Chenyang Zhao¹, Kai Wang¹, Christina Graf², Rudolf Stollberger², and Danny JJ Wang^1,3
1Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ²Institute of Medical Engineering, Graz University of Technology, Graz, Austria, ³Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States

Background suppression (BS) of pseudo-Continuous Arterial Spin Labeling (pCASL) needs to be optimized at 7T to ameliorate the ASL signal loss caused by the low inversion efficiency (IE) of BS due to B1 inhomogeneity. In this study, the original HS pulse used as BS at 3T was assessed at 7T, and four non-selective inversion pulses, including HS, WURST, OPTIM, and pTx adiabatic, were optimized and evaluated in phantom and in-vivo studies. Less than 80% IE achieved by the original HS pulse was improved to 85% by optimized pulses. Markedly higher tSNR was reported in pCASL with the optimized pulses.

Sara Pires Monteiro¹, Joana Pinto², Michael Chappell³, Ana Fouto¹, Miguel Viana-Baptista⁴, Pedro Vilela⁵, and Patrícia Figueiredo^1
1Department of Bioengineering, ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal, ²Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, ³Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nothingam, United Kingdom, ⁴Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental; CEDOC - Nova Medical School, New University of Lisbon, Lisbon, Portugal, ⁵Imaging Department, Hospital da Luz, Lisbon, Portugal

Arterial spin labeling (ASL) acquisitions at multiple post-labeling delays allow for appropriate kinetic models to be fitted to the data, potentially providing more accurate perfusion quantification. We investigated the influence of denoising strategies based on repetition averaging and ICA, together with the choice of an extended kinetic model with or without dispersion, on multi-delay ASL measurements from a group of small vessel disease patients and their age-matched controls. While ICA denoising generally improved model fitting, repetition averaging interacted with modeling dispersion and subject group, significantly impacting the estimation of perfusion and macrovascular contributions, mostly in arterial locations and with pathology.

Flora Kennedy McConnell^1,2,3, Jack Toner^1,2, Thomas Kirk^4,5, Yuriko Suzuki⁵, Martin Craig^1,2, Timothy S. Coalson⁶, Michael P. Harms⁷, Matthew F. Glasser^6,8, and Michael A. Chappell^1,2,3,5
1Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ²Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ³Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom, ⁴Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom, ⁵Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ⁶Department of Neuroscience, Washington University School of Medicine, Washington University in St Louis, St Louis, MO, United States, ⁷Department of Psychiatry, Washington University School of Medicine, Washington University in St Louis, St Louis, MO, United States, ⁸Department of Radiology, Washington University School of Medicine, Washington University in St Louis, St Louis, MO, United States

The arterial spin labelling (ASL) data from the Human Connectome Project (HCP) Lifespan studies can provide a source of unusually high-resolution hemodynamic measures from >2500 individuals with ages 5-21 and 37-100+. This work presents a summary of the minimal ASL processing pipeline used to provide pre-processed calibrated perfusion and arterial arrival time measurements for the cortical surface and subcortical volumes, from these individuals. The pipeline accounts for slice-wise image intensity variations resulting from the simultaneous multi-slice acquisition used to achieve high spatial resolution. These measures and this pipeline will be made available to the global neuroscience and neuroimaging communities. 

Lena Vaclavu¹, Kim van de Ven², and Matthias van Osch^1
1C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, ²Philips Healthcare, Best, Netherlands

In perfusion imaging with arterial spin labeling (ASL), the labeling plane is located further away from the isocenter of the scanner, than the imaging volume. This can result in offsets in resonance frequencies which can lead to imperfect inversion and hence lower labeling efficiency. In this study, a perfusion phantom is used to study the influence of off-resonance on two arterial spin labeling schemes, in order to determine whether an additional F0  determination at the labelling plane is the redeeming factor for successful ASL MRI independent of the labeling scheme used. 

Logan X Zhang¹ and Michael A Chappell^2,3,4
1Department of Engineering Science, Institute of Biomedical Engineering, Nottingham, United Kingdom, ²Nuffield Department of Clinical Neurosciences, University of Oxford, Wellcome Centre for Integrative Neuroimaging, FMRIB, Oxford, United Kingdom, ³School of Medicine, University of Nottingham, Radiological Sciences, Division of Clinical Neurosciences, Nottingham, United Kingdom, ⁴School of Medicine, University of Nottingham, Sir Peter Mansfield Imaging Centre and Mental Health & Clinical Neurosciences, Nottingham, United Kingdom

Blood-brain barrier permeability to water has been measured as the exchange time (Texch) between intra- and extra-vascular spaces using multi-TE arterial spin labelling (ASL). This relies on small signal changes associated with labelled blood water delivery, making it challenging to achieve high accuracy in short yet clinically-desirable scan durations. In this study, we used an optimal sampling framework to generate pseudo-continuous ASL protocols that were jointly optimised for delays and echo times. Simulation showed that while optimised protocols had overall improvements in parameter estimation compared to conventionally even-sampled protocols, accurately estimating Texch could not be achieved without sacrificing CBF accuracy. 

Yuriko Suzuki¹, Patricia Clement², Weiying Dai³, Sudipto Dolui⁴, Maria Fernández-Seara⁵, Thomas Lindner⁶, Henk JMM Mutsaerts⁷, Jan Petr⁸, Xingfeng Shao⁹, Manuel Taso¹⁰, and David L Thomas^11
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ²Ghent Institute for Functional and Metabolic Imaging, Ghent University, Ghent, Belgium, ³State University of New York at Binghamton, Binghamton, NY, United States, ⁴University of Pennsylvania, Philadelphia, PA, United States, ⁵Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain, ⁶University Hospital Hamburg-Eppendorf, Hamburg, Germany, ⁷Department of Radiology and nuclear medicine, Amsterdam neuroscience, Amsterdam University Medical Center, Amsterdam, Netherlands, ⁸Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, ⁹University of Southern California, Los Angeles, CA, United States, ¹⁰Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States, ¹¹Dept of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

As part of the Open Science Initiative for Perfusion Imaging (OSIPI), the aim of this work is to develop a Lexicon and Reporting Recommendations for arterial spin labeling (ASL) perfusion MR imaging and analysis. The lexicon describes standardized nomenclature and terminology for ASL acquisition techniques, parameters and physiological constants that are required for quantitative analysis. Additionally, a community-endorsed recommendation for reporting acquisition parameters in publications is provided. Overall, this project aims to improve the clarity and consistency of ASL terminology, which in turn will facilitate comparisons between different studies.

Jack Toner^1,2, Flora Kennedy McConnell^1,2,3, Yuriko Suzuki⁴, Timothy S. Coalson⁵, Michael P. Harms⁶, Matthew F. Glasser^5,7, and Michael A. Chappell^1,2,3,4
1Radiological Sciences, Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ²Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ³Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom, ⁴Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ⁵Department of Neuroscience, Washington University School of Medicine, St Louis, MO, United States, ⁶Department of Psychiatry, Washington University School of Medicine, St Louis, MO, United States, ⁷Department of Radiology, Washington University School of Medicine, St Louis, MO, United States

Simultaneous multi-slice (SMS) acquisitions enable higher resolutions to be achieved for arterial spin labelling (ASL) images. However, SMS acquisitions can introduce a banded pattern of intensity within the images. This reduces the quality of motion estimation as the algorithm aligns the bands in preference to the brain structures. We introduce a Gaussian Process model that can be used to correct the banding in SMS multiple post-labelling delay ASL data, which should improve motion correction. We demonstrate its effectiveness on 10 subjects from the Human Connectome Project Aging dataset. We anticipate that the model will generalise to other ASL datasets.

Udunna Anazodo^1,2, Joana Pinto³, Flora Kennedy McConnell^4,5,6, Cassandra Gould van Praag^7,8, Henk Mutsaerts⁹, Aaron Oliver Taylor¹⁰, Jan Petr¹¹, Diego Pineda-Ordóñez¹², Maria-Eleni Dounavi¹³, Irène Brumer¹⁴, Wei Siang Marcus Chan¹⁴, Jack Toner¹⁵, Jian Hu¹⁵, Logan X. Zhang³, Laura Bell¹⁶, Joseph G. Woods¹⁷, Moss Y Zhao¹⁸, Paula Croal^4,5, and Andre Monteiro Paschoal^19
1Lawson Health Research Institute,, London, ON, Canada, ²Western University, London, ON, Canada, ³Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, ⁴Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁵Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁶Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, United Kingdom, ⁷Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, ⁸Department of Psychiatry, University of Oxford, Oxford, United Kingdom, ⁹Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center,, Amsterdam, Netherlands, ¹⁰Gold Standard Phantoms Limited, London, United Kingdom, ¹¹Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical cancer research, Dresden, Germany, ¹²Department of Radiology, Clinica Del Country, Bogotá, Colombia, ¹³Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, ¹⁴Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany, ¹⁵Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ¹⁶Genentech, Inc., South San Francisco, CA, United States, ¹⁷Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, CA, United States, ¹⁸Department of Radiology, Stanford University, Stanford, CA, United States, ¹⁹Institute of Radiology and Department of Radiology and Oncology, University of Sao Paulo, Sao Paulo, Brazil

The OSIPI ASL MRI Challenge is an initiative of the ASL community aiming to characterize the variability of CBF quantification arising from different pipelines. The goal of this challenge is to establish best practice in ASL data processing, understand the sources of variability, make ASL analysis more reproducible, and enable fair comparison between studies. Here, we analyzed 3 submitted entries from 7 teams registered in the challenge. The preliminary results showed pipelines based in different programming languages and analysis tools, leading to important variability in the quantitative CBF maps compared to the ground-truth. 
 

Aaron Oliver-Taylor¹ and Xavier Golay^1,2
1Gold Standard Phantoms, London, United Kingdom, ²Queen Square Institute of Neurology, University College London, London, United Kingdom

Most perfusion phantoms that have been created make use of porous media to simulate the microvasculature, however their characterisation introduces additional uncertainties into a perfusion measurement. We take inspiration from a recently published impinging jet perfusion phantom and presenting a phantom that makes use of vortices to mix and disperse the perfusion signal. Multi post labelling delay pCASL data was acquired, and fit to the general kinetic model. By assuming the measured perfusion equates to the equivalent perfusion from the systemic flow rate the labelling efficiency can be estimated, and the resultant measured and fitted signal curves closely match.

Didi Chi^1,2, Yasmin Blunck^1,2, Rebecca Glarin², Catherine E. Davey^1,2, Josef Pfeuffer³, Daniel Staeb³, Jin Jin³, and Leigh A. Johnston^1,2
1Department of Biomedical Engineering, University of Melbourne, Parkville, Australia, ²Melbourne Brain Centre Imaging Unit, University of Melbourne, Parkville, Australia, ³MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia

PICORE-ASL using tr-FOCI pulses during labelling typically generates high power deposition at 7T. The current study introduces an asymmetric tr-FOCI pulse for PICORE-ASL to achieve SAR reduction. Results from simulations, phantom and in vivo experiments demonstrate the SAR reduction that can be achieved by using the asymmetric pulses without sacrificing inversion efficiency, slice profiles or CBF measurement.

Masa Bozic-Iven^1,2, Stanislas Rapacchi³, Iain Pierce⁴, George Thornton⁴, Qian Tao², Lothar Schad¹, Thomas Treibel⁴, and Sebastian Weingaertner^2
1Computer Assisted Clinical Medicine, University Heidelberg, Mannheim, Germany, ²Delft University of Technology, Delft, Netherlands, ³University Aix-Marseille, Marseille, France, ⁴Barts Heart Centre, London, United Kingdom

Despite promising results, clinical translation of myocardial arterial spin labelling (myoASL) is hampered by insufficient reproducibility and robustness. We investigated the influence of physiological and sequence parameters on FAIR-myoASL in simulations as well as phantom experiments, and developed a correction method based on separately acquired T1 maps. Our simulation and phantom results show acquisition related MBF differences, potentially undermining the reproducibility of myoASL measurements. Inaccuracies between true and reconstruction blood T1, further render the sequence susceptible to heart rate variations, particularly for larger T1 mismatch. Using accurate blood T1 times in reconstruction may improve robustness and reproducibility.

María Guadalupe Mora Álvarez¹, Li Zhao², Sudipto Dolui³, Manuel Taso⁴, Yiming Wang⁵, Limin Zhou⁵, Ze Wang⁶, Azeez Adebimpe⁷, Henk Mutsaerts⁸, and Ananth J. Madhuranthakam^5
1Department of Diagnostic and Interventional Neuroradiology, Technical University Munich (TUM), Munich, Germany, ²College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ³Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ⁴Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States, ⁵Department of Radiology, UT Southwestern Medical Center, Dallas, TX, United States, ⁶Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, ⁷Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States, ⁸Amsterdam University Medical Center, Amsterdam, Netherlands

Task force 2.2 of the Open Science Initiative for Perfusion Imaging (OSIPI) is developing a library of open-source functions, and scripts for Arterial Spin Labeled (ASL) perfusion imaging preprocessing and analysis. This is aimed for developers of ASL perfusion methods looking for specific functionalities or development templates, or who want to share their own in-house code with others. The collected source code will be organized and documented so as to support the open library development.