SpinDoctor: A MATLAB toolbox for diffusion MRI simulation.

The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium can be modeled by the multiple compartment Bloch-Torrey partial differential equation. Under the assumption of negligible water exchange between compartments, the time-dependent apparent diffusion coefficient can be directly computed from the solution of a diffusion equation subject to a time-dependent Neumann boundary condition. This paper describes a publicly available MATLAB toolbox called SpinDoctor that can be used 1) to solve the Bloch-Torrey partial differential equation in order to simulate the diffusion magnetic resonance imaging signal; 2) to solve a diffusion partial differential equation to obtain directly the apparent diffusion coefficient; 3) to compare the simulated apparent diffusion coefficient with a short-time approximation formula.

Efficient GPU-based Monte-Carlo simulation of diffusion in real astrocytes reconstructed from confocal microscopy.

The primary goal of this work is to develop an efficient Monte-Carlo simulation of diffusion-weighted signal in complex cellular structures, such as astrocytes, directly derived from confocal microscopy. In this study, we first use an octree structure for spatial decomposition of surface meshes. Octree structure and radius-search algorithm help to quickly identify the faces that particles can possibly encounter during the next time step, thus speeding up the Monte-Carlo simulation.

Modulation of water diffusion by activation-induced neural cell swelling in Aplysia Californica.

Diffusion functional magnetic resonance imaging (DfMRI) has been proposed as a method for functional neuroimaging studies, as an alternative to blood oxygenation level dependent (BOLD)-fMRI. DfMRI is thought to more directly reflect neural activation, but its exact mechanism remains unclear. It has been hypothesized that the water apparent diffusion coefficient (ADC) decrease observed upon neural activation results from swelling of neurons or neuron parts.

Quantitative DLA-based compressed sensing for T-weighted acquisitions.

High resolution Manganese Enhanced Magnetic Resonance Imaging (MEMRI), which uses manganese as a T contrast agent, has great potential for functional imaging of live neuronal tissue at single neuron scale. However, reaching high resolutions often requires long acquisition times which can lead to reduced image quality due to sample deterioration and hardware instability. Compressed Sensing (CS) techniques offer the opportunity to significantly reduce the imaging time.

DLA based compressed sensing for high resolution MR microscopy of neuronal tissue.

In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function.