dataesr/scientific-paragraphs-categorization huggingface dataset:
Competitors were invited to submit their application to the Challenge, and 10 teams provided their results from which a first qualitative and quantitative evaluation of the tractography methods was achieved, showing the superiority of global tractography and advanced HARDI models over streamline tractography algorithms with single tensor estimation. The original diffusion MRI dataset of the FiberCup phantom can be downloaded from the tractometer website. 1  A digital version of the Fiber Cup phantom also exists that was designed using the Fiber-Fox tool, described later in the manuscript that is available from the NITRC website. 2  The Pullen Crossing Phantom includes X-shaped phantoms each composed of polyester yarn material being itself composed of 18 filaments ( "fibers ") with a diameter of 10 m  ( Pullens et al.
What would the ideal simulated dMRI dataset (for tractography) look like? As introduced above, phantoms of any kind try to reproduce reality as closely as possible while providing full explainability and controllability. Along this line, an ideal digital dMRI phantom would reproduce the following aspects of reality perfectly: (1) a rich and complex microand macro-structure including different cell types and arrangements as well as short-and long-range fiber connections in realistic configurations, (2) diffusion-properties similar to the ones found in the different types of human brain tissue, as well as (3) the actual MR acquisition step including all its effects on the final imaging result. Due to various reasons, such a perfect reproduction of reality is of course not possible. There are for example computational limitations, i.
The multiple correlation function tool (MCF)  ( Laun, 2012 )  and UCL Camino Diffusion MRI Toolkit 3  are two openly available software packages that enable such simulations. Recently, another open-source approach was introduced in Lee et al. (  2021 ) that implements Monte Carlo simulations in 3D voxelized segmentations of cells in microscopy images. These approaches focus on the actual diffusion modeling and are frequently used for the development or evaluation of localized methods, such as signal reconstruction or the analysis of microstructure. For methods that work on multiple voxels or a whole image simultaneously (e. g. fiber tractography, connectomics or tract-based spatial statistics), an artificial signal in hundreds and thousands of voxels has to be synthesized. To this end, parametric models, which are computationally much more lightweight, are more appropriate and commonly used.
The principal diffusion direction of the tensor is obtained directly from the fiber centerline. Fig. 12  illustrates the approach. By summing several bundles, configurations like crossings or kissings can be generated. Delputte et al. (2006)  used the approach by  Leemans et al. (2005)  to generate a synthetic dataset on the basis of a fiber tractogram obtained from a smoothed in vivo diffusion-weighted image. Each streamline is treated as a centerline for one synthetic fiber bundle. Additionally they added Rician noise to the resulting dataset and used it to validate fiber tractography methods by calculating the distance between the ground truth fibers and the tractography results. Jeurissen et al. used the framework of Leemans and colleagues to measure tract dispersion of a probabilistic CSD tractography algorithm based on the residual bootstrap as a function of arc length along the trajectory for gold standard  ( Jeurissen et al.
Phantomas further calculates an approximation of the image contrast changes introduced by compartment specific tissue relaxation with T1 and T2 relaxation times representative for in vivo brain tissue. While not simulating the actual MR acquisition with all of its effects, this is the first dMRI phantom tool usable for fiber tractography that simulates relaxation induced contrast changes. The Phantomas website features a web interface for defining the fiber structure and its source code is available openly on GitHub. 7 Phantom s was used to create the testing and training data of the 2nd HARDI Reconstruction Challenge, 8  organized at ISBI 2013. With the Fiberfox dMRI simulator,  Neher et al. (2014)  presented an approach that unifies many aspects of the previous approaches in one comprehensive tool.
The non-fiber compartments are defined by classic volume fraction maps. Fiberfox further simulates the actual k-space acquisition, enabling a straightforward and realistic introduction of MRI effects, such as complex gaussian noise, eddy currents, inter-volume head motion, distortions, spikes, aliasing, N/2 ghosts, signal drift, Gibbs ringing and relaxation. This simulation of the MR acquisition can be controlled by a number of parameters typical for real MR scanners (e. g. TE, TR, partial fourier factor, ETL, readout type, b -value and gradient directions) and human tissue in the form of compartment specific relaxation constants (T1, T2) and diffusivities. Fiberfox is included in the Medical Imaging Interaction Toolkit (MITK) and openly available as source code and binary application. 9 he complete simulation is controllable via a graphical user interface  as well as batch-processing-friendly command line application.
Multiple datasets generated with Fiberfox and usable for fiber tractography validation have been published and are openly available  ( Neher et al. , 2014 ( Neher et al. , , 2017b ; ; Maier-Hein et al. , 2015 , 2017a , 2017b ; Neher and Maier-Hein, 2019 , 2020a , 2021 ). The phantom simulated for the ISMRM Tractography Challenge 2015  ( Maier-Hein et al. , 2017a , 2017b  ) is now one of the most widely used dMRI phantom datasets in the area of fiber tractography validation  ( Neher et al. , 2017a ; Poulin et al. , 2017 ; Théberge et al. , 2020 ; Benou and Riklin Raviv, 2019 ; Wegmayr et al. , 2019 ). In the context of the challenge it was used to evaluate 96 distinct tractography submissions from 20 research teams using the Tractometer evaluation suite  ( Côté et al. , 2013 ). Building on the experiences made with this single brain-like phantom, Neher et al.
recently published a large dataset of 99 subjects with various realistically simulated image contrasts and variations of acquisition parameters that extends the ISMRM challenge phantom in terms of the number of simulated images and subjects, the structural phantom complexity (71 major white matter tracts) and the range of simulated MRI artifacts. Fig. 17  illustrates some of the phantoms created with Fiberfox. An approach to manually define artificial fiber bundles similar to Fiberfox with a focus on polarized light imaging but also applicable to dMRI, the FAConstructor, was recently presented by  Reuter et al. (2019). FAConstructor is openly available on GitHub. 10 Another approach for simulating dMRI images that is very similar to Fiberfox was presented 10 https://github. com/3d-pli/FAConstructor by the authors of D-BRAIN  ( Perrone et al.
18 (a, b )  ( Sarwar et al. , 2019 ). The connectivity matrices of these phantoms were simulated using generative models that preserved the topological properties of the human brain, which include degree, betweenness centrality, clustering coefficient, characteristic path length, global efficiency, modularity and interconnectivity between hubs  ( Betzel et al. , 2016 ). The predefined connections were then modeled as curved tubular fiber bundles of fixed diameter. This connectome phantom generation process is presented in Fig. 18 (c ). The simulated phantoms were used to compare the binary connectomes estimated by deterministic and probabilistic tractography algorithms. The spher-  ical connectome phantoms and code for its generation is available on GitHub. 11  To quantify the accuracy of tractography estimated connectomes, Tractometer evaluation suite  ( Côté et al.
They can further lead to spatial offsets between the dMR images in a dataset, further undermining the estimates of microstructure obtained from them. To assess artifacts in tractography and support development and validation of dMRI artifact removal techniques it is necessary to have numerical simulations. These need to be able to simultaneously model diffusion-weighting contrast and MR physics realistic enough to model artifacts. The simplest numerical methods simply assign a single value of diffusion-weighting contrast to each of the three tissue types, then combine these signals using probabilistic segmentations to form an image  ( Bastin, 2001 ( Bastin, , 1999 ) ). Other methods use steady-state solutions of the Bloch Equations for the well known pulse sequences to simulate Gibbs ringing  ( Perrone et al.
, 2010a ( Drobnjak et al. , , 2010b ) ). POSSUM and DW-POSSUM are included in FSL (FMRIB Software Library) and are openly available as source code and binary ap-  plication. 12 The complete simulation is controllable via a graphical user interface as well as batch-processing-friendly command line application. Multiple datasets generated with DW-POSSUM are openly available. 13 ue to its realistic modeling of complex artifacts outlined above DW-POSSUM has been used for a range of applications in assessing artifacts and has contributed directly to development and validation of artifact correction algorithms. Graham et al. showed that one of the most commonly used correction techniques, eddy_correct 10 , introduces a systematic error that is significant enough to undermine any analysis performed on data corrected using this scheme  ( Graham et al.
, 2016 ). Andersson et al. (2017)  then used it to demonstrate that eddy framework provides a much better alternative, and gave an idea of the level of correction that it can be expected to achieve for commonly acquired datasets. DW-POSSUM was subsequently used also to evaluate eddy's ability to correct for slice outlier artefacts  ( Andersson et al. , 2016 ). Susceptibility-by-movement artefact is a phenomenon that is notoriously difficult to study in real data. The susceptibility artefact is caused by an off-resonance field induced by differences in magnetic susceptibility at the air-tissue interface, which cause geometric distortions when data is acquired with an EPI sequence. If the subject moves in the scanner, then the susceptibility field and, in turn, the distortions alter. Whilst the static case is well characterised and studied in the literature  ( Bihan et al.
, 2006 ) , comparatively little work has been done to assess the impact of this dynamic case, when the subject moves, and as a result the artefact is rarely considered when acquiring and processing data. POSSUM's ability to model the interaction between the movement and susceptibility artefact made it a natural test bed to assess the impact of the susceptibility-by-movement artefact. POSSUM can use an airtissue segmentation to obtain a first-order solution to Maxwell's equa-tions, providing a set of basis functions that describe how the susceptibility field changes with subject movement. This in turn assisted the development of new methods to correct the artefact:  ( Andersson et al. , 2018 )  proposed a technique to correct for the artefact, and was able to use data simulated with DW-POSSUM  (Graham et al.
A number of phantom datasets in this area has been published and used in larger tractography evaluation studies or it is possible to use them in such a way, for example the recently published 99 simulated brains dataset  ( Neher and Maier-Hein, 2020a , 2020b , 2021 ) , the other Fiberfox simulations  ( Maier-Hein et al. , 2017a , 2017b , 2015 ; Neher et al. , 2017b ; Neher and Maier-Hein, 2019 ) , the DW-POSSUM data sets 11 or the Phantomas examples. 17 In the future, some aspects will be particularly important for the field of fiber tractography validation and artifact correction using simulated dMRI data: Realism: There is still room for improvements in the area of simulations in terms of realism. One particular challenge will be to unify three aspects of a realistic numerical simulation: the cellular microstructure and the corresponding diffusion processes, a tissue macrostructure representative for the human brain; and a comprehensive approximation of a real MR acquisition.
Incorporating all of the realistic scanner and object effects creates a huge demand on the computational memory and time. The input object itself that describes the MR properties of the simulated brain/body can take thousands of GB of memory. To run a single MRI simulation of full Bloch equations with all of the features described can take hours. These problems are possibly the most pressing and have so far prevented many of the simulators from simulating fully realistic DIffusion MR images. New developments in GPU programming, and increasing power of computers will be crucial here in driving future research and developing new ways of parallelising and speeding up the simulation process. Available data : There is still a lack of large and variable openly available simulated datasets, including various image contrasts, anatomies, pathologies as well as variable MR acquisition settings.
Assessing artifacts, quality control and AI One of the very important aspects of artifact assessment in Diffusion MRI is quality control (QC). QC involves ensuring a dataset meets a certain set of standards before the dataset is given the clearance for inclusion in subsequent analyses. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold standard is manual inspection of the data, but this process is time-consuming and subjective. The current trend towards acquiring increasingly large datasets means the time required for human QC is becoming prohibitive. The HCP  ( Van Essen et al. , 2012 )  acquired data for 1200 subjects with almost 300 dMR volumes per subject and the UK Biobank will eventually acquire imaging data for 100,000 subjects with over 100 vol per subject  ( Miller et al.
, 2016 ). Recently supervised learning approaches based on convolutional neural networks (CNNs) have been shown to be competitive with manual inspection. However, CNNs tend to have many parameters requiring optimization -often in the millions -meaning they typically require large, manually labelled datasets for training which can be very challenging as is itself time-consuming to produce and still introduces an element of subjectivity. One potential way to address these issues is to use simulated data. Simulation could circumvent the need for human labeling by producing realistic datasets, along with ground-truth labels, for training machine learning tools on. In the case of QC, a simulator that was capable of producing datasets containing artefacts, such as motion, could be used to produce a training set.
This SAR configuration exploits the fact that scatterers present different responses to different polarizations of the incident wave. For backscattering in which waves are transmitted and collected in the same position and by considering the reciprocity theorem, PolSAR leads to three-dimensional data. On the contrary, when scattered waves are collected in a different position with respect to the transmitted one, i. e. forward scattering, PolSAR data are four-dimensional data. • Polarimetric SAR Interferometry (PolInSAR)  (Cloude and Papathanassiou 1998 ): This technique tries to combine both the advantages of InSAR and PolSAR. On the one hand, the introduction of interferometric diversity makes possible the data to be sensible to the structure of the target in the vertical dimension. On the other hand, the data are related to different scattering mechanisms in the same resolution cell, thanks to the polarimetric capabilities of the acquisition system.
Hence, PolInSAR data are sensible to different scattering mechanisms in the same image pixel, located at different heights. The introduction of simple scattering models allows to extract relevant information about the scatterer under study. Among the possible applications of this technique, the most important is the extraction of parameters related to the vegetation cover which allow biomass estimation. In terms of data dimensionality, PolInSAR data consist of six-dimensional data if backscattering is considered, whereas they are eight-dimensional data for forward scattering. • SAR Tomography (TomoSAR)  (Reigber and Moreira 2000) : As shown in the previous point, PolInSAR represents a first step to resolve the vertical structure of the imaged scatterer. In this direction, SAR tomography is a technique directed to achieve a real three-dimensional reconstruction of the scene under observation.
For instance, this circumstance determines the way PolSAR data has to be considered when distributed scatterers are of concern. A PolSAR system measures the 2 Â 2 complex scattering matrix S, which can be vectorized into the form presented by (1. 96)  (Cloude and Pottier 1996; Bebbington 1992) ; see also Sect. 1. 1. 2. 2. On the one hand, when this scattering vector refers to a distributed scatterer, a given sample of it has almost no information concerning the scatterer itself, as k consists of a sample of the pdf given by (1. 98)  (Oliver and Quegan 1998). On the other hand, if the mean value of k is estimated, it turns out to be zero. Thus, as reported in the literature, when distributed scatterers are studied, the vector k, or the scattering matrix S in the particular case of PolSAR, cannot completely describe the properties of the distributed scatterer.
The coherence represents also an important source of information when PolSAR data are addressed. In particular, the complex correlation coefficient parameter derived from circularly polarized data has been employed to characterize rough surfaces  (Mattia et al. 2003) , to study the sea surface  (Kasilingam et al. 2002)  or to discriminate sea ice types  (Wakabayashi et al. 2004 ). When obtained from linearly polarized data, the coherence has been also employed to characterize the forest cover in the Colombian Amazon  (Hoekman and Quinones 2002). In conjunction with polarimetric techniques, i. e. polarimetric SAR interferometry (PolInSAR), the interferometric coherence is employed to retrieve the forest vegetation  (Cloude and Papathanassiou 1998)  or the crop plants heights  (Ballester Berman et al.
These moments must be estimated from the measured data, or said in a different way, speckle noise must be filtered out or even eliminated to grant access to these statistical moments. In the case of PolSAR data, under the assumption of the vector k to be distributed according to the zero-mean, complex Gaussian distribution (1. 98), these moments correspond to the covariance C or the coherency T matrices. Section 1. 2. 3 already introduced the simplest approach to estimate the covariance or the coherency matrices, i. e. the multilook (1. 100), which corresponds to an incoherent average or a spatial average. Although the multilook approach corresponds to the maximum likelihood estimator of the covariance or coherency matrices, it presents the drawback that the estimation of the data is obtained at the expense of degrading the spatial resolution and the spatial details of the data.
PolSAR Speckle Noise Filtering Principles The objective of any PolSAR speckle noise filter to be defined is to estimate the covariance or the coherency matrix while retaining the spatial resolution and the spatial details of the data. From a general point of view, it would be necessary to determine the general principles a PolSAR filter should follow in order to perform a correct estimation of the information of interest. Different authors have addressed the necessity to specify the general principles of a PolSAR speckle filter and which are the potential limitations: Touzi et al. (Touzi and Lopes 1994) ,  Lee et al. (Lee et al. 1999)  and  López-Martínez et al. (Lopez Martínez and Fabregas 2008). In the previous three references, as the data is assumed to be characterized by the zero-mean, complex Gaussian pdf, the information to retrieve is on the second-order moments of the PolSAR data.
In  (Touzi and Lopes 1994) , the authors propose the use of the Mueller matrix, although they also consider the covariance matrix. In  (Lee et al. 1999; Lopez Martínez and Fabregas 2008) , the filtering is performed on the covariance or on the coherency matrices. In any case, the use of the covariance, the coherency or the Mueller matrices to filter the data is equivalent as all these matrices contain the same information. For instance, as indicated previously in Sect. 1. 1. 2. 2, the covariance and the coherency matrices are related by similarity transformations. Implicitly, the authors are considering that these matrices contain all the necessary information to characterize the PolSAR data. This assumption is only valid under the hypothesis of (1. 98), which only applies in the case of stationary data.
The presumption of more evolved stochastic data models that may take into account additional signal variability, for instance, texture, are always associated with the necessity to estimate additional stochastic moments associated with the texture information. Another point in which all the previous three references are in agreement is the need to consider the estimation of the previous matrices locally, adapting to the stationarity or homogeneity of the PolSAR data. This requirement is justified from two different points of view. The first one refers, due to the stationarity of PolSAR data, to the need to maintaining the spatial resolution and the radiometric information in the case of point or deterministic scatterers, which may be extended to the idea of preserving the spatial resolution and the spatial details of the PolSAR data.
The second refers to the fact that in the case of distributed scatterers, the covariance matrix must be estimated on stationary data, avoiding the mixture of different stationary areas. This idea implies that the PolSAR filter must adapt the filtering process to the morphology of the PolSAR data. The differences between the filtering principles for PolSAR data proposed by  Touzi and Lopes (1994) ,  Lee et al. (1999)  and Lopez  Martínez and Fabregas (2008)  are on how to consider the information that may be provided by the off-diagonal elements of the covariance or coherency matrices and whether this information may be employed to optimize speckle noise reduction or not. The approaches proposed by  Touzi and Lopes (1994)  and  Lee et al. (1999) ) suggest an extension of the multiplicative speckle noise model that applies for the diagonal entries of the covariance matrix to the off-diagonal ones, although it is also admitted that this extension may not lead to an optimum filtering of the speckle noise component.
PolSAR Speckle Noise Filtering Alternatives As indicated in Sect. 1. 2. 3, the first alternative to estimate the covariance or the coherency matrices is to consider their maximum likelihood estimator that corresponds to an incoherent spatial average as expressed in (1. 101). In this case, the estimation of the information is obtained at the expense of the loss of spatial resolution and spatial details. Consequently, in order to avoid the previous drawbacks, the PolSAR data filters should adapt to the morphology of the SAR image to retain the spatial details while leading to a correct and unbiased estimation of the covariance or coherency matrices. PolSAR images are inherently heterogeneous as they reflect the heterogeneity of the Earth surface. Consequently, a first alternative to adapt to this heterogeneity, in order to avoid the loss of spatial resolution and spatial details, is to adapt locally to the signal morphology.