Fitting IVIM with Variable Projection and Simplicial Optimization

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project.

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience

Bifurcated topological optimization for IVIM

In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.The National Institute of Biomedical Imaging And Bioengineering (NIBIB) of the National Institutes of Health (NIH); University of Washington’s Royalty Research Fund; NIH grants; the German Research Foundation (DFG) and a grant from the Alfred P. Sloan Foundation and the Gordon & Betty Moore Foundation to the University of Washington eScience Institute Data Science Environment.http://www.frontiersin.org/Neuroscienceam2022Chemical Engineerin

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

MORPHOLOGICAL SNOWFLAKES FOR ROBUST NONLINEAR FILTERING

The aim of this paper is to introduce nonlinear operators which are robust to noise, by combining the computation of the max/min for the dilation/erosion with an embedded robust filtering step. More precisely, the unitary robust nonlinear filters are computed using a new set of hexagonal symmetry-based structuring elements, called snowflakes. Each snowflake is composed of the union of a central pixel and six micro- neighbourhoods. In this framework, there are two different families of filters which can be defined: second order-operators and local selective operators. Besides the comparison of the practical behaviour of the various families of filters, some preliminary results on their algebraic properties and robustness against noise are given

A 3d automatic segmentation method based on mathematical morphology for multiphoton images of melanocyte-keratinocyte coculture skin model

International audienceMelanocyte-keratinocyte coculture models are interesting in vitro systems used to identify the de-pigmenting or pro-pigmenting potential of cosmetic ingredients. This potential can be estimated by calculating the melanin density inside this model. Multiphoton microscopy is a privileged microscopy technique for this kind of evaluation, thanks to its low invasiveness and appropriate contrast (time resolved two photon excited fluorescence) for melanin detection. On multiphoton images, the first necessary step to calculate melanin density is to delimitate the pixels where the tissue is located. In this paper, we proposed a tissue segmentation method based on mathematical morphology.This pigmented coculture model contains two types of cells: keratinocytes and melanocytes that form a three dimensional tissue with a thickness of about 40 µm. The samples are reconstructed in 96 well plates, fixed in 4% formalin and rinsed in PBS prior to image acquisition. Multiphoton imaging was performed with a LEICA TCS SP8 microscopy at 760 nm, 40x/1.1NA W objective. A multiphoton 3D (x, y, z) image of 205x205x50 µm3 volume corresponds to a stack of 25 en face images of 512x512 pixels (0.4 µm/pixel) acquired with 2 µm z-step. In this kind of models, segmentation is made difficult by the fact that the intensity of the fluorescence signal is heterogeneous over the tissue: dark regions inside the images can correspond either to background or to cytoplasmic regions. Therefore, the first step of our method consists in applying a horizontal area closing with size A. This operator fills all 2D dark structures which are smaller than A, closing any possible small and dark connections between the exterior and the interior of the tissue. Parameter A is taken equal to 150 µm2, i.e. the area of a cell. Afterwards, a reconstruction by erosion, starting from the first and last slides, is applied in 3D. This operation fills all dark structures inside the tissue. Finally, a simple threshold at the noise level produces the final mask of the tissue. The method has been evaluated on a database containing 24 3D images, of which 4 had been manually segmented. The results were considered satisfactory by the experts. This tissue segmentation method has been integrated in a software suite and is robust and fast enough in order to be used in an automatic process. We are currently working on the following step, namely melanin quantification, to estimate the global melanin density, its z-distribution inside the tissue and localization in keratinocytes and melanocytes

Automatic 3D segmentation of multiphoton images: a key step for the quantification of human skin

International audienceBackground/purposeMultiphoton microscopy has emerged in the past decade as a useful noninvasive imaging technique for in vivo human skin characterization. However, it has not been used until now in evaluation clinical trials, mainly because of the lack of specific image processing tools that would allow the investigator to extract pertinent quantitative three-dimensional (3D) information from the different skin components.MethodsWe propose a 3D automatic segmentation method of multiphoton images which is a key step for epidermis and dermis quantification. This method, based on the morphological watershed and graph cuts algorithms, takes into account the real shape of the skin surface and of the dermal–epidermal junction, and allows separating in 3D the epidermis and the superficial dermis.ResultsThe automatic segmentation method and the associated quantitative measurements have been developed and validated on a clinical database designed for aging characterization. The segmentation achieves its goals for epidermis–dermis separation and allows quantitative measurements inside the different skin compartments with sufficient relevance.ConclusionsThis study shows that multiphoton microscopy associated with specific image processing tools provides access to new quantitative measurements on the various skin components. The proposed 3D automatic segmentation method will contribute to build a powerful tool for characterizing human skin condition. To our knowledge, this is the first 3D approach to the segmentation and quantification of these original images

Automatic segmentation of epidermal layers through in vivo skin multiphoton microscopy images

International audienc