New bryophyte taxon records for tropical countries 2

Norris & T. Kop. Sabah, Mt. Kinabalu, Mary Strong Clemens. 10741, 15.11.1915 (L) as „Campylopus metzlerioides Broth. nom. nud.“ The species was known before (mostly as Atractylocarpus comosus Dix.) from Sumatra, Celebes, New Guinea, Bhutan and Nepal [JPF]

Aggregated motion estimation for real-time MRI reconstruction

Real-time magnetic resonance imaging (MRI) methods generally shorten the measuring time by acquiring less data than needed according to the sampling theorem. In order to obtain a proper image from such undersampled data, the reconstruction is commonly defined as the solution of an inverse problem, which is regularized by a priori assumptions about the object. While practical realizations have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal fidelity of the estimated images. Here we propose a novel approach for the reconstruction of serial real-time MRI data which integrates the deformations between nearby frames into the data consistency term. The method is not required to be affine or rigid and does not need additional measurements. Moreover, it handles multi-channel MRI data by simultaneously determining the image and its coil sensitivity profiles in a nonlinear formulation which also adapts to non-Cartesian (e.g., radial) sampling schemes. Experimental results of a motion phantom with controlled speed and in vivo measurements of rapid tongue movements demonstrate image improvements in preserving temporal fidelity and removing residual artifacts.Comment: This is a preliminary technical report. A polished version is published by Magnetic Resonance in Medicine. Magnetic Resonance in Medicine 201

Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function

A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo MRI data. The technique employs an advanced signal model for T2 relaxation that accounts for contributions from indirect echoes in a train of multiple spin echoes. An iterative solution of the nonlinear inverse reconstruction problem directly estimates spin-density and T2 maps from undersampled raw data. The algorithm is validated for simulated data as well as phantom and human brain MRI at 3 T. The performance of the advanced model is compared to conventional pixel-based fitting of echo-time images from fully sampled data. The proposed method yields more accurate T2 values than the mono-exponential model and allows for undersampling factors of at least 6. Although limitations are observed for very long T2 relaxation times, respective reconstruction problems may be overcome by a gradient dampening approach. The analytical gradient of the utilized cost function is included as Appendix.Comment: 10 pages, 7 figure

Joint T1 and T2 Mapping with Tiny Dictionaries and Subspace-Constrained Reconstruction

Purpose: To develop a method that adaptively generates tiny dictionaries for joint T1-T2 mapping. Theory: This work breaks the bond between dictionary size and representation accuracy (i) by approximating the Bloch-response manifold by piece-wise linear functions and (ii) by adaptively refining the sampling grid depending on the locally-linear approximation error. Methods: Data acquisition was accomplished with use of an 2D radially sampled Inversion-Recovery Hybrid-State Free Precession sequence. Adaptive dictionaries are generated with different error tolerances and compared to a heuristically designed dictionary. Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace. Results: All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Conclusion: Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude

Reconstruction and Dissection of the Entire Human Visual Pathway Using Diffusion Tensor MRI

The human visual system comprises elongated fiber pathways that represent a serious challenge for diffusion tensor imaging (DTI) and fiber tractography: while tracking of frontal fiber bundles may be compromised by the nearby presence of air-filled cavities, nerves, and eye muscles, the anatomic courses of the three main fiber bundles of the optic radiation are subject to pronounced inter-subject variability. Here, tractography of the entire visual pathway was achieved in six healthy subjects at high spatial accuracy, that is, at 1.8 mm isotropic spatial resolution, without susceptibility-induced distortions, and in direct correspondence to anatomic MRI structures. Using a newly developed diffusion-weighted single-shot STEAM MRI sequence, we were able to track the thin optic nerve including the nasal optic nerve fibers, which cross the optic chiasm, and to dissect the optic radiation into the anterior ventral bundle (Meyer's loop), the central bundle, and the dorsal bundle. Apart from scientific applications these results in single subjects promise advances in the planning of neurosurgical procedures to avoid unnecessary damage to the visual fiber system

Suppression of MRI Truncation Artifacts Using Total Variation Constrained Data Extrapolation

The finite sampling of k-space in MRI causes spurious image artifacts, known as Gibbs ringing, which result from signal truncation at the border of k-space. The effect is especially visible for acquisitions at low resolution and commonly reduced by filtering at the expense of image blurring. The present work demonstrates that the simple assumption of a piecewise-constant object can be exploited to extrapolate the data in k-space beyond the measured part. The method allows for a significant reduction of truncation artifacts without compromising resolution. The assumption translates into a total variation minimization problem, which can be solved with a nonlinear optimization algorithm. In the presence of substantial noise, a modified approach offers edge-preserving denoising by allowing for slight deviations from the measured data in addition to supplementing data. The effectiveness of these methods is demonstrated with simulations as well as experimental data for a phantom and human brain in vivo