Samuel St-Jean

Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn...

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 b...

Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analy...

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 b...

Analyzing microstructural properties projected along white matter pathways derived from diffusion magnetic resonance imaging (MRI) has become increasingly popular in recent years. Along-streamline profiling based on diffusion MRI tractometry is a framework that maps summary measures of the diffusion data (e.g., fractional anisotropy) at multiple po...

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, w...

We present consensus-based guidance for conducting and documenting multi-analyst studies. We discuss why broader adoption of the multi-analyst approach will strengthen the robustness of results and conclusions in empirical sciences.

Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations...

Small variations in diffusion MRI metrics between subjects are ubiquitous due to differences in scanner hardware and are entangled in the genuine biological variability between subjects, including abnormality due to disease. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variabi...

Quantitative scalar measures of diffusion MRI datasets are subject to normal variability across subjects, but potentially abnormal values may yield essential information to support analysis of controls and patients cohorts. However, small changes in the measured signal due to differences in scanner hardware or reconstruction methods in parallel MRI...

Diffusion magnetic resonance imaging (dMRI) is an imaging technique to obtain information about the microstructural properties of tissues non-invasively, such as white matter in the human brain. The properties of the dMRI signal can be used to extract scalar maps of tissues and their orientation based on the displacement of water molecules, reveali...

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Diffusion weighted magnetic resonance imaging is a noninvasive imaging technique which 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 subtl...

Purpose: To understand and characterize noise distributions in parallel imaging for diffusion MRI. Theory and Methods: Two new automated methods using the moments and the maximum likelihood equations of the Gamma distribution were developed. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two d...

Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analy...

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to...

ntravoxel incoherent motion (IVIM) MRI allows measuring pseudo perfusion as an extension to diffusion weighted imaging (DWI) measures such as the apparent diffusion coefficient (ADC). Recent applications in oncology makes it an attractive addition to the traditional ADC measurements which could help capturing potential microstructural alterations i...

Diffusion MRI infers information about the micro-structural architecture of the brain by probing the diffusion of water molecules. The process of virtually reconstructing brain pathways based on these measurements is called tractography. Various metrics can be mapped onto pathways to study their micro-structural properties. Tractometry is an along-...

We present a comparison of five different methods that estimate mappings between scanners for diffusion MRI data harmonisation. The methods are evaluated on a dedicated dataset of the same subjects acquired on three distinct scanners with 'standard' and 'state-of-the-art' protocols, with the latter having higher spatial and angular resolution. Our...

The quantification of diffusion MRI assumes the absence of motion and anatomical correspondence between diffusion sensitizing factors. To investigate the impact of processing order between motion correction and two denoising methods, we evaluated DKI and NODDI derived maps. Using repeated scans acquired with and without voluntary motion, three proc...

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct su...

Diffusion MRI suffers from relatively long scan times and low signal to noise ratio (SNR), which limits the acquired spatial resolution. In this work, we propose a unified framework for denoising and upsampling diffusion datasets based on a sparse representation of the diffusion signal. Our proposed method shows less blurring and increased anatomic...

Typical diffusion-weighted imaging (DWI) is susceptible to partial volume effects: different types of tissue that reside in the same voxel are inextricably mixed. For instance, in regions near the cerebral ventricles or parenchyma, fractional anisotropy (FA) from diffusion tensor imaging (DTI) may be underestimated, due to partial volumes of cerebr...

Diffusion MRI suffers from relatively long scan times and low signal to noise ratio (SNR), which limits the acquired spatial resolution. In this work, we propose a unified framework for denoising and upsampling diffusion datasets based on a sparse representation of the diffusion signal. Our proposed method shows less blurring and increased anatomic...

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which r...

One way to circumvent the typically low signal-to-noise ratio (SNR) in diffusion-weighted (DW) MRI datasets is to use denoising algorithms. While the advantage of this strategy is that it does neither increase the acquisition time nor requires a specific acquisition setup, it intrinsically relies on accurate estimation of noise properties i.e. type...

Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn...

For diffusion MRI studies relying on statistics computed along fiber trajectories, the extracted values might not be optimally aligned between subjects in the metric space, which could lead to subsequent erroneous statistical analysis. We thus propose a fast Fourier transform based 1D correction algorithm for a fast realignment (< 1 second) directl...

Synopsis: High-angular resolution diffusion (HARDI) MRI, like diffusion spectrum imaging-DSI, provides an attractive tool to investigate the complex white matter structure in the brainstem. However, due to the application of high b-values in the HARDI image acquisition, the raw images are SNR limited (SNR<10). In this study, we applied a novel deno...

Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to t...

Le début des années 2000 a vu la cartographie du génome humain se réaliser après 13 ans de recherche. Le défi du prochain siècle réside dans la construction du connectome humain, qui consiste à cartographier les connexions du cerveau en utilisant l’imagerie par résonance magnétique (IRM) de diffusion. Cette technique permet en effet d’étudier la ma...

Diffusion Weighted Images (DWIs) datasets are usually acquired at a lower spatial resolution than their structural counterpart due to a decrease in the Signal-to-Noise Ratio (SNR) and increased acquisition time as the voxel size is reduced. Achieving high spatial resolution improves the specificity of reconstructed tracts and diffusion features, wh...

We report our finding on using the cartesian SHORE algorithm for the sparc dMRI challenge using the three-shells datasets with 20, 30, and 60 gradients per shells (b-values of 1000, 2000, and 3000 s/mm 2 ). We pre-processed the data using a 3D Non-Local Means denoising on each DWIs separately.

We tested some classical deconvolution methods for the Sparse Reconstruction Challenge for Diffusion MRI (SPARC dMRI) of the MICCAI 2014 Workshop on Computational Diffusion MRI. We used the Constrained Spherical Deconvolution (CSD) and the Sharpening Deconvolution Transform (SDT) with two denoising algorithms : the Non Local Means (NLM) and the Non...

Diffusion Weighted Images (DWIs) datasets suffer from low Signal-to-Noise Ratio (SNR), especially at high b-values. High noise levels bias the measurements because of the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. We propose to use the redundancy of DWIs as a sparse represe...

For the ISBI HARDI reconstruction challenge 2013, we developed a local estimation method based on Multi-Tensor (MT) fitting with the Particle Swarm Optimization technique (PSO). For the contest, we compared using the raw DW and the DW denoised with adaptive nonlocal means using a rician noise model. Each DW images were processed independently. We c...

For the purpose of the ISBI HARDI reconstruction challenge 2013 and for the heavyweight category, we reconstructed the diffusion datasets using two methods: a) Generalized Q-sampling Imaging 2 with spherical deconvolution (GQID), and b) Diffusion Spectrum Imaging with Deconvolution (DSID). GQI2 provides a direct analytical formula to calculate the...

For the purpose of the ISBI HARDI reconstruction challenge 2013 and for the categories DTI and HARDI acquisitions, we reconstructed the diffusion datasets using two well established methods: a) Spherical Deconvolution Transform (SDT) and b) Constrained Spherical Deconvolution (CSD). The SDT is a sharpening operation which transforms the smooth diff...