Substantial knowledge of auditory processing within mammalian nervous systems emerged from neurophysiological studies of the mustached bat (Pteronotus parnellii). This highly social and vocal species retrieves precise information about the velocity and range of its targets through echolocation. Such high acoustic processing demands were likely the evolutionary pressures driving the over-development at peripheral (cochlea), metencephalic (cochlear nucleus), mesencephalic (inferior colliculus), diencephalic (medial geniculate body of the thalamus), and telencephalic (auditory cortex) auditory processing levels in this species. Auditory researchers stand to benefit from a three dimensional brain atlas of this species, due to its considerable contribution to auditory neuroscience. Our MRI-based atlas was generated from 2 sets of image data of an ex-vivo male mustached bat's brain: a detailed 3D-T2-weighted-RARE scan [(59 × 63 x 85) μm3] and track density images based on super resolution diffusion tensor images [(78) μm3] reconstructed from a set of low resolution diffusion weighted images using Super-Resolution-Reconstruction (SRR). By surface-rendering these delineations and extrapolating from cortical landmarks and data from previous studies, we generated overlays that estimate the locations of classic functional subregions within mustached bat auditory cortex. This atlas is freely available from our website and can simplify future electrophysiological, microinjection, and neuroimaging studies in this and related species.@en

Purpose: Diffusion kurtosis imaging (DKI) is an advanced magnetic resonance imaging modality that is known to be sensitive to changes in the underlying microstructure of the brain. Image voxels in diffusion weighted images, however, are typically relatively large making them susceptible to partial volume effects, especially when part of the voxel contains cerebrospinal fluid. In this work, we introduce the “Diffusion Kurtosis Imaging with Free Water Elimination” (DKI-FWE) model that separates the signal contributions of free water and tissue, where the latter is modeled using DKI. Theory and Methods: A theoretical study of the DKI-FWE model, including an optimal experiment design and an evaluation of the relative goodness of fit, is carried out. To stabilize the ill-conditioned estimation process, a Bayesian approach with a shrinkage prior (BSP) is proposed. In subsequent steps, the DKI-FWE model and the BSP estimation approach are evaluated in terms of estimation error, both in simulation and real data experiments. Results: Although it is shown that the DKI-FWE model parameter estimation problem is ill-conditioned, DKI-FWE was found to describe the data significantly better compared to the standard DKI model for a large range of free water fractions. The acquisition protocol was optimized in terms of the maximally attainable precision of the DKI-FWE model parameters. The BSP estimator is shown to provide reliable DKI-FWE model parameter estimates. Conclusion: The combination of the DKI-FWE model with BSP is shown to be a feasible approach to estimate DKI parameters, while simultaneously eliminating free water partial volume effects.

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In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1-weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1-weighted images is crucial. Conventionally, the T1-weighted images are first registered based on a general-purpose registration metric, after which the T1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1 map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T1 map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T1 parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art modelbased approaches, in terms of both motion and T1 map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T1-weighted data and with two in vivo human brain T1-weighted data sets, showing its applicability in real-life scenarios.

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Purpose: Quantitative T₁ mapping is a magnetic resonance imaging technique that estimates the spin-lattice relaxation time of tissues. Even though T₁ mapping has a broad range of potential applications, it is not routinely used in clinical practice as accurate and precise high resolution T₁ mapping requires infeasibly long acquisition times. Method: To improve the trade-off between the acquisition time, signal-to-noise ratio and spatial resolution, we acquire a set of low resolution T₁-weighted images and directly estimate a high resolution T₁ map by means of super-resolution reconstruction. Results: Simulation and in vivo experiments show an increased spatial resolution of the T₁ map, while preserving a high signal-to-noise ratio and short scan time. Moreover, the proposed method outperforms conventional estimation in terms of root-mean-square error. Conclusion: Super resolution T₁ estimation enables resolution enhancement in T₁ mapping with the use of standard (inversion recovery) T₁ acquisition sequences. Magn Reson Med, 2016.

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