Registration Pipeline Research Articles

Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualisation of Bone Defects

ABSTRACT Magnetic Resonance Imaging (MRI) is a medical imaging modality that allows for the evaluation of soft-tissue diseases and the assessment of bone quality. Preoperative MRI volumes are used by surgeons to identify defected bones, perform the segmentation of lesions, and generate surgical plans before the surgery. Nevertheless, conventional intraoperative imaging modalities such as fluoroscopy are less sensitive in detecting potential lesions. In this work, we propose a 2D/3D registration pipeline that aims to register preoperative MRI with intraoperative 2D fluoroscopic images. To showcase the feasibility of our approach, we use the core decompression procedure as a surgical example to perform 2D/3D femur registration. The proposed registration pipeline is evaluated using digitally reconstructed radiographs (DRRs) to simulate the intraoperative fluoroscopic images. The resulting transformation from the registration is later used to create overlays of preoperative MRI annotations and planning data to provide intraoperative visual guidance to surgeons. Our results suggest that the proposed registration pipeline is capable of achieving reasonable transformation between MRI and digitally reconstructed fluoroscopic images for intraoperative visualisation applications.

Tensor image registration library: Deformable registration of stand‐alone histology images to whole‐brain post‐mortem MRI data

BackgroundAccurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data. MethodsOur pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. ResultsAll 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2–0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. ConclusionsOur open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.

OCT-guided Robotic Subretinal Needle Injections: A Deep Learning-Based Registration Approach.

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina's fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework's capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8μm.

Homologous point transformer for multi-modality prostate image registration.

Registration is the process of transforming images so they are aligned in the same coordinate space. In the medical field, image registration is often used to align multi-modal or multi-parametric images of the same organ. A uniquely challenging subset of medical image registration is cross-modality registration-the task of aligning images captured with different scanning methodologies. In this study, we present a transformer-based deep learning pipeline for performing cross-modality, radiology-pathology image registration for human prostate samples. While existing solutions for multi-modality prostate image registration focus on the prediction of transform parameters, our pipeline predicts a set of homologous points on the two image modalities. The homologous point registration pipeline achieves better average control point deviation than the current state-of-the-art automatic registration pipeline. It reaches this accuracy without requiring masked MR images which may enable this approach to achieve similar results in other organ systems and for partial tissue samples.

Pattern-Based Cloth Registration and Sparse-View Animation

We propose a novel multi-view camera pipeline for the reconstruction and registration of dynamic clothing. Our proposed method relies on a specifically designed pattern that allows for precise video tracking in each camera view. We triangulate the tracked points and register the cloth surface in a fine-grained geometric resolution and low localization error. Compared to state-of-the-art methods, our registration exhibits stable correspondence, tracking the same points on the deforming cloth surface along the temporal sequence. As an application, we demonstrate how the use of our registration pipeline greatly improves state-of-the-art pose-based drivable cloth models. Furthermore, we propose a novel model, Garment Avatar , for driving cloth from a dense tracking signal which is obtained from two opposing camera views. The method produces realistic reconstructions which are faithful to the actual geometry of the deforming cloth. In this setting, the user wears a garment with our custom pattern which enables our driving model to reconstruct the geometry. Our code and data are available at https://github.com/HalimiOshri/Pattern-Based-Cloth-Registration-and-Sparse-View-Animation. The released data includes our pattern and registered mesh sequences containing four different subjects and 15k frames in total.

Spatiotemporal Atlas of the Fetal Brain Depicts Cortical Developmental Gradient.

The fetal brains experience rapid and complex development in utero during the second and third trimesters. In utero MRI of the fetal brain in this period enables us to quantify normal fetal brain development in the spatiotemporal domain. In this study, we established a high-quality spatiotemporal atlas between 23 and 38 weeks gestational age (GA) from 90 healthy Chinese human fetuses of both sexes using a pairwise and groupwise registration pipeline. We quantified the fetal cortical morphology indices and characterized their spatiotemporal developmental pattern. The cortical thickness exhibited a biphasic pattern that first increased and then decreased; the curvature fitted well into the Gompertz growth model; sulcal depth increased linearly, while surface area expanded exponentially. The cortical thickness and curvature trajectories consistently pointed to a characteristic time point around GA of 31 weeks. The characteristic GA and growth rate obtained from individual cortical regions suggested a central-to-peripheral developmental gradient, with the earliest development in the parietal lobe, and we also observed a superior-to-inferior gradient within the temporal lobe. These findings may be linked to biophysical events, such as dendritic arborization and thalamocortical fibers ingrowth. The proposed atlas was also compared with an existing fetal atlas from a white/mixed population. Finally, we examined the structural asymmetry of the fetal brains and found extensive asymmetry that dynamically changed with development. The current study depicted a comprehensive profile of fetal cortical development, and the established atlas could be used as a normative reference for neurodevelopmental and diagnostic purposes, especially in the Chinese population.SIGNIFICANCE STATEMENT We generated a high-quality 4D spatiotemporal atlas of the normal fetal brain development from 23 to 38 gestational weeks in a Chinese population and characterized the spatiotemporal developmental pattern of cortical morphology. According to the cortical development trajectories, the fetal cerebral cortex development follows a central-to-peripheral developmental gradient that may be related to the underlying cellular events. The majority of cortical regions already exhibit significant asymmetry during the fetal period.

Inter-day repeatability assessment of human retinal blood flow using clinical laser speckle contrast imaging.

Laser speckle contrast imaging (LSCI) can generate retinal blood flow maps inexpensively and non-invasively. These flow maps can be used to identify various eye disorders associated with reduced blood flow. Despite early success, one of the major obstacles to clinical adoption of LSCI is poor repeatability of the modality. Here, we propose an LSCI registration pipeline that registers contrast maps to correct for rigid movements. Post-registration, intra(same)-day and inter(next)-day repeatability are studied using various quantitative metrics. We have studied LSCI repeatability intra-day by using the coefficient of variation. Using the processing pipelines and custom hardware developed, similar repeatability was observed when compared to previously reported values in the literature. Inter-day repeatability analysis indicates no statistical evidence (p = 0.09) of a difference between flow measurements performed on two independent days. Further improvements to hardware, environmental controls, and participant control must be made to provide higher confidence in the repeatability of blood flow. However, this is the first time that repeatability across two different days (inter-day) using multiple exposure speckle imaging (MESI) has been analyzed and reported.

G-RMOS: GPU-accelerated Riemannian Metric Optimization on Surfaces

Surface mapping is used in various brain imaging studies, such as for mapping gray matter atrophy patterns in Alzheimer’s disease. Riemannian metrics on surface (RMOS) is a state-of-the-art surface mapping algorithm that optimizes Riemannian metrics to establish one-to-one correspondences between surfaces in the Laplace–Beltrami embedding space. However, owing to the complex calculation with accurate one-to-one correspondences, RMOS registration takes a long time. In this study, we propose G-RMOS, a graphics processing unit (GPU)-accelerated RMOS registration pipeline that uses three GPU kernel design strategies: 1. using GPU computing capability with a batch scheme; 2. using the cache in the GPU block to minimize memory latency in register and shared memory; and 3. maximizing the effective number of instructions per GPU cycle using instruction level parallelism. Using the experimental results, we compare the acceleration speed of the G-RMOS framework with that of RMOS using hippocampus and cortical surfaces, and show that G-RMOS achieves a significant speedup in surface mapping. We also compare the memory requirements for cortical surface mapping and show that G-RMOS uses less memory than RMOS.

Design of Area-Power Efficient Parallel Fir Filter with Mux Based Full Adder

Abstract: In this article, here we propose a new architecture of hardware for a high-speed finite impulse response (FIR) filter using seamless fine-grained pipelining. This proposed parallel full pipeline FIR filter can generate an output sample in a limited gate delays by fixing the pipeline registers in between components and also across the components. A precise critical path analysis at the gate level allows to create a suitable pipelining strategy depending on the throughput. This paper also presents modified full adder, based on multiplexers which establishes trade-offs in terms of area, power and delay. The advance FIR filters are incorporate to measure the balance between complexity and speed, and also maximum throughput. The effectiveness of the proposed method is synthesized and simulated using Xilinx Vivado 2018.3 along with improvement of area, delay and power consumption.

Development of a new toolbox for mouse PET–CT brain image analysis fully based on CT images and validation in a PD mouse model

Automatic analysis toolboxes are popular in brain image analysis, both in clinical and in preclinical practices. In this regard, we proposed a new toolbox for mouse PET–CT brain image analysis including a new Statistical Parametric Mapping-based template and a pipeline for image registration of PET–CT images based on CT images. The new templates is compatible with the common coordinate framework (CCFv3) of the Allen Reference Atlas (ARA) while the CT based registration step allows to facilitate the analysis of mouse PET–CT brain images. From the ARA template, we identified 27 volumes of interest that are relevant for in vivo imaging studies and provided binary atlas to describe them. We acquired 20 C57BL/6 mice with [18F]FDG PET–CT, and 12 of them underwent 3D T2-weighted high-resolution MR scans. All images were elastically registered to the ARA atlas and then averaged. High-resolution MR images were used to validate a CT-based registration pipeline. The resulting method was applied to a mouse model of Parkinson’s disease subjected to a test–retest study (n = 6) with the TSPO-specific radioligand [18F]VC701. The identification of regions of microglia/macrophage activation was performed in comparison to the Ma and Mirrione template. The new toolbox identified 11 (6 after false discovery rate adjustment, FDR) brain sub-areas of significant [18F]VC701 uptake increase versus the 4 (3 after FDR) macro-regions identified by the Ma and Mirrione template. Moreover, these 11 areas are functionally connected as found by applying the Mouse Connectivity tool of ARA. In conclusion, we developed a mouse brain atlas tool optimized for PET–CT imaging analysis that does not require MR. This tool conforms to the CCFv3 of ARA and could be applied to the analysis of mouse brain disease models.

Observation framework of errors in microprocessors with machine learning location inference of radiation-induced faults

This paper presents a non-intrusive failure reason capturing approach for processor-based system-on-a-chip (SoC) integrated as an intellectual property (IP). It provides diagnostic information about the origin of single-event upsets (SEU) in the context of technology qualification to radiations. Thanks to the combination of trace events buffering and error detection with triggering mechanisms, the module is able to capture an execution trace containing the error propagation, using only 1 KB of memory. The execution trace is supplemented by a configurable set of pipeline registers. For single-event functional interrupt (SEFI), we also propose a technique based on a machine-learning algorithm to find from which register the SEU comes from. The captured CPU trace is processed by a classification algorithm, trained on a fault-injection campaign database and providing an accuracy score up to 87 %.

Designing a Custom CPU Architecture Based on Hardware RTOS and Dynamic Preemptive Scheduler

The current trend in real-time operating systems involves executing many tasks using a limited hardware platform. Thus, a single processor system has to execute multiple tasks with different priorities in different real-time system (RTS) work modes. Hardware schedulers can greatly reduce event trigger latency and successfully remove most of the scheduling overhead, providing more computing cycles for applications. In this paper, we present a hardware-accelerated RTOS based on the replication of resources such as program counters, general purpose registers (GPRs) and pipeline registers. The implementation of this new concept, based on real-time event handling implemented in hardware, is intended to meet the current rigorous requirements imposed by critical real-time systems. The most important attribute of this FPGA implementation is the time required for task context switching, which is only one clock cycle or three clock cycles when working with the atomic instructions used in the case of inter-task synchronization and communication mechanisms. The main contribution of this article is its focus on mutexes and the speed of response associated with related events. Thus, fast switching between threads is also validated, considering the handling of events in the hardware using HW_nMPRA_RTOS (HW-RTOS). The proposed architecture implements inter-task synchronization and communication mechanisms with high performance, improving the overall response time when the mutex or message is expected to relate to a higher-priority task.

Does the 3D Feature Descriptor Impact The Registration Accuracy in Laparoscopic Liver Surgery?

Abstract In laparoscopic liver surgery (LLS) image-guided navigation systems could support the surgeon by providing subsurface information such as the positions of tumors and vessels. For this purpose, one option is to perform a registration of preoperative 3D data and 3D surface patches reconstructed from laparoscopic images. Part of an automatic 3D registration pipeline is the feature description, which takes into account various geometric and spatial information. Since there is no leading feature descriptor in the field of LLS, two feature descriptors are compared in this paper: The Fast Point Feature Histogram (FPFH) and Triple Orthogonal Local Depth Images (TOLDI). To evaluate their performance, three perturbations were induced: varying surface patch sizes, spatial displacement, and Gaussian deformation. Registration was performed using the RANSAC algorithm. FPFH outperformed TOLDI for small surface patches and in case of Gaussian deformations in terms of registration accuracy. In contrast, TOLDI showed lower registration errors for patches with spatial displacement. While developing a 3D-3D registration pipeline, the choice of the feature descriptor is of importance, consequently a careful choice suitable for the application in LLS is necessary.

End-to-End Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration

Even though considerable progress has been made in deep learning-based 3D point cloud processing, how to obtain accurate correspondences for robust registration remains a major challenge because existing hard assignment methods cannot deal with outliers naturally. Alternatively, the soft matching-based methods have been proposed to learn the matching probability rather than hard assignment. However, in this paper, we prove that these methods have an inherent ambiguity causing many deceptive correspondences. To address the above challenges, we propose to learn a partial permutation matching matrix, which does not assign corresponding points to outliers, and implements hard assignment to prevent ambiguity. However, this proposal poses two new problems, i.e. existing hard assignment algorithms can only solve a full rank permutation matrix rather than a partial permutation matrix, and this desired matrix is defined in the discrete space, which is non-differentiable. In response, we design a dedicated soft-to-hard (S2H) matching procedure within the registration pipeline consisting of two steps: solving the soft matching matrix (S-step) and projecting this soft matrix to the partial permutation matrix (H-step). Specifically, we augment the profit matrix before the hard assignment to solve an augmented permutation matrix, which is cropped to achieve the final partial permutation matrix. Moreover, to guarantee end-to-end learning, we supervise the learned partial permutation matrix but propagate the gradient to the soft matrix instead. Our S2H matching procedure can be easily integrated with existing registration frameworks, which has been verified in representative frameworks including DCP, RPMNet, and DGR. Extensive experiments have validated our method, which creates a new state-of-the-art performance.

KdO-Net: Towards Improving the Efficiency of Deep Convolutional Neural Networks Applied in the 3D Pairwise Point Feature Matching

In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D pairwise point cloud matching. The classic pipeline of 3D point cloud registration involves two steps, viz., the point feature matching and the globally consistent refinement. We focus on the first step that can be further divided into three parts, viz., the key point detection, feature descriptor extraction, and pairwise-point correspondence estimation. In practical applications, the point feature matching is ambiguous and challenging owing to the low overlap of multiple scans, inconsistency of point density, and unstructured properties. To solve these issues, we propose the KdO-Net for 3D pairwise point feature matching and present a novel nearest neighbor searching strategy to address the computation problem. Thereafter, our method is evaluated with respect to an indoor BundleFusion benchmark, and generalized to a challenging outdoor ETH dataset. Further, we have extended our method over our complicated and low-overlapped TUM-lab dataset. The empirical results graphically demonstrate that our method achieves a superior precision and a comparable feature matching recall to the prior state-of-the-art deep learning-based methods, despite the overlap being less than 30 percent. Finally, we implement quantitative and qualitative ablated experiments and visualization interpretations for illustrating the insights and behavior of our network.

An Effective Method to Identify Microarchitectural Vulnerabilities in GPUs

Graphics Processing Units (GPUs) are increasingly adopted in several domains where reliability is fundamental, such as self-driving cars and autonomous systems. Unfortunately, GPU devices have been shown to have a high error rate, while the constraints imposed by real-time safety-critical applications make traditional (and costly) replication-based hardening solutions inadequate. This work proposes an effective methodology to identify the architectural vulnerable sites in GPUs modules, i.e., the locations that, if corrupted, most affect the correct instructions execution. We first identify, through an innovative method based on Register-Transfer Level (RTL) fault injection experiments, the architectural vulnerabilities of a GPU model. Then, we mitigate the fault impact via selective hardening applied to the flip-flops that have been identified as critical. We evaluate three hardening strategies: Triple Modular Redundancy (TMR), Triple Modular Redundancy against SETs ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> TMR), and Dual Interlocked Storage Cells (Dice flip-flops). The results gathered on a publicly available GPU Model (FlexGripPlus) considering functional units, pipeline registers, and warp scheduler controller show that our method can tolerate from 85% to 99% of faults in the pipeline registers, from 50% to 100% of faults in the functional units and up to 10% of faults in the warp scheduler, with a reduced hardware overhead (in the range of 58% to 94% when compared with traditional TMR). Finally, we adapt the methodology to perform a complementary evaluation targeting permanent faults and identify critical sites prone to propagate fault effects across the GPU. We found that a considerable percentage (65% to 98%) of flip-flops that are critical for transient faults are also critical for permanent faults.

MU-NET: A MULTISCALE UNSUPERVISED NETWORK FOR REMOTE SENSING IMAGE REGISTRATION

Abstract. Registration for multi-sensor or multi-modal image pairs with a large degree of distortions is a fundamental task for many remote sensing applications. To achieve accurate and low-cost remote sensing image registration, we propose a multiscale unsupervised network (MU-Net). Without costly ground truth labels, MU-Net directly learns the end-to-end mapping from the image pairs to their transformation parameters. MU-Net performs a coarse-to-fine registration pipeline by stacking several deep neural network models on multiple scales, which prevents the backpropagation being falling into a local extremum and resists significant image distortions. In addition, a novel loss function paradigm is designed based on structural similarity, which makes MU-Net suitable for various types of multi-modal images. MU-Net is compared with traditional feature-based and area-based methods, as well as supervised and other unsupervised learning methods on the Optical-Optical, Optical-Infrared, Optical-SAR and Optical-Map datasets. Experimental results show that MU-Net achieves more robust and accurate registration performance between these image pairs with geometric and radiometric distortions.We share the datasets and the code implemented by Pytorch at https://github.com/yeyuanxin110/MU-Net.

CanSAR chemistry registration and standardization pipeline

BackgroundIntegration of medicinal chemistry data from numerous public resources is an increasingly important part of academic drug discovery and translational research because it can bring a wealth of important knowledge related to compounds in one place. However, different data sources can report the same or related compounds in various forms (e.g., tautomers, racemates, etc.), thus highlighting the need of organising related compounds in hierarchies that alert the user on important bioactivity data that may be relevant. To generate these compound hierarchies, we have developed and implemented canSARchem, a new compound registration and standardization pipeline as part of the canSAR public knowledgebase. canSARchem builds on previously developed ChEMBL and PubChem pipelines and is developed using KNIME. We describe the pipeline which we make publicly available, and we provide examples on the strengths and limitations of the use of hierarchies for bioactivity data exploration. Finally, we identify canonicalization enrichment in FDA-approved drugs, illustrating the benefits of our approach.ResultsWe created a chemical registration and standardization pipeline in KNIME and made it freely available to the research community. The pipeline consists of five steps to register the compounds and create the compounds’ hierarchy: 1. Structure checker, 2. Standardization, 3. Generation of canonical tautomers and representative structures, 4. Salt strip, and 5. Generation of abstract structure to generate the compound hierarchy. Unlike ChEMBL’s RDKit pipeline, we carry out compound canonicalization ahead of getting the parent structure, similar to PubChem’s OpenEye pipeline. canSARchem has a lower rejection rate compared to both PubChem and ChEMBL. We use our pipeline to assess the impact of grouping the compounds in hierarchies for bioactivity data exploration. We find that FDA-approved drugs show statistically significant sensitivity to canonicalization compared to the majority of bioactive compounds which demonstrates the importance of this step.ConclusionsWe use canSARchem to standardize all the compounds uploaded in canSAR (> 3 million) enabling efficient data integration and the rapid identification of alternative compound forms with useful bioactivity data. Comparison with PubChem and ChEMBL pipelines evidenced comparable performances in compound standardization, but only PubChem and canSAR canonicalize tautomers and canSAR has a slightly lower rejection rate. Our results highlight the importance of compound hierarchies for bioactivity data exploration. We make canSARchem available under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) at https://gitlab.icr.ac.uk/cansar-public/compound-registration-pipeline.

Multi-contrast computed tomography healthy kidney atlas

The construction of three-dimensional multi-modal tissue maps provides an opportunity to spur interdisciplinary innovations across temporal and spatial scales through information integration. While the preponderance of effort is allocated to the cellular level and explore the changes in cell interactions and organizations, contextualizing findings within organs and systems is essential to visualize and interpret higher resolution linkage across scales. There is a substantial normal variation of kidney morphometry and appearance across body size, sex, and imaging protocols in abdominal computed tomography (CT). A volumetric atlas framework is needed to integrate and visualize the variability across scales. However, there is no abdominal and retroperitoneal organs atlas framework for multi-contrast CT. Hence, we proposed a high-resolution CT retroperitoneal atlas specifically optimized for the kidney organ across non-contrast CT and early arterial, late arterial, venous and delayed contrast-enhanced CT. We introduce a deep learning-based volume interest extraction method by localizing the 2D slices with a representative score and crop within the range of the abdominal interest. An automated two-stage hierarchal registration pipeline is then performed to register abdominal volumes to a high-resolution CT atlas template with DEEDS affine and non-rigid registration. To generate and evaluate the atlas framework, multi-contrast modality CT scans of 500 subjects (without reported history of renal disease, age: 15–50 years, 250 males & 250 females) were processed. PDD-Net with affine registration achieved the best overall mean DICE for portal venous phase multi-organs label transfer with the registration pipeline (0.540 ± 0.275, p < 0.0001 Wilcoxon signed-rank test) comparing to the other registration tools. It also demonstrated the best performance with the median DICE over 0.8 in transferring the kidney information to the atlas space. DEEDS perform constantly with stable transferring performance in all phases average mapping including significant clear boundary of kidneys with contrastive characteristics, while PDD-Net only demonstrates a stable kidney registration in the average mapping of early and late arterial, and portal venous phase. The variance mappings demonstrate the low intensity variance in the kidney regions with DEEDS across all contrast phases and with PDD-Net across late arterial and portal venous phase. We demonstrate a stable generalizability of the atlas template for integrating the normal kidney variation from small to large, across contrast modalities and populations with great variability of demographics. The linkage of atlas and demographics provided a better understanding of the variation of kidney anatomy across populations.

FV 13 Transcranial direct current stimulation modulates stroke-induced secondary thalamic neurodegeneration in mice

Introduction: Transcranial direct current stimulation (tDCS) non-invasively promotes recovery after stroke ( Braun et al. Exp. Neurol. 2016; Hummel et al. Brain 2005 ). Stroke affects entire networks beyond the focal lesion, impacting post-stroke impairment ( Blaschke et al. Stroke 2021 ). The thalamus constitutes a central hub due to its numerous functional connections but is also prone to secondary injury ( Cao et al. Front. Neurol. 2020 ). Under the hypothesis that tDCS promotes recovery by supporting neuroprotection, we investigated the effects of tDCS on secondary thalamic neurodegeneration after stroke. Methods: Cortical stroke was induced in the sensory-motor cortex (photothrombosis model in mice). Starting three days after stroke, cathodal tDCS over the ipsilesional somatosensory cortex was performed daily for ten days (39.6 kC/m 2 ), sham-stimulated mice served as control. Behavioral tests over time assessed functional recovery. Secondary degeneration of the ipsilesional thalamus was evaluated ex vivo 28 days after stroke. Using an atlas-based registration pipeline ( Pallast et al. J. Neurosc. Methods 2019 ), the absolute number of GFAP+ astrocytes and relative number of ipsilesional NeuN+ neurons compared to the contralateral unaffected thalamus were evaluated. Lesion maps based on T2-weighted Magnetic Resonance Imaging (MRI) before tDCS were used for voxel-based lesion-symptom mapping (VBLSM) to evaluate the effect of the lesion location on remote neurodegeneration. Functional connectivity (FC) between the lesioned sensorimotor cortex and the ipsilesional thalamus was measured by resting-state functional MRI. Additionally, glucose metabolism after a likewise tDCS regimen in healthy mice was measured by positron emission tomography compared to sham stimulation. Results: Repetitive tDCS decreased the ipsilateral thalamic glucose metabolism in unlesioned animals, while accelerating motor recovery after stroke. Four weeks after cortical stroke, secondary glial scaring was found in the ipsilesional thalamus, most pronounced in the posterior (Po), ventral posterolateral (VPL), and ventral posteromedial (VPM) nucleus, its extent correlating to the cortical lesion size (R 2 =0.3, p<0.01). Neurodegeneration was associated with a lesion cluster (VSBLM, p<0.05) extending laterally and including the callosal white matter tracts as well as deeper primary somatosensory layers. A decrease in FC between the primary somatosensory area of the lower limb (SS-LL) and the thalamus correlated with secondary neurodegeneration (R 2 =0.25, p<0.01). Intriguingly, in contrast to glial scaring, tDCS reduced thalamic neurodegeneration by over 60% (p<0.05). Conclusion: Cortical stroke induced network changes and led to remote secondary structural impairment of the thalamus, depending on the affection of corticothalamic connections. TDCS mitigated this remote secondary neurodegeneration. Data suggest previously unknown effects of tDCS on remote brain regions after stroke.