dataesr/scientific-paragraphs-categorization huggingface dataset:
opendose. org. It allows easy access to the SAF database and the calculation of S values for 1,252 radionuclides. S values generated for the models considered so far are close to those already available in the literature. A new website section for performing model-based dosimetry calculations is under development. The collaboration is open to new research teams willing to participate in any OpenDose-related activity, from the production of dosimetric data to the development of the website and dosimetry tools.
ACKNOWLEDGMENTS The authors acknowledge the support of  France Grilles , the  EGI Infrastructure , the  Biomed Virtual Organization , and the  Virtual Imaging Platform  for providing computing resources and services. The authors also acknowledge  Yves-Marie Gouesnard  for his contribution to the website. The OpenDose collaborators include Ernesto Amato,  Department of Biomedical and Dental Sciences, University of Messina, Messina, Italy , and  Department of Morphologic and Functional Imaging, University of Messina, Messina, Italy; Lucrezia Auditore, Department of Biomedical and Dental Sciences, University of Messina, Messina, Italy , and  Department of Morphologic and Functional Imaging ,  University of Messina, Messina, Italy; Sorina Camarasu-Pop, Université de Lyon-CREATIS-CNRS UMR 5220-Inserm U1044-INSA Lyon-Université Lyon 1-Centre Léon Bérard, Lyon, France; Frederic Cervenansky, Université de Lyon-CREATIS-CNRS UMR 5220-Inserm U1044-INSA Lyon-Université Lyon 1-Centre Léon Bérard, Lyon, France ;  José M.
DISCLOSURE This work was supported by the  National Measurement System of the U. K. Department for Business, Energy and Industrial Strategy. This work was performed within the framework of  SIRIC LYriCAN Grant INCa-INSERM-DGOS-12563  and  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  ANR. Part of this work was supported by the  Euratom   Research and Training Program ,  2014-2018 , under grant agreement  755523  (MEDIRAD). Calculations were partially performed on the  Ziemowit computer cluster in the Laboratory of Bioinformatics and Computational Biology, Silesian University of Technology , created in  EU Innovative Economy Program   POIG. 02. 01. 00-00-166/08  and expanded in the  POIG. 02. 03.
Abstract-Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies.
These include rule-based techniques to capture histological descriptions of atrial anatomy and fibre structure. 12, 20, 24, 25, 44 An alternative approach that may be used to incorporate fibre direction in computational models is to register a fibre atlas to a patient specific geometry. 20, 28, 36 Fibre atlases used in current modelling pipelines are either synthetic, constructed in a rule-based manner based on histological and morphological descriptions of atrial tissue, or are derived from single large animal datasets. 48 These approaches, however, exhibit limited validation, are not representative of individual patients, are not necessarily from self consistent data sets, and do not always cover both left and right atria. In addition, there is growing interest within the cardiac electrophysiology community in variability, and these deterministic rule-based methods do not allow direct propagation of uncertainty through to model simulation output.
To address the need for a self consistent data driven representative fibre map and the corresponding uncertainty quantification, we develop a biatrial fibre atlas constructed as an average of the high-resolution DTMRI dataset of Pashakhanloo et al. 29  We first project the fibres for each of the endocardial and epicardial left and right atrial surfaces onto a local coordinate system. We then construct mean left and right atrial endocardial and epicardial fibre fields and evaluate the local standard deviation of the fibre orientations. We compare simulated local activation time maps for each of the meshes with each of the individual fibre fields or the mean field. We also simulate atrial fibrillation (AF) across the different combinations of anatomy and fibre field and compare arrhythmia properties to the patient specific fibre field simulations.
MATERIALS AND METHODS We initially describe the segmentation and meshing steps used to construct left and right atrial endocardial and epicardial meshes with fibre fields for each of the ex-vivo DTMRI datasets (''Data Modalities'' and ''Segmentation, Meshing and Fibre Assignment'' sections). We further develop our atrial coordinate system to fix the locations of the pulmonary veins, vena cava and appendages at positions in the coordinate system corresponding to the average location across the anatomies to minimise the average distortion (''Atrial Coordinates'' section). We use the atrial coordinate system to transfer fibre fields between atrial geometries and register all fibres to a single mesh to calculate a mean fibre field with uncertainty (''Fibre Mapping and Calculating Mean Fibre Fields'' section), which is visualised using a streamline based approach (''Fibre Visualisation'' section).
Finally we describe the methodologies used for simulating local activation time maps (''Local Activation Time Simulations'' section) and arrhythmia simulations (''Arrhythmia Simulations'' section). The main steps in this methodology are shown in the schematic in Fig. 1. Data corresponding to the endocardial and epicardial surface meshes, their fibre fields and the average fibre fields are available online (https://doi. org/10. 5281/zenodo. 3764 917 and https://doi. org/10. 18742/RDM01-596).
Data Modalities All DTMRI data used here are from the study of Pashakhanloo et al. in which human hearts were procured through the National Disease Research Interchange (Philadelphia, PA). 29 We used seven out of eight biatrial datasets from Pashakhanloo et al. 29  because for one of the atria we were unable to achieve a segmentation that could be used for calculation of the universal atrial coordinates used for registration. Biatrial anatomies were obtained from non-diffusion weighted images (b0), together with DTMRI images reconstructed to 0:4 mm Â 0:4 mm Â 0:4 mm for assigning fibre fields to the atrial meshes.
We then used an 8-connectivity filter, together with a cluster size tolerance of 300 pixels, to fill any holes in the combined segmentation. Specifically, identified clusters bounded by the blood pool were categorised as blood pool, whereas clusters surrounded by mask pixels were assumed to be holes in the mask. Finally, the segmentation was manually edited using ITK-snap  47  software to remove any erroneous voxels or fill any missing areas. Simulation meshes were constructed from the segmented images using the following sequence of steps. First, a marching cubes algorithm implemented within the medical image registration toolkit (http s://github. com/BioMedIA/MIRTK) was used to extract an isosurface from the segmentation. This surface was then modified in Meshlab 7 using the following sequence of filters to create a watertight surface: screened Poisson surface reconstruction, marching cubes, and quadric edge collapse decimation.
The resulting surface was then trimmed to open the mesh at the pulmonary veins (PV), superior vena cava (SVC), inferior vena cava (IVC), coronary sinus (CS) and mitral and tricuspid valves (MV and TV) using Paraview software. 1 Finally, this mesh was remeshed using mmgtools meshing software (https://g ithub. com/MmgTools/mmg) to produce meshes suitable for simulations, with an average edge length of 0. 34 mm. Fibres were assigned to each element of these surface meshes by finding the closest fibre in the DTMRI.
Atrial Coordinates To register data across atrial anatomies, to create an average fibre atlas and to calculate streamlines for fibre visualisation, each of the left and right atrial endocardial surfaces was expressed in the universal atrial coordinate (UAC) system. 33 UAC were calculated using two Laplace solves for each atria with Dirichlet boundary conditions of zero and one applied along two sets of boundary nodes. For the LA, the two coordinates were chosen as a septal to lateral coordinate (a LA ), and a coordinate from the posterior mitral valve, over the roof, to the anterior mitral valve (b LA ), since these fields are close to orthogonal and use easily identifiable atrial structures. Equivalently for the RA, the first coordinate passes from the lateral tricuspid valve to the septal tricuspid valve (a RA ), and the second coordinate was from the inferior vena cava to the superior vena cava (b RA ).
Atrial fibres are inherently defined relative to anatomical structures (the PV, SVC, IVC, CS, left and right atrial appendages). Consequently, mapping fibres between cases requires that these structures have the same UAC values on all meshes. To create a coordinate system with anatomical structures located at the same UAC, the original UAC system was extended to include additional boundary conditions. Detailed methodology on mapping anatomical structures to atlas coordinate locations, the choice of boundary conditions and the Laplace solves used to construct the UAC system are given in the supplementary material, and shown in Fig. 2. This implementation uses the VTK and VMTK 2 python libraries, together with carpentry software for the Laplace solves (https://carp. medunigraz. at/carpu tils/).
Identical initial conditions were used for the endocardial and epicardial surfaces. For each simulation set-up, phase singularities were calculated for 10 s of arrhythmia data to identify centres of rotational activity and re-entrant driver locations, or until termination if this occurred before 10 s. Detected phase singularities were then post-processed to calculated spatial phase singularity density maps using our previously published methodology. 32 e calculated the Pearson correlation coefficient between each pair of 2D phase singularity maps using the MATLAB corr2 function, where 1 is total positive linear correlation, 0 is no linear correlation, and 2 1 is total negative linear correlation. FIGURE 3. UAC fibre mapping. An example is shown for mapping LA anatomy 1 with its associated fibre field (the source, top row) to anatomy 6 (the target, bottom row).
RESULTS We first present the left atrial endocardial and epicardial fibre maps coloured by UAC angle (''Left Atrial Fibre Maps'' section), followed by the corresponding right atrial maps (''Right Atrial Fibre Maps'' section). We then calculated the average LA and RA fibre fields across the anatomies by mapping each anatomy and associated fibre field to a single LA and RA anatomy from the dataset using UAC (''Average Fibre Maps'' section). The effects of fibre field on local activation time maps during pacing (''Effects of Fibre Field on Local Activation Time Maps'' section) and also on electrical reentrant driver location during AF (''Effects of Fibre Field on Arrhythmia Dynamics'' section) were determined.
BIOMEDICAL ENGINEERING SOCIETY coefficients were computed between each map and the gold standard map for a given anatomy. Figure  8a  shows the PS density maps for the endocardium of the LA bilayer model simulations for which PS maps in each row are compared to the corresponding map. Visually, PS density maps vary with both anatomy and with fibre map. This is quantified by calculating correlation matrices (Pearson correlation coefficient, corr2 in MATLAB) between each pair of LA and RA bilayer fibre map simulations, shown in Fig. 8b. Correlation coefficients close to 1 indicate similar PS density maps. The mean correlation coefficients calculated across anatomies for each fibre map are also given in Fig. 8b. Fibre field 1 has the highest mean correlation coefficient for the LA bilayer simulations (0.
Main Findings In this study, we developed a methodology for constructing an average fibre atlas-for use in computational modelling studies and to account for anisotropy in clinical measurements-from the high resolution DTMRI dataset of Pashakhanloo et al. 29  We expressed each fibre field in a UAC system and mapped each anatomy and its associated fibre field to the same atrial anatomy on which an average fibre field was calculated. We tested the effects of fibre field on predicted activation times. The impact on average LAT difference was small with a range of mean local activation time differences for LA fields: 2. 66-5. 56 ms, and for RA fields: 2. 29-4. 38 ms, which supports the use of atlas based fibres for activation simulations. However, maximum LAT differences were larger, and differences increase further when anisotropy increases, which may be particularly important in the case of regional differences in anisotropy.
Comparison to Other Methodologies and Atlases for Personalising Atrial Fibre Fields Methodologies for personalising atrial fibre fields fall broadly into two categories: rule-based methodologies and registration-based methodologies. Rulebased methodologies generate a new fibre field for each anatomy following a sequence of rules, typically based on histological descriptions. Alternatively, registration techniques map fibres to an anatomy from a reference atlas, which may itself be constructed using a rulebased approach. Our methodology falls somewhere between the two categories: we first construct a fibre atlas from DTMRI data and then register this atlas using UAC to a new anatomy; equally, expressing the DTMRI atlas in UAC creates a rule-based approach for assigning fibres to a new model in a UAC basis.
24, 25 Our current study assigns fibres to both the left and right atria, uses fewer landmark points (3) and incorporates fibre fields from DTMRI data. 29. Roney et al. registered the bilayer fibre atlas of Labarthe et al. to patient-specific bilayer models using the UAC system. 24, 33 Our current methodology significantly extends this study by fixing the locations of key anatomical structures, including the PV, vena cava, appendages and CS, to their average location across the dataset to generate an atlas coordinate system, and uses high-resolution DTMRI fibres. McDowell et al. use an image-based registration method to transform the atlas geometry and fibre field of Krueger et al. to target geometries. 20, 28 Their technique involves assigning a large number of landmarks manually before applying a 3D thin plate spline transformation and a large deformation diffeomorphic metric mapping.
The average wall thickness for the Pashakhanloo et al. dataset is 2. 73mm (5-95% quartiles: 0. 98-4. 38 mm), which is 4. 34 voxels (5-95% quartiles: 1. 56-6. 96 voxels). 29 As such, the resolution of the DTMRI is sufficient for differentiating the two fibre fields across the majority of the atrium. 45 Instead of including the same fibre atlas in all patient-specific models, future work could investigate choosing the fibre map corresponding to the atrial anatomy closest in morphology to the target atrial anatomy, where atrial morphologies could be compared using a principal component analysis. 41 The current UAC system could be extended to include a transmural coordinate by performing a Laplace solve with zero on the endocardial surface and one on the epicardial surface, following Bishop et al.
Conclusions We have developed a methodology for registering atrial fibre fields to construct an average fibre atlas from the high resolution DTMRI dataset of Pashakhanloo et al. 29  We have made this atlas, together with the associated atrial coordinates required for registration, available online. The impact of fibre field on average paced activation times was small, supporting the use of atlas based fibres for activation simulations. Arrhythmia dynamics were more dependent on fibre field, suggesting that atrial fibre fields should be carefully assigned to patient-specific arrhythmia models. For LA bilayer model AF simulations, fibre field 1 was optimal; whereas for RA bilayer models, the average fibre field was optimal. As such, in the case that a single atlas fibre field is used for simulation studies, we suggest using the fibre field corresponding to patient 1 for LA simulations, and the average of the fibre fields for RA simulations.
ACKNOWLEDGEMENTS CHR acknowledges a  Medical Research Council Skills Development Fellowship  ( MR/S015086/1 ). SAN acknowledges support from the  UK Engineering and Physical Sciences Research Council  ( EP/M012492/1 ,  NS/A000049/1  and  EP/P01268X/1 ), the  British Heart Foundation  ( PG/15/91/31812 ,  PG/13/37/30280 ) and  Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. This work was supported by the  Wellcome/EPSRC Centre for Medical Engineering  [ WT 203148/Z/16/Z ], and a  Transatlantic Network grant  from the  Leducq Foundation  (to NT and EV, Research Grant number  16 CVD 02 ). FP acknowledges support from the  American Heart Association  ( 18POST33990040 ). This study received financial support from the  French Government  as part of the '' Investments of the Future '' program managed by the  National Research Agency (ANR) , Grant reference  ANR-10-IAHU-04.
The ArmeoSpring device The ArmeoSpring (Hocoma, Switzerland), is a passive exoskeleton which provides UE weight support and allows early training of motor skills  [27]. It has six degrees of freedom and can be attached to the UE at the level of the arm, forearm and wrist. It thus allows self-initiated arm movements in a large tridimensional workspace. Support against gravity is provided by adjustable springs for the upper arm (9 levels, from A, no tension exerted and minimum support, to I, maximum tension and support) and the forearm (5 levels from A to E). It is supplied with the Armeocontrol 1. 22 software, which provides many functional exercises, simulated in a virtual environment with auditory and visual feedback. The software also allows recording kinematic parameters via seven sensors positioned on the different exoskeleton joints, and provides all the exoskeleton joint angles, the effector location in a tridimensional workspace (used to control a cursor on a screen) and the grip pressure.
Kinematic assessment A unitary movement was defined between two consecutive targets and considered only if both balls, the previous and the next, were successfully caught. During a trial, a maximum of 12 balls had to be caught, thus representative of 12 consecutive unitary movements. The Armeocontrol software records raw data, specific to the assessment exercise, at a frequency of 64 Hz, corresponding in this case to the hand position in the horizontal plane (XY), and the time when the target appeared, was caught and disappeared. From the raw data file, we computed kinematic parameters with a custom code implemented on Matlab software (see Additional file 1: S1), freely downloadable at https ://githu b. com/ david gasq/Armeo _2DHor izCat ch. git. These parameters were chosen because, based on the recommendations by Schwarz et al.
Data analysis The statistical analyses were performed using Statistica software (StatSoft. Inc. Version 10). The significance threshold of the p-value was set at 0. 05. For each trial and each patient, the parameter data were averaged from all the successful unitary movements (a maximum of 12 balls). The data were also averaged for each session (10 consecutives trials). We first ensured that the kinematic parameters of the summary report and those calculated with the custom code were consistent (paired t-tests not statistically significant, see Additional file 1: Figure  S2 ). Although we tried to standardize the starting position at the beginning of the exercise, we observed that not all the patients started from the same position. Some patients had their hands already almost placed over the ball.
If significant, a Tukey post-hoc analysis was applied to analyze significant main effects and interactions. If no trial * session interaction was found, we considered the same trial effect across sessions. The retention of the kinematic parameters was inferred from the rm-ANOVA results with the between-session comparisons. Indeed, the data obtained from the last day of training (S3) were compared to those obtained at the end of the previous day, during S2. Thirdly, reliability was studied specifically on the averaged data of the sessions and trials for which we considered there was no longer an obvious learning effect (the last four trials of S2 and S3, see "Results" section for details). The relative reliability was evaluated using the intraclass correlation coefficients (ICC) that provide information on inter-and intra-session reliability.
Discussion In this pilot study, kinematic parameters computed from data provided by the ArmeoSpring exoskeleton were analyzed to investigate the relevance of these parameters in the assessment of post-stroke hemiplegic patients during a 2D-horizontal catching exercise. The results highlighted an intra-and inter-session learning effect for all the parameters except PeakVel. The reliability analysis, applied to data without a priori learning, showed that PeakVel and Score had the lowest margin of error.
Important considerations and limitations Since we wanted unrestricted arm movements, the exoskeleton was unlocked at the level of shoulder and elbow. Sometimes, the hand was directly located at the first ball position and therefore the movement observed did not reflect the real one. Consequently, in our study, the data were averaged over 11 consecutive movements and not 12 as designed in the horizontal catching task, and as the Armeocontrol software computes kinematic parameters. We investigated the psychometric properties of certain carefully chosen kinematic parameters based on a recent review  [5] , that represent all the dimensions of a movement. The kinematic parameters were slightly different (although not significantly) from those provided in the ArmeoSpring report (see Additional file 1: Figure  S2 ), but computed with a stricter and more rigorous methodology (removal of the first target, trajectories considered only if the departure and arrival targets are reached).
This may be used to administer a short assessment protocol to post-stroke patients with the ArmeoSpring, but could also limit its ease of use in routine care by a clinician. To exceed these limits, we have made available to the community the script used to calculate the parameters (see Additional file 1: S1). Depending on the research questions and hypothesis, some parameters may be more appropriate than others to capture movement patterns. As demonstrated, we must be careful in the interpretation since the initial parameter value may also depend on learning processes that are relatively independent from the impairment reductions  [23]. In our study, learning occurred mainly between session 1 and sessions 2 and 3, and until the sixth repetition for some parameters. Consequently, in similar conditions and particularly for the parameters MovementTime, Task-Time, HPR and nPeaks, we suggest considering the first session (consisting of 10 repeated trials) as a session of familiarization with the device and the task to avoid data corruption by learning processes.