Fuyixue Wang^1,2, Zijing Dong^1,3, Timothy G. Reese¹, Lawrence L. Wald^1,2, and Kawin Setsompop^1,2
1A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, ³3Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States

An efficient quantitative mapping sequence based on 3D Echo-Planar Time-resolved Imaging (3D-EPTI) is proposed. The acquisition contains inversion recovery gradient echo readouts follow by GRASE-like readouts to provide sensitivity to T₁, T₂ and T₂*. Fast k-TI-TE coverage is achieved by fusing highly-accelerated spatiotemporal CAIPI sampling with golden-angle radial-blade Cartesian under-sampling, where the reconstruction is performed using the subspace model. We demonstrate the high-efficiency of the proposed  technique by obtaining multi-contrast images and quantitative maps at 1-mm isotropic resolution whole-brain in 3 minutes.

Sen Ma^1,2, Anthony G. Christodoulou², Nan Wang^1,2, Marwa Kaisey³, Nancy L. Sicotte³, and Debiao Li^1,2
1Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ²Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ³Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Quantitative multi-parametric relaxometry MRI (e.g., T1, T2, and T1ρ mapping) can demonstrate longitudinal brain changes and enhance lesion contrasts against normal appearing matters in multiple sclerosis. Conventional methods that quantify these relaxation parameters are time-consuming and subject to motion, thus challenging for clinical practice. We present a novel approach that simultaneously quantifies T1, T2, and T1ρ with whole brain coverage in 9min, using the recently developed Multitasking framework that models the multidimensional image as a low-rank tensor. This technique is validated on healthy volunteers. We also demonstrate the feasibility of lesion characterization on relapsing remitting multiple sclerosis patients.

Le Zhang¹, Shu-Fu Shih^1,2, Tess Armstrong^1,3, and Holden H. Wu^1,2,3
1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Physics and Biology in Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Quantification of T₁, proton-density fat fraction (PDFF), and R₂* in the liver can provide information about a range of diseases. Existing Cartesian acquisition schemes generally require breath-holding, which limits spatial coverage and may be difficult for sick, elderly or pediatric patients. In this study, we propose a variable-flip-angle (VFA) golden-angle-ordered (GA) 3D stack-of-radial sequence that can provide multiparametric mapping with volumetric liver coverage in three minutes during free-breathing and with intrinsic motion compensation capability. Pilot studies in healthy subjects demonstrate agreement with reference breath-held scans and good measurement repeatability.

Naoyuki Takei¹, David Shin², Dan Rettmann³, Shohei Fujita^4,5, Issei Fukunaga⁴, Akifumi Hagiwara⁴, Ken-Pin Hwang⁶, Marcel Warntjes⁷, Shigeki Aoki⁴, Suchandrima Banerjee², and Hiroyuki Kabasawa^1
1GE Healthcare, Tokyo, Japan, ²GE Healthcare, Menlo Park, CA, United States, ³GE Healthcare, Rochester, MN, United States, ⁴Juntendo University School of Medicine, Tokyo, Japan, ⁵The University of Tokyo Graduate School of Medicine, Tokyo, Japan, ⁶The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States, ⁷SyntheticMR, Linkoping, Sweden

To aim for reliable parametric mapping to motion artifact, prospective motion correction was integrated to a multi-parametric technique, 3D QALAS. 2D Spiral navigators were inserted into wait times in the QALAS without impacting scan time for motion tracking and correction. The effectiveness of prospective motion correction was demonstrated. The proposed technique is expected to yield prospectively motion corrected 3D brain volumetric images of multiple contrasts and quantitative mappings.

Tianle Cao^1,2, Nan Wang^1,2, Sen Ma^1,2, Yibin Xie¹, Sara Gharabaghi³, E. Mark Haacke^3,4,5, Anthony G. Christodoulou¹, and Debiao Li^1
1Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States, ²Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, United States, ³Magnetic Resonance Innovations, Inc, Bingham Farms, MI, United States, ⁴Wayne State University School of Medicine, Detroit, MI, United States, ⁵The MRI Institute for Biomedical Research, Bingham Farms, MI, United States

A new approach for simultaneous quantitative mapping of T1, R2* and susceptibility was presented in this work. This technique employed IR pulses followed by N=152 segments of multi-echo FLASH readout. We were able to reconstruct the images for each echo time and inversion time under the multitasking framework for furhther analysis. Results of both visual comparison and statistical analysis showed that our proposed method agreed well with the reference but were more time efficient and robust to interscan motion.

Florian Wiesinger^1,2, Emil Ljungberg², Mathias Engström³, Sandeep Kaushik¹, Tobias Wood², Steven Williams², Gareth Barker², and Ana Beatriz Solana^1
1GE Healthcare, Munich, Germany, ²King’s College London, London, United Kingdom, ³GE Healthcare, Stockholm, Sweden

Here we describe a new framework for 3D, high-resolution Parameter mapping in a Swift and SilenT manner, termed PSST.  The method combines T1 and T2 contrast preparation with segmented, silent, zero TE (ZTE) image encoding and an analytical signal model.  Four canonical schemes are presented and demonstrated in phantom and in-vivo brain experiments.

Rahel Heule¹, Jonas Bause¹, Orso Pusterla^2,3,4, and Klaus Scheffler^1,5
1High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, ²Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, ³Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, ⁴Department of Biomedical Engineering, University of Basel, Basel, Switzerland, ⁵Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany

Prominent asymmetries in the bSSFP frequency profile in tissues with distinct fiber pathways are known to be a confounding factor in the quantification of relaxation times from a series of phase-cycled scans. It has been demonstrated that the resulting bias can be eliminated by training artificial neural networks using gold standard relaxation times as target. Here, the ability of neural networks to not only provide gold standard brain tissue T₁ and T₂ values as well as field map estimates (B₁, ∆B₀) but also to highly accelerate the acquisition by reducing the number of phase-cycles is explored.

Gilad Liberman¹, Fuyixue Wang¹, Zijing Dong¹, and Kawin Setsompop^1
1Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States

Model-based reconstruction approaches benefit from tight representation of the signal and from optimization on meaningful quantitative parameter maps, while requiring advanced algorithms and increased computational resources. We propose a generalized iterative thresholding algorithm with parameter splitting for model-based reconstruction,  along with an efficient implementation. The approach is flexible and generalizable to problems in various MRI domains. We demonstrate it on the common image with phase evolution and signal decay model tackled with multi-echo GRE and Echo-Planar Time-resolved Imaging (EPTI), resulting in better image quality in comparison with GRAPPA and subspace constrained reconstruction, and increased z-scores in a low-SNR functional experiment.

Nick Scholand^1,2, Xiaoqing Wang^1,2, Sebastian Rosenzweig^1,2, H. Christian M. Holme^1,2, and Martin Uecker^1,2
1Department of Interventional and Diagnostic Radiology, University Medical Center, Göttingen, Germany, ²German Centre for Cardiovascular Research, Göttingen, Germany

Conventional quantitative MRI estimates parameters by fitting a known analytical signal model to pixels of images with different contrasts. By combining image reconstruction and the signal model into one non-linear inverse problem, model-based reconstruction methods can estimate the parameters directly from k-space. Avoiding the acquisition and reconstruction of intermediate images they require much less data. Furthermore, they can be directly combined with parallel imaging and compressed sensing, but still rely on analytical models and carefully designed MRI sequences.
Here, we generalize this framework to work with arbitrary sequences using a Runge-Kutta based simulation of spin dynamics.
 

Oscar van der Heide^1,2, Alessandro Sbrizzi^1,2, Tom Bruijnen^1,3, and Cornelis van den Berg^1,2
1Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Radiology, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands, ³Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands

MR-STAT is a framework for obtaining multi-parametric quantitative MR maps using data from single short scans. A single large-scale optimization problem is solved in which spatial localisation of signal and estimation of tissue parameters are performed simultaneously. In previous work, MR-STAT was presented using gradient-balanced sequences with linear, Cartesian readouts. To demonstrate the generic nature of the MR-STAT framework and to explore potentially more efficient acquisition schemes, we extend MR-STAT to non-Cartesian gradient trajectories as well as gradient-spoiled sequences. We compare the our results from golden angle radial, gradient-spoiled acquisitions to low-rank ADMM MRF reconstructions on the same data sets.