9 research outputs found
Learning-based Ensemble Average Propagator Estimation
By capturing the anisotropic water diffusion in tissue, diffusion magnetic
resonance imaging (dMRI) provides a unique tool for noninvasively probing the
tissue microstructure and orientation in the human brain. The diffusion profile
can be described by the ensemble average propagator (EAP), which is inferred
from observed diffusion signals. However, accurate EAP estimation using the
number of diffusion gradients that is clinically practical can be challenging.
In this work, we propose a deep learning algorithm for EAP estimation, which is
named learning-based ensemble average propagator estimation (LEAPE). The EAP is
commonly represented by a basis and its associated coefficients, and here we
choose the SHORE basis and design a deep network to estimate the coefficients.
The network comprises two cascaded components. The first component is a
multiple layer perceptron (MLP) that simultaneously predicts the unknown
coefficients. However, typical training loss functions, such as mean squared
errors, may not properly represent the geometry of the possibly non-Euclidean
space of the coefficients, which in particular causes problems for the
extraction of directional information from the EAP. Therefore, to regularize
the training, in the second component we compute an auxiliary output of
approximated fiber orientation (FO) errors with the aid of a second MLP that is
trained separately. We performed experiments using dMRI data that resemble
clinically achievable -space sampling, and observed promising results
compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201
The Connectome Mapper: an open-source processing pipeline to map connectomes with MRI
Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org
The Translational Role of Diffusion Tensor Image Analysis in Animal Models of Developmental Pathologies
Diffusion Tensor Magnetic Resonance Imaging (DTI) has proven itself a powerful technique for clinical investigation of the neurobiological targets and mechanisms underlying developmental pathologies. The success of DTI in clinical studies has demonstrated its great potential for understanding translational animal models of clinical disorders, and preclinical animal researchers are beginning to embrace this new technology to study developmental pathologies. In animal models, genetics can be effectively controlled, drugs consistently administered, subject compliance ensured, and image acquisition times dramatically increased to reduce between-subject variability and improve image quality. When pairing these strengths with the many positive attributes of DTI, such as the ability to investigate microstructural brain organization and connectivity, it becomes possible to delve deeper into the study of both normal and abnormal development. The purpose of this review is to provide new preclinical investigators with an introductory source of information about the analysis of data resulting from small animal DTI studies to facilitate the translation of these studies to clinical data. In addition to an in depth review of translational analysis techniques, we present a number of relevant clinical and animal studies using DTI to investigate developmental insults in order to further illustrate techniques and to highlight where small animal DTI could potentially provide a wealth of translational data to inform clinical researchers