv2025

Post-operative tissue fragment puzzling using histopathological vision transformer alignment HiViTAlign

1In pathology, reconstructing adjacent tissue parts enables an overview of the macro environment of objects like tumors. Especially, malignoma are of interest to verify invasion and resection margins, as patients with positive margins face a higher mortality risk. Reassembling image fragments is widely used in other domains, but adjacent blocks in pathology are mostly analyzed separately missing global context. In this project, neighboring tissue of pig organ whole slide images (WSI) are reconstructed without a ground truth based on histological sections at the end of a complex work-up process. Histological tissue slices with artifacts, frayed or disrupted boundaries and sometimes missing pieces complicate the puzzling task. Thus, typical approaches such as direct feature comparison of tissue boundaries or estimating a tiles position based on an overview image or a known structures are not applicable. A new approach is presented using partial image registration where only parts of a fixed and a moving image are aligned for adjacency. In contrast to existing projects aligning subsequent tissue slices of the same block, WSIs from separated blocks will be reassembled for adjacency. The used three stage vision transformer extracts image features on various scales, compares neighboring tiles by shape, color and texture and predicts transformation parameters. Even though the pipeline is capable of handling rigid transformation such as rotation or reflection, only translation is currently supported due to the limited training set. Supervised training of the network can be realized using a puzzle generator creating irregular shaped fragments of masked whole slide images. The factorized trained neural network is embedded into a sophisticated histopathological vision transformer alignment (HiViTAlign) pipeline executing the following steps in roughly 10 seconds per reassembled tissue puzzle: First, extract the specimen and mask the background in each whole slide image. Second, compare tile boundaries using partial image registration. Third, calculate the adjacency by boundary proximity for each image pair. Fourth, determine a minimal spanning tree to optimize adjacency of pairwise registrations and transformations for tissue reconstruction. The python source code for HiViTAlign to start puzzling with WSIs or other objects is available at https://github.com/cpheidelberg/HiViTAlign. The generator for creating a dataset with irregular shaped tiles can be downloaded from https://github.com/cpheidelberg/ImagePuzzleGenerator. 2 Author summaryHistopathology as the microscopic analysis of tissue remains the gold standard for evaluating tumors, especially when assessing resection margins. However, the physical processing of tissue disrupts its original three dimensional structure, leaving pathologists with fragmented, two-dimensional slices that lack spatial context. This fragmentation makes it difficult to understand the full extent and orientation of tumors and to correlate pathology results with radiological imaging used in surgical planning. In this study, we present a computational pipeline for histopathological vision transformer alignment (HiViTAlign) that reassembles fragmented histological tissue sections, similar to solving a jigsaw puzzle. Using a deep learning model based on Vision Transformers, our method predicts how individual tissue fragments are spatially related and outputs transformation parameters for adjacency. While the pipeline is designed to accommodate a variety of rigid transformations (e.g., rotation and scaling), its current implementation, constrained by the limited diversity of the training dataset, focuses solely on predicting translational shifts between fragments. A custom dataset generator was developed to create realistic puzzles from whole slide images, assigning original coordinates to each fragment to enable supervised training. The full pipeline was evaluated on both synthetic datasets and real-world whole slide images, demonstrating its ability to reconstruct tissue cross-sections without requiring a reference image. This method may support more accurate spatial interpretation of pathological specimens and better integration with surgical imaging data. The open-source Python code, we developed, invites collaboration and innovation, reflecting our commitment to advancing computational pathology through technology and shared resources. Paper to be submitted to PLOS Computational Biology.

pathology↗