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Objectives: To present a publicly available torso segmentation network for large epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images. Materials & Methods: We extracted preliminary segmentations from TotalSegmentator, spine, and body composition networks for VIBE images, then improved them iteratively and retrained a nnUNet network. Using subsets of NAKO (85 subjects) and UK Biobank (16 subjects), we evaluated with Dice-score on a holdout set (12 subjects) and existing organ segmentation approach (1000 subjects), generating 71 semantic segmentation types for VIBE images. We provide an additional network for the vertebra segments 22 individual vertebra types. Results: We achieved an average Dice score of 0.89 +- 0.07 overall 71 segmentation labels. We scored > 0.90 Dice-score on the abdominal organs except for the pancreas with a Dice of 0.70. Conclusion: Our work offers a detailed and refined publicly available full torso segmentation on VIBE images., Comment: https://github.com/robert-graf/TotalVibeSegmentator

Purpose: To develop an open-source and easy-to-use segmentation model that can automatically and robustly segment most major anatomical structures in MR images independently of the MR sequence. Materials and Methods: In this study we extended the capabilities of TotalSegmentator to MR images. 298 MR scans and 227 CT scans were used to segment 59 anatomical structures (20 organs, 18 bones, 11 muscles, 7 vessels, 3 tissue types) relevant for use cases such as organ volumetry, disease characterization, and surgical planning. The MR and CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, pathologies, scanners, body parts, sequences, contrasts, echo times, repetition times, field strengths, slice thicknesses and sites). We trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients (Dice) to evaluate the model's performance. Results: The model showed a Dice score of 0.824 (CI: 0.801, 0.842) on the test set, which included a wide range of clinical data with major pathologies. The model significantly outperformed two other publicly available segmentation models (Dice score, 0.824 versus 0.762; p<0.001 and 0.762 versus 0.542; p<0.001). On the CT image test set of the original TotalSegmentator paper it almost matches the performance of the original TotalSegmentator (Dice score, 0.960 versus 0.970; p<0.001). Conclusion: Our proposed model extends the capabilities of TotalSegmentator to MR images. The annotated dataset (https://zenodo.org/doi/10.5281/zenodo.11367004) and open-source toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.

We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration. Unlike other methods, our method uses compact patches of a constant small size to derive solutions that can combine global transformations with local deformations. This approach minimizes the memory footprint of the GPU during training, but also enables us to operate on numerous amounts of randomly overlapping small patches during inference to mitigate image and patch boundary problems. PatchMorph adeptly handles world coordinate transformations between two input images, accommodating variances in attributes such as spacing, array sizes, and orientations. The spatial resolution of patches transitions from coarse to fine, addressing both global and local attributes essential for aligning the images. Each patch offers a unique perspective, together converging towards a comprehensive solution. Experiments on human T1 MRI brain images and marmoset brain images from serial 2-photon tomography affirm PatchMorph's superior performance., Comment: This work has been submitted to the IEEE for possible publication

Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where 3D images are common, the problems are apparent. A typical approach to solve this limitation is to break the task into smaller subtasks by dividing images into smaller image patches. Another approach, if applicable, is to look at the 2D image sections separately, and to solve the problem in 2D. Often, the loss of global context makes such approaches less effective; important global information might not be present in the current image patch, or the selected 2D image section. Here, we propose Deep Neural Patchworks (DNP), a segmentation framework that is based on hierarchical and nested stacking of patch-based networks that solves the dilemma between global context and memory limitations.

Filter-exchange imaging (FEXI) has already been utilized in several biomedical studies for evaluating the permeability of cell membranes. The method relies on suppressing the extracellular signal using strong diffusion weighting (the mobility filter causing a reduction in the overall diffusivity) and monitoring the subsequent diffusivity recovery. Using Monte Carlo (MC) simulations, we demonstrate that FEXI is not uniquely sensitive to the transcytolemmal exchange but also to the geometry of involved compartments: Complicated geometry offers locations where spins remain unaffected by the mobility filter; moving to other locations afterward, such spins contribute to the diffusivity recovery without actually permeating any membrane. This exchange mechanism challenges the interpretation of FEXI in complex media such as brain gray matter and opens large room for investigation towards crystallizing the genuine membrane permeation and characterizing the compartment geometry., Comment: 9 pages with 7 figures

Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network architecture and abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a data-driven framework to optimize and validate parameters of dMRI-based fiber-tracking algorithms using neural tracer data as a reference. Japan's Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. We considered four criteria for goodness of fiber tracking: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage, applied using a multi-objective optimization algorithm. We implemented a variant of non-dominated sorting genetic algorithm II (NSGA-II) to optimize five major parameters of a global fiber-tracking algorithm over multiple brain samples in parallel. Using optimized parameters compared to the default parameters, dMRI-based fiber tracking performance was significantly improved, while minimizing false positives and impossible cross-hemisphere connections. Parameters optimized for 10 tracer injection sites showed good generalization capability for other brain samples. These results demonstrate the importance of data-driven adjustment of fiber-tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

Diffusion-weighted magnetic resonance imaging in brain white matter probes tissue microstructure and allows for the estimation of compartmental diffusion parameters. Recently, it became apparent that traditional single-direction diffusion encodings are not fully sufficient to resolve the white matter compartmental diffusivities. Multiple diffusion encodings have been suggested to make the problem less ambiguous, however, it still remained unclear whether such protocols would completely solve the problem. Here, we constructively prove that a combination of linear and planar diffusion encodings is enough to determine the parameters of the three compartment white matter model.

In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract direction and tract presence. The automatic learning of fiber tracts based on diffusion MRI data is a rather new idea, which tries to overcome limitations of atlas-based techniques. HAMLET takes a such an approach. Unlike the current trend in machine learning, HAMLET has only a small number of free parameters HAMLET is based on spherical tensor algebra which allows a translation and rotation covariant treatment of the problem. HAMLET is based on a repeated application of convolutions and non-linearities, which all respect the rotation covariance. The intrinsic treatment of such basic image transformations in HAMLET allows the training and generalization of the algorithm without any additional data augmentation. We demonstrate the performance of our approach for twelve prominent bundles, and show that the obtained tract estimates are robust and reliable. It is also shown that the learned models are portable from one sequence to another.

Biophysical modeling is the mediator of evaluating the cellular structure of biological tissues using diffusion-weighted MRI. It is however the bottleneck of microstructural MRI. Beyond the complexity of diffusion, the current development is hindered by the fact that biophysical models heavily rely on diffusion-specific properties of diverse cellular compartments that are still unknown and must be measured in vivo. Obtaining such parameters by straightforward fitting is hindered by the degenerated landscape of the likelihood functions, in particular, the signal obtained for multiple diffusion directions and moderate diffusion weighting strength is not enough to estimate these parameters: different parameter constellations explain the signal equally well. The aim of this study is to measure the central parameter of white matter models, namely the intra-axonal water diffusivity in the normal human brain. Proper estimation of this parameter is complicated due to (i) the presence of both intra- and extra-axonal water compartments and (ii) the orientation dispersion of axons. Our measurement involves an efficient suppression of extra-axonal space and all cellular processes oriented outside a narrow cone around the principal fiber direction. This is achieved using a planar water mobility filter -- a strong diffusion weighting that suppresses signal from all molecules that are mobile in the plane transverse to the fiber bundle. Following the planar filter, the diffusivity in the remaining compartment is measured using linear and isotropic weighting. We find the specifically averaged intra-axonal diffusivity $D_0 = 2.25\pm 0.03{\,\rm \mu m^2/ms}$ for the timing of the applied gradients. Extrapolation to the infinite diffusion time gives $D_\infty \approx 2.0{\,\rm \mu m^2/ms}$. This result imposes a strong limitation on the parameter selection for biophysical modeling of diffusion-weighted MRI.