Michael Paquette

Learn More
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 the(More)
Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated(More)
PURPOSE Diffusion Spectrum Imaging enables to reconstruct the ensemble average propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive sensing offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of three sampling strategies(More)
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) [1], [2] and b) Constrained Spherical Deconvolution (CSD) [3]. The SDT is a sharpening operation which transforms the smooth(More)
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 [1], [2] with spherical deconvolution [3],[4] (GQID), and b) Diffusion Spectrum Imaging with Deconvolution [5] (DSID). GQI2 provides a direct analytical formula to(More)
Diffusion Spectrum Imaging (DSI) has been used for tractography in several publicly available software and a number of recent high impact publications. However, there are several important theoretical, numerical and practical considerations that are often ignored. We revisit the theoretical and state-of-the-art processing steps necessary to go from the DSI(More)
Purpose:Diffusion Spectrum Imaging (DSI) enables to reconstruct the Ensemble Average Propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive Sensing (CS) offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of 3 sampling(More)
The Heterodyne Instrument for Far Infrared (HIFI) on ESA's Herschel Space Observatory utilizes a variety of novel RF components in its five SIS receiver channels covering 4801250 GHz and two HEB receiver channels covering 14101910 GHz. The local oscillator unit will be passively cooled while the focal plane unit is cooled by superfluid helium and cold(More)
Recent development in sampling theory now allows the sampling and reconstruction of certain non-bandlimited functions on the sphere, namely a sum of weighted Diracs. Because the signal acquired in diffusion Magnetic Resonance Imaging (dMRI) can be modeled as the convolution between a sampling kernel and two dimensional Diracs defined on the sphere, these(More)
  • 1