Zijing Dong^1,2, Jonathan R. Polimeni^1,2, Lawrence L. Wald^1,2, and Fuyixue Wang^1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States

To achieve high spatial resolution in-vivo dMRI (mesoscale at isotropic 500μm), we propose a novel SuperRes-EPTI technique that combines our recently developed EPTI technique with a rotating-view super-resolution acquisition and reconstruction. The SuperRes-EPTI can achieve an overall 3.5× SNR gain (SuperRes×EPTI = 2.7×1.3), while providing images completely-free from distortions that allow for improved super-resolution reconstruction and high effective resolution. The SuperRes acquisition scheme was designed to avoid spin-history artifacts, and incorporates rigid motion correction between thick-slice volumes for high motion-robustness. In-vivo distortion-free dMRI data were acquired at 500μm-isotropic resolution at 3T that reveals detailed fibers in gray matter.

L. Tugan Muftuler¹, Andrew S. Nencka², Volkan E. Arpinar², Jaemin Shin³, Baolian Yang⁴, Graeme McKinnon⁴, Suchandrima Banerjee⁵, and Kevin M. Koch^2
1Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, ²Radiology, Medical College of Wisconsin, Milwaukee, WI, United States, ³GE Healthcare, New York, NY, United States, ⁴GE Healthcare, Waukesha, WI, United States, ⁵GE Healthcare, Menlo Park, CA, United States

Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Simultaneous multi-slice (SMS) accelerates data acquisition by exciting multiple image slices simultaneously and separating the overlapping slices using a mathematical model. However, SMS acceleration introduces increased noise in reconstructed images and crosstalk between simultaneously excited slices. These compounded effects from SMS acceleration could affect tissue microstructure parameters derived from advanced diffusion MRI models. 

Hunter Moss¹ and Jens Jensen^1
1MUSC, CHARLESTON, SC, United States

A fiber orientation density function (fODF) gives the angular density of axonal fibers within a white matter voxel. Estimated fODFs obtained with diffusion MRI are prone to aliasing errors, depending on the number of diffusion directions used in the data acquisition. Here these errors are quantified for several typical fODFs represented with spherical harmonic expansions truncated at a specified degree. Choosing the number of directions equal to 2 to 3 times the number of adjustable parameters in the spherical harmonic expansion is found to be sufficient to reduce aliasing errors to a low level.

Bruce Iain¹, Yixin Ma¹, and Allen Song^1
1Brain Imaging and Analysis Center, Duke University, Durham, NC, United States

An ability to characterize the corticobulbar and corticospinal tracts from the motor cortex into the spinal cord with diffusion tensor imaging remains a challenge due to limiting factors such as achievable spatial resolutions, spatial coverages, and signal-to-noise ratio in the spinal cord. By extending the field-of-view through a combination of sub-millimeter isotropic axial and sagittal acquisitions, this study presents a technique to delineate the complete pyramidal tracts with data acquired at a sufficient spatial resolution to resolve intricate structural details such as the pyramidal decussation. Such a delineation can facilitate placement of spinal cord stimulation electrodes for movement disorder treatments.

Zhibo S. Zhu¹, Nam Gyun Lee², Ye Tian¹, Ahsan Javed³, Mark Griswold⁴, Adrienne Campbell-Washburn³, and Krishna S. Nayak^1
1Department Of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, ²Department Of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ³Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States, ⁴Department of Radiology, Case Western Reserve University, Cleveland, OH, United States

We perform a thorough comparison of MRF with reference relaxometry approaches on a NIST/ISMRM phantom and a healthy volunteer. In the NIST phantom, relative bias from -4.7% to 27.7% for the NiCl₂ vials and from 5.01% to 28% for the MnCl₂ vials within a biological relevant T₁ and T₂ range. In-vivo, MRF has good repeatability (variations <2%) but substantial bias in WM and GM against the reference approach.

Jason Ostenson^1,2, Bruce M. Damon³, and Evan L. Brittain^4
1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, ²Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ³Stephens Family Clinical Research Institute Carle Foundation Hospital, Urbana, IL, United States, ⁴Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, United States

Multi-parametric fat-water imaging of brown adipose tissue (BAT) is an important tool to study BAT’s composition and metabolism. We propose a single breath-hold MR fingerprinting method to simultaneously estimate the triglyceride number of double bonds (NDB) and number of methylene-interrupted double bonds (NMIDB), as well as water T₁. We provide results of simulation, phantom, and example human BAT studies. The proposed method’s NDB and NMIDB estimates strongly correlate with MRS estimates (ρ = 0.998), and the T₁ estimates are similar to those from MRS. The NDB, NMIDB, and T₁ estimates in/around BAT are similar to those from the literature.

Xiaoxia Zhang¹, Sebastian Flassbeck¹, and Jakob Assländer^1
1Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York University, New York, NY, United States

Synthetic contrasts are commonly derived from parameter maps via Bloch simulation.Typically, model imperfections, in particular partial volume effects, cause artifacts in those images. Recently, it has been proposed to overcome this problem by mapping directly from MR-Fingerprinting data to synthetic contrasts with neural networks. Those methods, however, face the MRF-typical undersampling artifacts, as well as the computational burden of hundreds of input images. We propose to first reconstruct images in a low-rank sub-space, which maintains the correct partial volume contrast, but allows for removal of undersampling artifacts, and to map from this space to synthetic contrasts with a neural network.

Di Cui¹, Xiaoxi Liu¹, Peng Cao², Angela Jakary¹, Jing Liu¹, Janine Lupo¹, Yan Li¹, and Duan Xu^1
1University of California, San Francisco, San Francisco, CA, United States, ²The University of Hong Kong, Hong Kong, China

An MR fingerprinting method using dual echo EPI was developed in this study for simultaneous quantification of T₁, T₂, T₂*, proton density and off resonance. In vivo evaluations performed in patients with lower grade glioma tumor demonstrated the ability of the technique to acquire multiple reflexivity maps within a clinically manageable scan time with each slice using 16 seconds and achieving total brain coverage in approximately four minutes.  

Bruno Sa de la Rocque Guimaraes^1,2, Khaled Talaat^1,2, Michael Mullen³, Essa Yacoub³, and Stefan Posse^1,4
1Neurology Department, University of New Mexico, Albuquerque, NM, United States, ²Nuclear Engineering Department, University of New Mexico, Albuquerque, NM, United States, ³Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, ⁴Physics and Astronomy Department, University of New Mexico, Albuquerque, NM, United States

Here we describe the development of a high-spatial resolution multi-slice single-shot Diffusion-Tensor proton-echo-planar-spectroscopic-imaging (PEPSI) technique using a binomial spatial-spectral refocusing RF pulse for mapping the diffusion tensor of water, Cho, Cr and NAA. Analysis of NAA and water diffusion in white matter obtained in a 4-slice acquisition with 1cc voxel size in 6 minutes (b_max = 2430.7 mm²/s) showed a mean ADC value of 0.15*10^-3 and 0.66*10^-3 mm²/s, which is consistent with values reported in the literature. Single-shot high spatial resolution diffusion sensitive MR spectroscopic imaging has the potential to probe metabolite diffusion across extended brain regions.

Guruprasad Krishnamoorthy¹, Matthew M Willmering², Jason C Woods^2,3, and James G Pipe^4
1MR R&D, Philips Healthcare, Rochester, MN, United States, ²Center for Pulmonary Imaging Research, Divisions of Pulmonary Medicine and Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, ³Departments of Pediatrics, Radiology, and Physics, University of Cincinnati College of Medicine, Cincinnati, OH, United States, ⁴Department of Radiology, Mayo Clinic, Rochester, MN, United States

FLORET (Fermat looped, orthogonally encoded trajectories) is an efficient center-out 3D spiral trajectory, and it supports ultra-short echo times. In this work, a new trajectory ordering scheme and FLORET balanced steady-state free precession (FLORET-bSSFP) were developed at 1.5T with inline reconstruction. Simulations, phantom, and human lung imaging were performed to compare the performance of the proposed trajectory ordering with standard ordering. The proposed trajectory ordering scheme minimized artifacts caused by the changing gradient moments while improving temporal stability and motion robustness. High-quality free-breathing lung images were obtained using FLORET-bSFFP and spoiled gradient echo-based UTE FLORET.

David Ma¹, Hortense Le¹, Scott Small^2,3,4, and Jia Guo^2,5
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Department of Psychiatry, Columbia University, New York, NY, United States, ³Department of Neurology, Columbia University, New York, NY, United States, ⁴Taub Institute Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, United States, ⁵Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

Deep learning is an effective image processing approach that has been enthusiastically adopted in Magnetic Resonance Spectroscopy (MRS). Methods such as multilayer perceptrons (MLP) and convolutional neural networks (CNN) have been applied to frequency and phase correction (FPC) to help resolve frequency and phase shifts that arise in MRS. However, both methods need to be trained separately with frequency and phase offsets to perform FPC. In this study, we aim to introduce a spectrum registration technique using CNNs that perform simultaneous correction of both frequency and phase shifts of single voxel MEGA-PRESS MRS simulated data.