Shohei Fujita^1,2, Guido Buonincontri^3,4, Matteo Cencini^3,5, Naoyuki Takei⁶, Rolf F. Schulte⁷, Issei Fukunaga¹, Akifumi Hagiwara¹, Wataru Uchida^1,8, Masaaki Hori⁹, Ryusuke Irie^1,2, Koji Kamagata¹, Osamu Abe², and Shigeki Aoki^1
1Department of Radiology, Juntendo University, Tokyo, Japan, ²Department of Radiology, The University of Tokyo, Tokyo, Japan, ³Imago7 Foundation, Pisa, Italy, ⁴IRCCS Stella Maris, Paris, Italy, ⁵Department of Physics, University of Pisa, Pisa, Italy, ⁶MR Applications and Workflow, GE Healthcare, Tokyo, Japan, ⁷GE Healthcare, Munich, Germany, ⁸Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo, Japan, ⁹Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan

Magnetic Resonance fingerprinting (MRF) provides simultaneous acquisition of T1 and T2 values with high reliability. However, the reproducibility and repeatability of human brain morphometry based on MRF still requires investigation. Here, we examined the feasibility of three-dimensional (3-D) MRF to evaluate the brain cortical thickness and volumetric analysis in healthy volunteers. Scan-rescan tests of both 3-D MRF and conventional 3D T1-weighted imaging were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using automatic brain segmentation software. High agreement between conventional scans and scan-rescan repeatability in healthy human brains were observed.

Ruiyang Zhao^1,2, Diego Hernando^1,2, David T Harris¹, Louis Hinshaw³, Ke Li^1,2, Jessica Miller⁴, Perry J Pickhardt¹, Ihab R Kamel⁵, Mahadevappa Mahesh⁵, Mounes Aliyari Ghasabeh⁵, Mustafa R Bashir^6,7,8, Jean Shaffer^6,7, Carolyn Lowry⁶, Daniele Marin⁶, Takeshi Yokoo⁹, Lakshmi Ananthakrishnan⁹, Xinhui Duan⁹, and Scott B Reeder^{1,2,3,10,11
1}Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ³Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁴Human Oncology, University of Wisconsin-Madison, Madison, WI, United States, ⁵Radiology, John Hopkins University, Baltimore, MD, United States, ⁶Radiology, Duke University, Durham, NC, United States, ⁷Center for Advanced Magnetic Resonance Development, Duke University, Durham, NC, United States, ⁸Medicine, Duke University, Durham, NC, United States, ⁹Radiology, University of Texas Southwestern, Dallas, TX, United States, ¹⁰Medicine, University of Wisconsin-Madison, Madison, WI, United States, ¹¹Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Accurate quantification of liver fat content is needed for early detection, staging, and treatment monitoring of non-alcoholic fatty liver disease. Chemical shift encoded MRI techniques enable accurate fat quantification though proton density fat fraction maps. CT is capable of quantifying fat based on the decrease in attenuation with increasing liver fat concentration. Current MR quantitative fat phantoms do not accurately mimic CT-based attenuation in the presence of liver fat. Therefore, the purpose of this work was to develop and validate the performance of a novel multimodality phantom that mimics the signals of liver fat in both MRI and CT.

Ruvini Navaratna^1,2, Timothy J Colgan¹, Ruiyang Zhao^1,2, Houchun Harry Hu³, Mark Bydder⁴, Takeshi Yokoo⁵, Mustafa R Bashir^6,7,8, Michael S Middleton⁹, Suraj D Serai¹⁰, Daria Malyarenko¹¹, Thomas Chenevert¹¹, Mark Smith³, Walter Henderson⁹, Gavin Hamilton⁹, Yunhong Shu¹², Claude B Sirlin⁹, Jean A Tkach¹³, Andrew T Trout¹³, Jean H Brittain¹⁴, Diego Hernando^1,2, and Scott B Reeder^{1,2,15,16,17
1}Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ³Radiology, Nationwide Children's Hospital, Columbus, OH, United States, ⁴Radiological Sciences, University of California - Los Angeles, Los Angeles, CA, United States, ⁵Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States, ⁶Radiology, Duke University Medical Center, Durham, NC, United States, ⁷Division of Gastroenterology, Duke University Medical Center, Durham, NC, United States, ⁸Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, United States, ⁹Radiology, University of California - San Diego, San Diego, CA, United States, ¹⁰Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, ¹¹Radiology, University of Michigan, Ann Arbor, MI, United States, ¹²Radiology, Mayo Clinic, Rochester, MN, United States, ¹³Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ¹⁴Calimetrix, LLC, Madison, WI, United States, ¹⁵Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ¹⁶Medicine, University of Wisconsin-Madison, Madison, WI, United States, ¹⁷Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Chemical shift-encoded MRI (CSE-MRI) is well-established to quantify proton density fat-fraction (PDFF) as a quantitative biomarker of hepatic steatosis.¹ However, temperature is known to affect the accuracy and precision of PDFF quantification.² In this study­­­, we aim to characterize the effects of temperature on PDFF quantification using computer simulations, temperature-controlled phantom experiments, and a multi-center phantom study. Further, we present a novel method to minimize temperature-related fat quantification bias for magnitude-based CSE-MRI methods.

Matteo Mancini^1,2,3, Eva Alonso-Ortiz², Mara Cercignani¹, Julien Cohen-Adad², and Nikola Stikov^2
1Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom, ²NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada, ³CUBRIC, Cardiff University, Cardiff, United Kingdom

Are myelin-sensitive maps interchangeable? We performed a scan-rescan study in 5 subjects using T1 mapping, magnetization transfer and myelin water fraction. We found overall that all metrics have high scan-rescan repeatability and that they are highly correlated with each other. However, isolating the main factor behind this shared variance is not straightforward because of the different interplays between the measures. In the end, what will matter is to what extent these relationships are preserved in the presence of pathology.

Gian Franco Piredda^1,2,3, Peipeng Liang⁴, Tom Hilbert^1,2,3, Hongjian He⁵, Jean-Philippe Thiran^2,3, Yi Sun⁶, Jianhui Zhong^5,7, Kuncheng Li^8,9, and Tobias Kober^1,2,3
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ²Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ³LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁴School of Psychology, Capital Normal University, Beijing Key Laboratory of Learning and Cognition, Beijing, China, ⁵Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China, ⁶MR Collaboration, Siemens Healthcare Ltd., Shanghai, China, ⁷Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, ⁸Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China, ⁹Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China

It was recently shown that brain atlases of normative relaxation times enable automated detection of tissue alterations on a single-subject basis. In this work, normative quantitative T₁ and T₂ atlases were obtained from a large-scale adult cohort of healthy volunteers (#997) covering a comprehensive age range (19-72y) in a multi-centric study including eleven sites. Atlases were derived by linearly modelling the inter-subject variability of T₁/T₂ while accounting for effects such as gender and age differences. Travelling subjects were scanned in nine centers with the same protocol, the comparison of the acquired maps showed good reproducibility of the employed relaxometry sequences.

Mikael Brudfors¹, Yaël Balbastre¹, John Ashburner¹, Siawoosh Mohammadi^2,3, and Martina F Callaghan^1
1Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom, ²Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ³Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Intra-scan motion is a common source of artefacts in magnetic resonance imaging (MRI), which cannot be easily corrected. However, in quantitative MRI (qMRI), several volumes with varying parameters are acquired, and some sort of data redundancy exists. In this abstract, we propose a general framework where corrupted voxels are treated as missing entries and imputed using a Bayesian model of differently weighted MRI volumes. We demonstrate its efficacy in the context of various multi-parameter mapping (MPM) qMRI protocols, in which one volume is corrupted by motion. We show that the model can efficiently recover the corrupted data without introducing bias.

Gregor Körzdörfer¹, Pedro Lima Cardoso², Peter Bär², Simone Kitzer², Wolfgang Bogner^2,3, Siegfried Trattnig^2,3, and Mathias Nittka^1
1Siemens Healthcare GmbH, Erlangen, Germany, ²High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ³Christian Doppler Laboratory for Clinical Molecular MR Imaging, MOLIMA, Vienna, Austria

In contrast to qualitative MRI, motion artifacts can be more subtle in quantitative MRI methods such as Magnetic Resonance Fingerprinting (MRF). Errors caused by motion are not easily detectable by visual inspection of resulting maps. Hence, there is clear need for supporting the reliability of results with regard to motion-induced errors. We present a method to detect if significant through-plane motion occurred during an MRF scan, without external motion tracking devices or acquiring additional data. The method is based on classifying the spatiotemporal residuals either by eye or a neural network. The performance was successfully evaluated in a patient study.

Suma Anand¹, Adam Michael Bush², Christopher Michael Sandino³, Shreyas Vasanawala², and Michael Lustig^1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, ²Radiology, Stanford University, Palo Alto, CA, United States, ³Electrical Engineering, Stanford University, Palo Alto, CA, United States

Obesity is a major cause of preventable morbidity and mortality in the US. A growing body of work suggests that triglyceride composition and its spatial distribution play a central role in this epidemic, necessitating the need for better non-invasive fat imaging. We propose a motion-robust acquisition scheme that combines the spatial resolution of MRI and the spectral resolution of MR spectroscopy using 2D multi-echo rosette k-space sampling. We validate the method with an oil phantom and demonstrate its motion robustness with a free-breathing in vivo acquisition.

Li Feng¹, Kai Tobias Block², Thomas Benkert³, Ye Tian⁴, Chenyu Liu¹, Fang Liu^5,6, Zahi Fayad¹, and Yang Yang^1
1Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, United States, ³MR Applications Development, Siemens Healthcare GmbH, Erlangen, Germany, ⁴Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, ⁵Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ⁶Department of Radiology, University of Wisconsin Madison, Madison, WI, United States

This work presents a framework for inversion-recovery (IR)-prepared stack-of-stars imaging and its applications for rapid free-breathing 3D liver MRI. Building upon a previously developed stack-of-stars 3D GRE sequence (RAVE: RAdial Volumetric Encoding), a non-selective 180^o IR pulse has been implemented that is periodically played-out to achieve IR preparation (IR-Prepped RAVE). The new sequence allows (1) single-echo acquisition in combination with GRASP-Pro (imProved Golden-angle RAdial Sparse Parallel) reconstruction for free-breathing volumetric T1 mapping of the liver, and (2) multi-echo acquisition in combination with dynamic model-based reconstruction for IR-prepped and contrast-resolved fat/water separation.

Tobias Streubel^1,2, Leonie Klock³, Martina Callaghan⁴, Simone Kühn^3,5, Antoine Lutti⁶, Karsten Tabelow⁷, Nikolaus Weiskopf², Gabriel Ziegler^8,9, and Siawoosh Mohammadi^1,2
1Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ²Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ⁴Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, London, United Kingdom, ⁵Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany, ⁶Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne, Switzerland, ⁷Stochastic Algorithms and Nonparametric Statistics, Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany, ⁸Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University, Magdeburg, Germany, ⁹German Center for Neurodegenerative Diseases, Magdeburg, Germany

We introduced novel error maps for proton density, longitudinal relaxation and magnetization transfer saturation rates that are more sensitive to artifacts than previously used error measures. We showed that they can be used to identify and down weigh local errors in the quantitative parameter maps for an experiment consisting of two successive multi-parameter mapping (MPM) measurements in a group of 10 healthy subjects.

Teresa Correia¹, Torben Schneider², and Amedeo Chiribiri^1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Philips Healthcare, Guildford, United Kingdom

First-pass perfusion cardiac MR (FP-CMR) is becoming essential for evaluating myocardial ischemia. However, FP-CMR requires ECG-gating and breath-holding, leading to a trade-off between spatial resolution and coverage. Moreover, perfusion abnormalities are often identified visually by highly trained operators. Recently, quantitative FP-CMR and compressed sensing (CS) have been proposed to reduce operator-dependency and moderately accelerate acquisitions, respectively. Here, a model-based reconstruction is proposed to directly estimate quantitative myocardial perfusion maps from highly undersampled acquisitions. Thus, allowing for higher spatial resolution and coverage than indirect methods, where dynamic images are reconstructed using CS and quantitative maps are obtained subsequently using tracer-kinetic modeling.

Shouchang Guo¹, Douglas C. Noll², and Jeffrey A. Fessler^1
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, ²Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Oscillating Steady-State Imaging (OSSI) is a new fMRI acquisition method that can provide high SNR signals, but does so at the expense of imaging time. We previously used a physics-based regularizer for high-quality, undersampled reconstruction by modeling the oscillating signal with physics parameters. However, the reconstructions were not quantitative, as the key parameter $$$T_2'$$$ for BOLD effects was not studied. In this work, to quantify MRI parameters of physiological importance, we jointly reconstruct the images and the parameters. The proposed manifold model reconstructs high-resolution images from 12-fold undersampled data, while also providing quantitative $$$T_2'$$$ estimates for fMRI.

Zhengguo Tan^1,2, Peter Dechent³, Xiaoqing Wang^1,2, Nick Scholand^1,2, Dirk Voit⁴, Jens Frahm^2,4, and Martin Uecker^1,2
1Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany, ²German Center for Cardiovascular Research (DZHK), Göttingen, Germany, ³Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany, ⁴Biomedizinische NMR, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany

To achieve dynamic and simultaneous access to R2* relaxation rates, B0 field inhomogeneities and water/fat separation, we developed a model-based reconstruction technique on BART for continuous acquisitions based on undersampled multi-echo multi-spoke radial FLASH. Beside spatial smoothness constraints on coil sensitivity and B0 field maps, L1 wavelet regularization is applied to the water, fat and R2* maps. Preliminary results of brain fMRI data demonstrate significant T2* change from 37 to 67 ms in the occipital visual cortex. In addition, R2* mapping of free-breathing liver with only 15 RF shots per frame (194 ms temporal resolution) reveals increased R2* during inspiration.

Steven T. Whitaker¹, Gopal Nataraj², Jon-Fredrik Nielsen³, and Jeffrey A. Fessler^1
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, ²Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ³Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Myelin water fraction (MWF) estimates are desirable for tracking the progression of demyelinating diseases such as multiple sclerosis. To address the long scan times of conventional MWF imaging methods, faster steady-state scans have been studied recently. One such steady-state scan is small-tip fast recovery (STFR). This work compares STFR-based MWF estimates using a two-compartment tissue model without exchange to those obtained using a three-compartment tissue model with exchange. Using a three-compartment model with exchange results in MWF estimates that are closer to traditional multi-echo spin echo (MESE) estimates.

Brendan Lee Eck^1,2, Jeehun Kim², Mingrui Yang², and Xiaojuan Li^2
1Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, OH, United States, ²Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States

Quantitative T_1ρ imaging has been studied for evaluating changes in tissue composition, in particular for detecting early cartilage degeneration in osteoarthritis. Magnetic resonance fingerprinting (MRF) provides a framework for rapid, robust acquisition of quantitative tissue property maps. Simulation experiments using spin-lock prepared MRF with different pulse schedules were conducted to demonstrate the feasibility of quantification of T_1ρ in addition to T₁ and T₂. All tested sequences were sensitive to T_1ρ and produced tissue property maps with major structures of interest. Differing accuracy and precision between sequences suggests opportunities for optimizing MRF for simultaneous T₁, T₂, and T_1ρ quantification.