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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 77:1818–1830, 2017. © 2016 International Society for Magnetic Resonance in Medicine

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 ׫202F»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.

Purpose Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. Method An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation. Results Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error. Conclusion The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time. Magn Reson Med 75:181–195, 2016. © 2015 Wiley Periodicals, Inc.

Zebra finches are an excellent model to study the process of vocal learning, a complex socially-learned tool of communication that forms the basis of spoken human language. So far, structural investigation of the zebra finch brain has been performed ex vivo using invasive methods such as histology. These methods are highly specific, however, they strongly interfere with performing whole-brain analyses and exclude longitudinal studies aimed at establishing causal correlations between neuroplastic events and specific behavioral performances. Therefore, the aim of the current study was to implement an in vivo Diffusion Tensor Imaging (DTI) protocol sensitive enough to detect structural sex differences in the adult zebra finch brain. Voxel-wise comparison of male and female DTI parameter maps shows clear differences in several components of the song control system (i.e. Area X surroundings, the high vocal center (HVC) and the lateral magnocellular nucleus of the anterior nidopallium (LMAN)), which corroborate previous findings and are in line with the clear behavioral difference as only males sing. Furthermore, to obtain additional insights into the 3-dimensional organization of the zebra finch brain and clarify findings obtained by the in vivo study, ex vivo DTI data of the male and female brain were acquired as well, using a recently established super-resolution reconstruction (SRR) imaging strategy. Interestingly, the SRR-DTI approach led to a marked reduction in acquisition time without interfering with the (spatial and angular) resolution and SNR which enabled to acquire a data set characterized by a 78 μm isotropic resolution including 90 diffusion gradient directions within 44 h of scanning time. Based on the reconstructed SRR-DTI maps, whole brain probabilistic Track Density Imaging (TDI) was performed for the purpose of super resolved track density imaging, further pushing the resolution up to 40 μm isotropic. The DTI and TDI maps realized atlas-quality anatomical maps that enable a clear delineation of most components of the song control and auditory systems. In conclusion, this study paves the way for longitudinal in vivo and high-resolution ex vivo experiments aimed at disentangling neuroplastic events that characterize the critical period for vocal learning in zebra finch ontogeny.

Quantitative TTo improve the trade-off between the acquisition time, signal-to-noise ratio and spatial resolution, we acquire a set of low resolution TSimulation and in vivo experiments show an increased spatial resolution of the TSuper resolution T

In magnetic resonance imaging the spin-lattice relaxation time (T 1 ) of tissues is estimated from a set of T 1 -weighted images. As the T 1 estimation is a voxel-wise estimation, correct alignment of the T 1 -weighted images is crucial for T 1 mapping. Therefore, to correct for motion, the T 1 -weighted images are often registered prior to the T 1 estimation. This two-step approach, however, does not provide an accurate motion detection due to the high dynamic contrast changes in the series of images. Moreover, interpolation effects during registration, propagate to the T 1 estimation. In this work, we propose a unified approach, where the motion estimation is integrated into the T 1 estimation procedure. The motion model parameters and T 1 parameters are jointly estimated using a Maximum Likelihood (ML) approach. Results on simulation and real data experiments show a substantial improvement in the precision and accuracy of the estimated T 1 map compared to the conventional two-step approach.

Quantitative T1 mapping is a magnetic resonance imaging technique in which the spin-lattice relaxation time of tissues is estimated. Even though T1 mapping has a broad range of potential applications, it is not routinely used in clinical practice. The long acquisition time of the required set of high resolution T1-weighted images, needed to obtain a precise high resolution T1 map, is clinically infeasible. To improve the trade-off between the acquisition time, SNR and spatial resolution, we propose to combine super-resolution reconstruction with T1 parameter estimation into one integrated method that estimates a high resolution T1 map directly from a set of low resolution T1-weighted images.

Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images.An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation.Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error.The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time.