Probing dynamic myocardial microstructure with cardiac magnetic resonance diffusion tensor imaging

This article is an invited editorial comment on the paper entitled “In vivo cardiovascular magnetic resonance diffusion tensor imaging shows evidence of abnormal myocardial laminar orientations and mobility in hypertrophic cardiomyopathy” by Ferreira et al., and published as Journal of Cardiovascular Magnetic Resonance 2014; 16:87

Statistical DSI Brain Tractography: A Way to Handle the Kiss-Cross Uncertainty.

Despite the advent of diffusion magnetic resonance imaging and tractography algorithms, the accurate mapping of complex fiber kiss-crossings areas of the brain remains out of reach. In this study, we present a statistical DSI-based tractography algorithm which explores all possible paths in the brain white matter. We also introduce a cortex connectivity graph whose weighted edges correspond to the connection likelihood. The tests performed on the centrum semi-ovale have shown that a simple thresholding applied to the edges of this graph allows us to image the connectivity of any part of the brain to the desired level of complexity

Imaging the Brain Neuronal Network with Diffusion MRI: A Way to Understand Its Global Architecture

In order to better understand the high complexity of the brain, the detailed study of its individual components clearly seems insufficient. The backbone of complexity in the nervous system is composed of the large scale architectural characteristics of the neuronal network. Newly, by the advent of MR tractography, its investigation is accessible. We report on two important network characteristics that were already guessed from functional investigations and animal ex vivo studies, but never directly addressed in the human subject, ie the small world and hierarchical architecture of the human long-range brain axonal network

Diffusion MR Tractography of the Heart

Histological studies have shown that the myocardium consists of an array of crossing helical fiber tracts. Changes in myocardial fiber architecture occur in ischemic heart disease and heart failure, and can be imaged non-destructively with diffusion-encoded MR. Several diffusion-encoding schemes have been developed, ranging from scalar measurements of mean diffusivity to a 6-dimensional imaging technique known as diffusion spectrum imaging or DSI. The properties of DSI make it particularly suited to the generation of 3-dimensional tractograms of myofiber architecture. In this article we review the physical basis of diffusion-tractography in the myocardium and the attributes of the available techniques, placing particular emphasis on DSI. The application of DSI in ischemic heart disease is reviewed, and the requisites for widespread clinical translation of diffusion MR tractography in the heart are discussed

Accelerated Diffusion Spectrum Imaging with Compressed Sensing Using Adaptive Dictionaries

Diffusion Spectrum Imaging (DSI) offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (~1 hour). It is possible to accelerate DSI by sub-Nyquist sampling of the q-space followed by nonlinear reconstruction to estimate the diffusion probability density functions (pdfs). Recent work by Menzel et al. imposed sparsity constraints on the pdfs under wavelet and Total Variation (TV) transforms. As the performance of Compressed Sensing (CS) reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for sparse representation of diffusion pdfs can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in DSI, whereby we reduce the scan time of whole brain DSI acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the novel 3T Connectome MRI, whose strong gradients are particularly suited for DSI. The RMSE from the proposed reconstruction is up to 2 times lower than that of Menzel et al.’s method, and is actually comparable to that of the fully-sampled 50 minute scan. Further, we demonstrate that a dictionary trained using pdfs from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from another subject.National Institutes of Health (U.S.) (NIH R01 EB007942)National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB K99EB012107)National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB R01EB006847)National Institute for Biomedical Imaging and Bioengineering (U.S.) (K99/R00 EB008129)National Center for Research Resources (U.S.) (NCRR P41RR14075)National Institutes of Health (U.S.) (NIH Blueprint for Neuroscience Research U01MH093765)National Institutes of Health (U.S.) (The Human Connectome project)Siemens Aktiengesellschaft (Siemens-MIT Alliance)Center for Integration of Medicine and Innovative Technology (MIT-CIMIT Medical Engineering Fellowship