Eugene Milshteyn^1,2, Georgy Guryev³, Angel Torrado-Carvajal^1,2,4, Jacob K. White³, Lawrence L. Wald^1,2,5, and Bastien Guerin^1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain, ⁵Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States

Current parallel transmission protocols have conservative safety limits due to offline simulations of generic body models. Instead, a personalized medicine approach should be used, whereby the patient-specific SAR is calculated as the patient lies on the table. In this way, more accurate, and hopefully less conservative safety limits can be employed. In this study, we develop a fast methodology for patient-specific SAR calculations with an 8 channel pTx head coil at 7T. We show a real-time approach in 6 volunteers for scanning the patient, segmenting the body, and performing an electromagnetic simulation in order to generate the local SAR maps.

Merlin J Fair¹ and Kawin Setsompop^1,2
1Radiological Sciences Laboratory, Stanford University, Stanford, CA, United States, ²Electrical Engineering, Stanford University, Stanford, CA, United States

A rapid calibration scan for estimating eddy-currents in diffusion acquisitions was developed using the time-resolved PEPTIDE imaging approach. This calibration scan estimates temporally-varying eddy-current fields across three principal diffusion-directions in <30s, with estimates of eddy-fields across other directions derived through a linear model. The accuracy of this method was validated in simulation, phantom and in-vivo experiments. In a high-SNR diffusion phantom, estimated eddy-fields closely match that of “FSL-eddy” on a large blip-up and blip-down 64-direction EPI dataset. For in-vivo data at b=5000s/mm2, estimates from PEPTIDE-based calibration was able to maintain high-accuracy estimation of the eddy-current field despite low SNR.

Raphael Tomi-Tricot^1,2,3, Jan Sedlacik^2,3, Jonathan Endres⁴, Juergen Herrler⁵, Patrick Liebig⁶, Rene Gumbrecht⁶, Dieter Ritter⁶, Tom Wilkinson^2,3, Pip Bridgen⁷, Sharon Giles⁷, Armin M. Nagel⁴, Joseph V. Hajnal^2,3, Radhouene Neji^1,2, and Shaihan J. Malik^2,3
1MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom, ²Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁴Institute of Radiology, University Hospital Erlangen, Erlangen, Germany, ⁵Institute of Neuroradiology, University Hospital Erlangen, Erlangen, Germany, ⁶Siemens Healthcare GmbH, Erlangen, Germany, ⁷School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Direct Signal Control (DSC) uses parallel transmission (pTx) with more flexibility than conventional static RF shimming to tackle RF inhomogeneity at ultra-high field in fast spin echo (FSE) sequences by varying complex weights of successive RF pulses independently along the refocusing train. Also, unlike other dynamic pTx methods, it preserves RF pulse properties and sequence timing. This work demonstrates the applicability of DSC in routine conditions for neuroimaging, with minimal workflow disruption. In-vivo T2-weighted FSE results exhibit higher signal and better homogeneity when using DSC over RF shimming, while explicitly ensuring safe operation.

Steve C.N. Hui^1,2, Mark Mikkelsen^1,2, Helge J. Zöllner^1,2, Vishwadeep Ahluwalia³, Sarael Alcauter⁴, Laima Baltusis⁵, Deborah A. Barany⁶, Laura R. Barlow⁷, Robert Becker⁸, Jeffrey I. Berman⁹, Adam Berrington¹⁰, Pallab K. Bhattacharyya¹¹, Jakob Udby Blicher¹², Wolfgang Bogner¹³, Mark S. Brown¹⁴, Vince D. Calhoun¹⁵, Ryan Castillo¹⁶, Kim M. Cecil¹⁷, Yeo Bi Choi¹⁸, Winnie C.W. Chu¹⁹, William T. Clarke²⁰, Alexander R. Craven²¹, Koen Cuypers²², Michael Dacko²³, Camilo de la Fuente-Sandoval²⁴, Patricia Desmond²⁵, Aleksandra Domagalik²⁶, Julien Dumont²⁷, Niall W. Duncan²⁸, Ulrike Dydak²⁹, Katherine Dyke³⁰, David A. Edmondson¹⁷, Gabriele Ende⁸, Lars Ersland³¹, C. John Evans³², Alan S. R. Fermin³³, Antonio Ferretti³⁴, Ariane Fillmer³⁵, Tao Gong³⁶, Ian Greenhouse³⁷, James T. Grist³⁸, Meng Gu³⁹, Ashley D. Harris⁴⁰, Katarzyna Hat⁴¹, Stefanie Heba⁴², Eva Heckova¹³, John P. Hegarty II⁴³, Kirstin-Friederike Heise⁴⁴, Aaron Jacobson⁴⁵, Jacobus F.A. Jansen⁴⁶, Christopher W. Jenkins⁴⁷, Stephen J. Johnston⁴⁸, Christoph Juchem⁴⁹, Alayar Kangarlu⁵⁰, Adam B. Kerr⁵, Karl Landheer⁵¹, Thomas Lange⁵², Phil Lee⁵³, Swati Rane Levendovszky⁵⁴, Catherine Limperopoulos⁵⁵, Feng Liu⁵⁶, William Lloyd⁵⁷, David J. Lythgoe⁵⁸, Maro G. Machizawa⁵⁹, Erin L. MacMillan⁷, Richard J. Maddock⁶⁰, Andrei V. Manzhurtsev⁶¹, María L. Martinez-Gudino⁶², Jack J. Miller⁶³, Heline Mirzakhanian⁶⁴, Paul G. Mullins⁶⁵, Jamie Near⁶⁶, Wibeke Nordhøy⁶⁷, Georg Oeltzschner^1,2, Raul Osorio⁶², Maria C.G. Otaduy⁶⁸, Erick H. Pasaye⁴, Ronald Peeters⁶⁹, Scott J. Peltier⁷⁰, Ulrich Pilatus⁷¹, Nenad Polomac⁷¹, Eric C. Porges⁷², Subechhya Pradhan⁵⁵, James Joseph Prisciandaro⁷³, Nick Puts⁷⁴, Caroline D. Rae⁷⁵, Francisco Reyes-Madrigal⁷⁶, Timothy P.L. Roberts⁹, Caroline E. Robertson⁷⁷, Muhammad G. Saleh⁷⁸, Jens T. Rosenberg⁷⁹, Diana-Georgiana Rotaru⁵⁸, O'Gorman Tuura L. Ruth⁸⁰, Kristian Sandberg¹², Ryan Sangill⁸¹, Keith Schembri⁸², Anouk Schrantee⁸³, Natalia A. Semenova⁸⁴, Debra Singel⁸⁵, Rouslan Sitnikov⁸⁶, Jolinda Smith⁸⁷, Yulu Song³⁶, Craig Stark⁸⁸, Diederick Stoffers⁸⁹, Stephan P. Swinnen⁴⁴, Costin Tanase⁶⁰, Sofie Tapper^1,2, Martin Tegenthoff⁴², Thomas Thiel⁹⁰, Marc Thioux⁹¹, Peter Truong⁹², Pim van Dijk⁹¹, Nolan Vella⁸², Rishma Vidyasagar⁹³, Andrej Vovk⁹⁴, Guangbin Wang³⁶, Lars T. Westle⁶⁷, Timothy K. Wilbur⁵⁴, William R. Willoughby⁹⁵, Martin Wilson⁹⁶, Hans-Jörg Wittsack⁹⁷, Adam J. Woods⁹⁸, Yen-Chien Wu⁹⁹, Junqian Xu¹⁰⁰, Maria Yanez Lopez¹⁰¹, David K.W. Yeung¹⁹, Qun Zhao¹⁰², Xiaopeng Zhou²⁹, Gasper Zupan⁹⁴, and Richard A.E. Edden^1,2
1Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³GSU/GT Center for Advanced Brain Imaging, Georgia Institute of Technology, Atlanta, GA, United States, ⁴Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico, ⁵Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States, ⁶Kinesiology, University of Georgia, Athens, GA, United States, ⁷Department of Radiology, The University of British Columbia, Vancouver, BC, Canada, ⁸Center for Innovative Psychiatry and Psychotherpay Research, Department Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ⁹Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, ¹⁰Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom, ¹¹Imaging Institute, The Cleveland Clinic, Cleveland, OH, United States, ¹²Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark, ¹³Department of Biomedical Imaging and Image-guided Therapy, High-Field MR Center, Medical University of Vienna, Vienna, Austria, ¹⁴Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States, ¹⁵Tri-Institutional Center for Translational Research in Neuroimaging and Data Science(TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States, ¹⁶Neuroscience Research AustraliaNeuRA Imaging, Randwick, Australia, ¹⁷Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ¹⁸Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States, ¹⁹Department of Imaging & Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ²⁰Wellcome Centre for Integrative Neuroimaging, NDCN, University of Oxford, Oxford, United Kingdom, ²¹Department of Biological and Medical Psychology, University of Bergen, Haukeland University Hospital, Bergen, Norway, ²²REVAL Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium, ²³Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ²⁴Laboratory of Experimental Psychiatry & Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico, ²⁵Department of Radiology, University of Melbourne/ Royal Melbourne Hospital, Melbourne, Australia, ²⁶Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland, ²⁷Clinical Imaging Core Facility, CI2C Lille, Lille, France, ²⁸Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan, ²⁹School of Health Sciences, Purdue University, West Lafayette, IN, United States, ³⁰School of Psychology, University of Nottingham, Nottingham, United Kingdom, ³¹Department of Clinical Engineering, University of Bergen, Haukeland University Hospital, Bergen, Norway, ³²CUBRIC, Cardiff University, Cardiff, United Kingdom, ³³Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan, ³⁴Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy, ³⁵Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Germany, ³⁶Department of Imaging and Nuclear Medicine, Shandong Medical Imaging Research Institute, Shandong University, Jinan, China, ³⁷Human Physiology, University of Oregon, Eugene, OR, United States, ³⁸Physiology, Anatomy, and Genetics/ Oxford Centre for Magnetic Resonance, The University of Oxford / Department of Radiology, The Churchill Hospital, The University of Oxford, Oxford, United Kingdom, ³⁹Department of Radiology, Stanford University, Stanford, CA, United States, ⁴⁰Department of Radiology, University of Calgary, Calgary, AB, Canada, ⁴¹Institute of Psychology, Jagiellonian University, Krakow, Poland, ⁴²Department of Neurology, BG University Hospital Bergmannsheil, Bochum, Germany, ⁴³Psychiary & Behavioral Sciences, Stanford University, Stanford, CA, United States, ⁴⁴Department of Movement Sciences, KU Leuven, Leuven, Belgium, ⁴⁵Department of Radiology, University of California San Diego, San Diego, CA, United States, ⁴⁶Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands, ⁴⁷CUBRIC, Cardiff university, Cardiff, United Kingdom, ⁴⁸Psychology Dept. / Clinical Imaging Facility, Swansea University, Swansea, United Kingdom, ⁴⁹Biomedical Engineering and Radiology, Columbia University, New York City, NY, United States, ⁵⁰Psychiatry, Columbia University Irving Medical Center/New York State Psychiatric Institute, New York City, NY, United States, ⁵¹Biomedical Engineering, Columbia University, New York City, NY, United States, ⁵²Department of Radiology, Medical Physics, University of Freiburg, Freiburg, Germany, ⁵³Department of Radiology, University of Kansas Medical Center, Kansas, KS, United States, ⁵⁴Department of Radiology, University of Washington, Seattle, WA, United States, ⁵⁵Developing Brain Institute, Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, United States, ⁵⁶Department of Psychiatry, Columbia University Irving Medical Center/New York State Psychiatric Institute, New York, NY, United States, ⁵⁷Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom, ⁵⁸Department of Neuroimaging, King's College London, London, United Kingdom, ⁵⁹Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan, ⁶⁰Psychiatry and Behavioral Sciences, University of California Davis, Imaging Research Center, Davis, CA, United States, ⁶¹Department of Radiology, Clinical and Research Institute of Emergency Pediatric Surgery and Trauma, Moscow, Russian Federation, ⁶²Imágenes Cerebrales, Instituto Nacional de Psiquiatría Ramón de la Fuente, Mexico City, Mexico, ⁶³Department of Physics, University of Oxford, Oxford, United Kingdom, ⁶⁴Department of Psychiatry, University of California San Diego, San Diego, CA, United States, ⁶⁵Department of Psychology, Bangor University, Bangor, United Kingdom, ⁶⁶Douglas Mental Health University Institute and Department of Psychiatry, McGill University, Montreal, QC, Canada, ⁶⁷Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, ⁶⁸LIM44, Instituto e Departamento de Radiologia, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil, ⁶⁹Department of Imaging & Pathology, Department of Radiology, University Hospitals Leuven, KU Leuven, Leuven, Belgium, ⁷⁰Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ⁷¹Institute of Neuroradiology, Goethe-University Frankfurt, Frankfurt, Germany, ⁷²Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States, ⁷³Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States, ⁷⁴Forensic & Neurodevelopmental Sciences, King's College London, London, United Kingdom, ⁷⁵NeuRA Imaging, Neuroscience Research Australia, Randwick, Australia, ⁷⁶Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico, ⁷⁷Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States, ⁷⁸Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, ⁷⁹McKnight Brain Institute, AMRIS, University of Florida, Gainesville, FL, United States, ⁸⁰Center for MR Research, University Children's Hospital, Zurich, Switzerland, ⁸¹Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus, Denmark, ⁸²Medical Physics, Mater Dei Hospital, Imsida, Malta, ⁸³Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands, ⁸⁴504, Emanuel Institute of Biochemical Physics of the Russian Academy of Sciences, Moscow, Russian Federation, ⁸⁵Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States, ⁸⁶Clinical Neuroscience, MRI Centre, Karolinska Institutet, Clinical Neuroscience, MRI Centre, Sweden, ⁸⁷Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, United States, ⁸⁸Department of Neurobiology and Behavior, Facility for Imaging and Brain Research (FIBRE) & Campus Center for Neuroimaging (CCNI), University of California, Irvine, Irvine, CA, United States, ⁸⁹Spinoza Centre for Neuroimaging, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands, ⁹⁰Institute of Clinical Neuroscience and Medical Psychology, University Dusseldorf, Medical Faculty, Düsseldorf, Germany, ⁹¹Otorhinolaryngology, Head and Neck Surgery, University of Groningen, University Medical Center Groningen, Groningen, Netherlands, ⁹²Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada, ⁹³Melbourne Dementia Research Centre, Florey Institute of Neurosciences and Mental Health, Melbourne, Australia, ⁹⁴Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia, ⁹⁵Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States, ⁹⁶Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom, ⁹⁷Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Düsseldorf, Germany, ⁹⁸Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, College of Public Health and Health Professions. Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, United States, ⁹⁹Department of Radiology, TMU-Shuang Ho Hospital, New Taipei City, Taiwan, ¹⁰⁰Department of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, United States, ¹⁰¹Perinatal Imaging & Health, King's College London, London, United Kingdom, ¹⁰²Bioimaging Research Center, University of Georgia, Athens, GA, United States

This project aimed to examine the relationship between gradient-induced heating and field drift on a large sample of MRI scanners. A standardized phantom protocol was established, and spectroscopy was performed before and after running 10 minutes of echo-planar imaging (EPI). MRS data were acquired from 87 scanners. The frequency drift trace was extracted by modeling the water signal in each transient. Drift rates of up to 1.3 Hz/minute were seen before EPI, and 4 Hz/minute after. This dataset will allow sites to benchmark scanner drift, for consideration in planning research protocol order and examine the need for real-time field-frequency locking.

Mirsad Mahmutovic¹, Alina Scholz¹, Nicolas Kutscha¹, Markus W. May¹, Torsten Schlumm², Roland Müller², Kerrin Pine², Luke J. Edwards², Nikolaus Weiskopf^2,3, David O. Brunner⁴, Harald E. Möller², and Boris Keil^1
1Institute of Medical Physics and Radiation Protection, TH Mittelhessen University of Applied Sciences, Giessen, Germany, ²Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany, ⁴Skope Magnetic Resonance Technologies AG, Zurich, Switzerland

Combining highly parallel array coils, magnetic field monitoring, and high gradient strength provide a complementary approach to enhance diffusion-weighted imaging (DWI). A 64-channel receive brain array with an incorporated 16-channel field camera system was developed to be used with 300 mT/m gradients from the 3T Connectom scanner. The increased signal-to-noise ratio (SNR) and implemented parallelism was highly beneficial, and will improve SNR-starved high-b value DWI acquisitions, while the magnetic field monitoring data successfully corrected the images from blurring, aliasing and distortions.

Nam G. Lee¹, Rajiv Ramasawmy², Adrienne E. Campbell-Washburn², and Krishna S. Nayak^1,3
1Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ²Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States, ³Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States

Non-Cartesian imaging can suffer from local blurring caused by concomitant fields and off-resonance. Concomitant fields are especially problematic when using prolonged non-Cartesian readouts with high gradient amplitudes at lower field strengths. We present a new reconstruction method, denoted MaxGIRF, for non-Cartesian imaging that corrects concomitant fields and trajectory errors without specialized hardware. The proposed method utilizes gradient impulse response functions to predict gradients waveforms which are in-turn used to estimate concomitant fields with analytic expressions. Image artifacts were successfully mitigated by the proposed method from 2D SE spiral imaging of the human brain acquired on a prototype 0.55T MRI system.

Niek RF Huttinga¹, Tom Bruijnen¹, Cornelis AT van den Berg¹, and Alessandro Sbrizzi^1
1Department of Radiotherapy, Computational Imaging Group for MR therapy & Diagnostics, University Medical Center Utrecht, Utrecht, Netherlands

We present a probabilistic framework to perform simultaneous real-time 3D motion estimation and uncertainty quantification. We extend our preliminary work to a realistic prospective in-vivo setting, and demonstrate it on an MR-LINAC. The acquisition+processing time for 3D motion-fields is around 15ms, yielding a 67Hz frame-rate. Results indicate high quality predictions, and uncertainty estimates that could be used for real-time quality assurance during MR-guided radiotherapy on an MR-LINAC. 

Christian R. Meixner¹, Sebastian Schmitter^2,3, Jürgen Herrler⁴, Arnd Dörfler⁴, Michael Uder¹, and Armin M. Nagel^1,3
1Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, ²Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Germany, ³Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ⁴Institute of Neuro-Radiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany

4D-angiography exploiting pseudo-continuous arterial spin labeling at 7T suffers from specific absorption rate constraints, low B₁⁺ efficiency for the labeling and B₁⁺ inhomogeneity in the readout. In this work, we propose a B₁⁺-phase shim trading B₁⁺ homogeneity and transmit efficiency for the labeling combined with a dynamic transmission 2-spoke excitation readout. In volunteer measurements, the proposed approach outperformed the standard circular polarized mode by an increased vessel intensity and more vessel conspicuity.

Fikunwa O. Kolawole^1,2, Tyler Edward Cork^2,3,4, Michael Loecher^2,3, Judith Zimmermann^3,5, Seraina A. Dual³, Marc E. Levenston^1,3, and Daniel B. Ennis^2,3
1Mechanical Engineering, Stanford University, Stanford, CA, United States, ²Radiology, Veterans Administration Health Care System, Palo Alto, CA, United States, ³Radiology, Stanford University, Stanford, CA, United States, ⁴Bioengineering, Stanford University, Stanford, CA, United States, ⁵Computer Science, Technical University of Munich, Garching, Germany

Cardiac MRI and finite element based techniques can be used to obtain subject-specific myocardial material properties. Verifying the accuracy and precision of these techniques requires overcoming the challenge of obtaining ground-truth in vivo myocardial stiffness estimates. This work presents a highly controlled in vitro diastolic filling setup incorporating a 3D-printed heart phantom developed with myocardial tissue-mimicking material of known mechanical and MRI properties. The setup enables acquisition of the data needed to estimate myocardial stiffness in computational models: phantom geometry, loading pressures, boundary conditions, and filling strains. This setup is designed to enable extensive validation of myocardial stiffness estimation frameworks.

Martin Dawson Holland¹, Andres Morales¹, Sean Simmons², Brandon Smith¹, Samuel R Misko¹, Roy P Koomullil¹, Junzhong Xu³, David A Hormuth, II⁴, Junzhong Xu³, Thomas E Yankeelov⁴, and Harrison Kim^1
1University of Alabama at Birmingham, Birmingham, AL, United States, ²Objective Design, Birmingham, AL, United States, ³Vanderbilt University Medical Center, Nashville, TN, United States, ⁴University of Texas, Austin, TX, United States

A new point-of-care portable perfusion phantom was developed to reduce inter- and intra-scanner variability of quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).  This device is disposable, easily operable, and conveniently deliverable for widespread, routine clinical use.  As this device has high repeatability (intraclass correlation coefficient = 0.997), it can be utilized to improve the accuracy of quantitative DCE-MRI based analysis of many diseases including cancer.