27 research outputs found
De-noising of diffusion-weighted MRI data by averaging of inconsistent input data in wavelet space
Diffusion Weighted Images datasets with high spatial resolution and strong diffusion weighting are often deteriorated with low SNR. Here, we demonstrate the feasibility of a recently presented repetition-free averaging based de-noising (AWESOME). That technique reduces noise by averaging over a series of N images with varying contrast in wavelet space and regains intensities and object features initially covered by noise. We show that high resolution DWIs are achievable in a quality that almost equals to that obtained from 6fold complex averaging
De-noising of diffusion-weighted MRI data by averaging of inconsistent input data in wavelet space
Diffusion Weighted Images datasets with high spatial resolution and strong diffusion weighting are often deteriorated with low SNR. Here, we demonstrate the feasibility of a recently presented repetition-free averaging based de-noising (AWESOME). That technique reduces noise by averaging over a series of N images with varying contrast in wavelet space and regains intensities and object features initially covered by noise. We show that high resolution DWIs are achievable in a quality that almost equals to that obtained from 6fold complex averaging
Highly efficient MRI through multi-shot echo planar imaging
Multi-shot echo planar imaging (msEPI) is a promising approach to achieve
high in-plane resolution with high sampling efficiency and low T2* blurring.
However, due to the geometric distortion, shot-to-shot phase variations and
potential subject motion, msEPI continues to be a challenge in MRI. In this
work, we introduce acquisition and reconstruction strategies for robust,
high-quality msEPI without phase navigators. We propose Blip Up-Down
Acquisition (BUDA) using interleaved blip-up and -down phase encoding, and
incorporate B0 forward-modeling into Hankel structured low-rank model to enable
distortion- and navigator-free msEPI. We improve the acquisition efficiency and
reconstruction quality by incorporating simultaneous multi-slice acquisition
and virtual-coil reconstruction into the BUDA technique. We further combine
BUDA with the novel RF-encoded gSlider acquisition, dubbed BUDA-gSlider, to
achieve rapid high isotropic-resolution MRI. Deploying BUDA-gSlider with
model-based reconstruction allows for distortion-free whole-brain 1mm isotropic
T2 mapping in about 1 minute. It also provides whole-brain 1mm isotropic
diffusion imaging with high geometric fidelity and SNR efficiency. We finally
incorporate sinusoidal wave gradients during the EPI readout to better use coil
sensitivity encoding with controlled aliasing.Comment: 13 pages, 10 figure
Axon diameters and myelin content modulate microscopic fractional anisotropy at short diffusion times in fixed rat spinal cord
Mapping tissue microstructure accurately and noninvasively is one of the
frontiers of biomedical imaging. Diffusion Magnetic Resonance Imaging (MRI) is
at the forefront of such efforts, as it is capable of reporting on microscopic
structures orders of magnitude smaller than the voxel size by probing
restricted diffusion. Double Diffusion Encoding (DDE) and Double Oscillating
Diffusion Encoding (DODE) in particular, are highly promising for their ability
to report on microscopic fractional anisotropy ({\mu}FA), a measure of the pore
anisotropy in its own eigenframe, irrespective of orientation distribution.
However, the underlying correlates of {\mu}FA have insofar not been studied.
Here, we extract {\mu}FA from DDE and DODE measurements at ultrahigh magnetic
field of 16.4T in the aim to probe fixed rat spinal cord microstructure. We
further endeavor to correlate {\mu}FA with Myelin Water Fraction (MWF) derived
from multiexponential T2 relaxometry, as well as with literature-based
spatially varying axonal diameters. In addition, a simple new method is
presented for extracting unbiased {\mu}FA from three measurements at different
b-values. Our findings reveal strong anticorrelations between {\mu}FA (derived
from DODE) and axon diameter in the distinct spinal cord tracts; a moderate
correlation was also observed between {\mu}FA derived from DODE and MWF. These
findings suggest that axonal membranes strongly modulate {\mu}FA, which - owing
to its robustness towards orientation dispersion effects - reflects axon
diameter much better than its typical FA counterpart. The {\mu}FA exhibited
modulations when measured via oscillating or blocked gradients, suggesting
selective probing of different parallel path lengths and providing insight into
how those modulate {\mu}FA metrics. Our findings thus shed light into the
underlying microstructural correlates of {\mu}FA and are (...
Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-space Metrics
International audienceThe non-Gaussian noise distribution in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging and on Mean Apparent Propagator Magnetic Resonance Imaging, i.e. q-space metrics. We show that phase correction is still a challenge, but also an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values