M. Okan Irfanoglu¹, Ahmad Beyh^2,3, Marco Catani², Flavio Dell'Acqua², and Carlo Pierpaoli^1
1QMI, NIBIB/NIH, Bethesda, MD, United States, ²Natbrainlab, King's College London, London, United Kingdom, ³Laboratory of Neurobiology, Department of Cell and Developmental Biology, University College London, London, United Kingdom

The Human Connectome Project (HCP) has brought significant advancements in hardware, acquisition, and preprocessing. Even after a decade since its collection, the HCP diffusion MRI data is still relevant for its richness and high resolution. Noise and geometric distortions, however, are particularly pronounced in this dataset. In this work, we have reprocessed nearly the entire HCP dMRI dataset while applying several recent processing improvements. We compared the quality of the newly processed dMRI outputs to the release version. We observed clearly detectable improvements. The data originated from this new processing will be made publicly available.

Peter Adany¹, In-Young Choi^1,2,3,4, and Phil Lee^1,3,4
1Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States, ²Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States, ³Department of Radiology, University of Kansas Medical Center, Kansas City, KS, United States, ⁴Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, United States

We have recently developed the spatial domain, Fast LIpid signal Processing (FLIP) algorithm to remove subcutaneous lipid signals in MRSI.  Practical application of FLIP to 3D EPSI was challenging due to the need for long processing times. We present an updated algorithm, named FLIP-COIL, which significantly reduces the processing times utilizing receive coil senstivity profiles. Algorithms including FLIP, FLIP-COIL, as well as PGA, HSVD and L2 are compared in 3D EPSI of ten subjects. This study demostrates that the FLIP-COIL approach can drastically reduce processing time with favorable performance of lipid removal in 3D EPSI over existing other algorithms.

Dominik Ludwig^1,2, Frederik B. Laun³, Karel D. Klika⁴, Julian Rauch^1,2, Mark E. Ladd^1,2,5, Peter Bachert^1,2, and Tristan A. Kuder^1
1Deparment of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ²Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany, ³Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ⁴Molecular Structure Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁵Faculty of Medicine, Heidelberg University, Heidelberg, Germany

Diffusion pore imaging might open a new window into pathologies by providing histology-like information non-invasively by estimating pore size and shape distributions. For current pore imaging approaches, closed pores filled with an NMR visible diffusing medium are assumed, while extraporal components and exchange are neglected, which limits applicability. We propose a method based on Gaussian phase approximation to suppress effects of extraporal fluids and transmembrane water exchange and compare the approach to a filter-based method. Thus, one main obstacle for in vivo applications is reduced, the required high gradient amplitudes may be obtained using local gradient coils in the future.

Istvan N Huszar¹, Adele Smart¹, Saad Jbabdi¹, Mark Jenkinson¹, Amy FD Howard¹, and Karla L Miller^1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

The BigMac dataset combines extensive post-mortem MRI with histology and polarised light imaging (PLI) in a macaque brain. Adding a new extension to our recently developed platform, the Tensor Image Registration Library (TIRL), we address unique challenges associated with this dataset and successfully align 77 2-D polarised light micrographs to high-resolution structural MRI data.

Noam Omer¹, Neta Stern¹, Tamar Blumenfeld-Katzir¹, Chen Solomon¹, Meirav Galun², and Noam Ben-Eliezer^1,3,4
1Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, ²Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel, ³Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, ⁴Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States

The common approach to myelin mapping relies on multi-T₂ component (mcT₂) analysis, where the signal from a single voxel is separated into its underlying distribution of T₂ values. This approach is highly challenging due to the ill posedness of extracting multiple free parameters from a single voxel data.    We present a new data-driven paradigm, where the white matter is first analyzed to identify a finite set of multi-T₂ distributions, which are then used to locally analyze the signal in each voxel. Application on white matter tissue produced improved myelin quantifications, without a priory fixing the number of sub-voxel compartments.

Leonardo Campos¹, Kelley M. Swanberg¹, Martin Gajdošík¹, Karl Landheer¹, and Christoph Juchem^1,2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Radiology, Columbia University, New York, NY, United States

Quantification of in vivo proton magnetic resonance spectra (¹H-MRS) still commonly involves evaluation of exclusively the real part of acquired spectral signals, but ignoring the information contained in the imaginary component may limit precise identification of individual metabolite contributions. Here, we assess quantification precision relative to SNR and noise correlation for both real and complex linear combination model fits of simulated ¹H-MRS spectra reflecting brain metabolite concentrations and T₂. Extending our results to inclusion of measured in vivo baselines, we demonstrate consistent improvements in metabolite quantification precision and/or accuracy by complex relative to real fits. 

Michael Paquette¹, Cornelius Eichner¹, and Alfred Anwander^1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

We introduce a computationally efficient fODF-weighted graph structure where shortest-paths through white matter compute the probability of connection while naturally limiting the angle of propagation between steps. Connectivity matrices obtained from this structure maintain many properties of probabilistic streamline count connectomes while avoiding the sampling bias of tractography.

Jeff Snyder¹, Peter Seres¹, Robert W Stobbe¹, Justin G Grenier¹, Penelope Smyth², Gregg Blevins², and Alan H Wilman^1
1Biomedical Engineering, University of Alberta, Edmonton, AB, Canada, ²Department of Medicine, Division of Neurology, University of Alberta, Edmonton, AB, Canada

A fast reconstruction method for PD-T2w T2 maps is presented and compared to the previous L2 norm minimization technique using Bland-Altman analysis in a five patient multiple sclerosis data set acquired at 3 T.  The new (subtraction) technique was in excellent agreement with the L2 norm method (confidence intervals of -1.1 to +1.2 ms), with average single slice reconstruction times of 0.6 s compared to 134 s.  The speed of T2 map production allowed accurate (based on sequence simulation via Bloch equations) inline T2 maps directly on the MRI console.