4 research outputs found
Automatic, fast and robust characterization of noise distributions for diffusion MRI
Knowledge of the noise distribution in magnitude diffusion MRI images is the
centerpiece to quantify uncertainties arising from the acquisition process. The
use of parallel imaging methods, the number of receiver coils and imaging
filters applied by the scanner, amongst other factors, dictate the resulting
signal distribution. Accurate estimation beyond textbook Rician or noncentral
chi distributions often requires information about the acquisition process
(e.g. coils sensitivity maps or reconstruction coefficients), which is not
usually available. We introduce a new method where a change of variable
naturally gives rise to a particular form of the gamma distribution for
background signals. The first moments and maximum likelihood estimators of this
gamma distribution explicitly depend on the number of coils, making it possible
to estimate all unknown parameters using only the magnitude data. A rejection
step is used to make the method automatic and robust to artifacts. Experiments
on synthetic datasets show that the proposed method can reliably estimate both
the degrees of freedom and the standard deviation. The worst case errors range
from below 2% (spatially uniform noise) to approximately 10% (spatially
variable noise). Repeated acquisitions of in vivo datasets show that the
estimated parameters are stable and have lower variances than compared methods.Comment: v2: added publisher DOI statement, fixed text typo in appendix A
Automated characterization of noise distributions in diffusion MRI data
Knowledge of the noise distribution in diffusion MRI is the centerpiece to
quantify uncertainties arising from the acquisition process. Accurate
estimation beyond textbook distributions often requires information about the
acquisition process, which is usually not available. We introduce two new
automated methods using the moments and maximum likelihood equations of the
Gamma distribution to estimate all unknown parameters using only the magnitude
data. A rejection step is used to make the framework automatic and robust to
artifacts. Simulations were created for two diffusion weightings with parallel
imaging. Furthermore, MRI data of a water phantom with different combinations
of parallel imaging were acquired. Finally, experiments on freely available
datasets are used to assess reproducibility when limited information about the
acquisition protocol is available. Additionally, we demonstrated the
applicability of the proposed methods for a bias correction and denoising task
on an in vivo dataset. A generalized version of the bias correction framework
for non integer degrees of freedom is also introduced. The proposed framework
is compared with three other algorithms with datasets from three vendors,
employing different reconstruction methods. Simulations showed that assuming a
Rician distribution can lead to misestimation of the noise distribution in
parallel imaging. Results showed that signal leakage in multiband can also lead
to a misestimation of the noise distribution. Repeated acquisitions of in vivo
datasets show that the estimated parameters are stable and have lower
variability than compared methods. Results show that the proposed methods
reduce the appearance of noise at high b-value. The proposed algorithms herein
can estimate both parameters of the noise distribution automatically, are
robust to signal leakage artifacts and perform best when used on acquired noise
maps.Comment: v3: Peer reviewed version v2: Manuscript as submitted to Medical
image analysis v1: Manuscript as submitted to Magnetic resonance in medicin
Harmonization of diffusion MRI datasets with adaptive dictionary learning
Diffusion magnetic resonance imaging is a noninvasive imaging technique that
can indirectly infer the microstructure of tissues and provide metrics which
are subject to normal variability across subjects. Potentially abnormal values
or features may yield essential information to support analysis of controls and
patients cohorts, but subtle confounds affecting diffusion MRI, such as those
due to difference in scanning protocols or hardware, can lead to systematic
errors which could be mistaken for purely biologically driven variations
amongst subjects. In this work, we propose a new harmonization algorithm based
on adaptive dictionary learning to mitigate the unwanted variability caused by
different scanner hardware while preserving the natural biological variability
present in the data. Overcomplete dictionaries, which are learned automatically
from the data and do not require paired samples, are then used to reconstruct
the data from a different scanner, removing variability present in the source
scanner in the process. We use the publicly available database from an
international challenge to evaluate the method, which was acquired on three
different scanners and with two different protocols, and propose a new mapping
towards a scanner-agnostic space. Results show that the effect size of the four
studied diffusion metrics is preserved while removing variability attributable
to the scanner. Experiments with alterations using a free water compartment,
which is not simulated in the training data, shows that the effect size induced
by the alterations is also preserved after harmonization. The algorithm is
freely available and could help multicenter studies in pooling their data,
while removing scanner specific confounds, and increase statistical power in
the process.Comment: v5 Peer review for Human Brain Mapping v4: Peer review round 2 v3:
Peer reviewed version v2: Fix minor text issue + add supp materials v1: To be
submitted to Neuroimag
Automatic, fast and robust characterization of noise distributions for diffusion MRI
Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g.coils sensitivity maps or reconstruction coefficients), which is not usually available. We introduce a new method where a change of variable naturally gives rise to a particular form of the gamma distribution for background signals. The first moments and maximum likelihood estimators of this gamma distribution explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the method automatic and robust to artifacts. Experiments on synthetic datasets show that the proposed method can reliably estimate both the degrees of freedom and the standard deviation. The worst case errors range from below 2% (spatially uniform noise) to approximately 10% (spatially variable noise). Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variances than compared methods