Sudipto Dolui¹, Hongli Fan², Paula L. Croal^3,4, Charlotte Buchanan⁴, Lydiane Hirschler⁵, Udunna Anazodo⁶, David L. Thomas⁷, Henk J.M.M. Mutsaerts⁸, and Jan Petr^9
1Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²The Johns Hopkins School of Medicine, Baltimore, BC, United States, ³Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁴Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁵Leiden University Medical Center, Leiden, Netherlands, ⁶Western University, London, ON, Canada, ⁷UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁸Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location VU, Amsterdam, Netherlands, ⁹Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany

As a part of the Open Source Initiative for Perfusion Imaging (OSIPI), we have created an inventory of software for automated processing of Arterial Spin Labeling (ASL) perfusion MRI data. We contacted the ASL community through different channels, inviting software developers to list their pipelines by completing a questionnaire covering different aspects and features of a desired pipeline. We received inputs from 18 developers and have summarized the main characteristics of their pipelines based on the information they provided. We expect that this inventory will facilitate ASL-related research, reduce duplicate development, and enable translation of ASL to clinical practice.

Udunna Anazodo^1,2, Joana Pinto³, Flora A. Kennedy McConnell^4,5,6, Maria-Eleni Dounavi⁷, Cassandra Gould van Praag⁸, Henk Mutsaerts⁹, Aaron Oliver Taylor¹⁰, André Paschoal¹¹, Jan Petr¹², Diego Pineda-Ordóñez¹³, Joseph G. Woods¹⁴, Moss Y. Zhao¹⁵, and Paula L. Croal^4,5
1Department of Medical Biophysics, University of Western Ontario, London, ON, Canada, ²Imaging Division, Lawson Health Research Institute, London, ON, Canada, ³Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, ⁴Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, ⁵Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ⁶Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, United Kingdom, ⁷Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, ⁸Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, ⁹Amsterdam University Medical Center, Amsterdam, Netherlands, ¹⁰Gold Standard Phantoms Limited, London, United Kingdom, ¹¹Department of Radiology, LIM44 - HCFMUSP, Sao Paulo, Brazil, ¹²Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, ¹³Department of Radiology, Clinica Del Country, Bogotá, Colombia, ¹⁴Department of Radiology, University of California San Diego, San Diego, CA, United States, ¹⁵Department of Radiology, Stanford University, Stanford, CA, United States

The OSIPI ASL MRI Challenge is a community-led initiative aiming to establish the range of approaches used for ASL image analysis and cerebral blood flow (CBF) quantification. Challenge data will consist of population-based and synthetic pseudo-continuous ASL images, with participants analysing the data and submitting resulting CBF maps and mean tissue CBF, along with documentation. Entries will be scored on accuracy, reproducibility and documentation quality. Through documenting the analysis choices made within the community, we will begin to better understand sources of variability, ultimately identifying an optimum pipeline, and moving towards the much-needed consensus of ASL image processing standards.

Zhiyuan Zhang^1,2, Timothy Macaulay³, and Lirong Yan^1
1USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ²Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ³Division of Biokinesiology and Physical Therapy, Ostrow School of Dentistry, University of Southern California, Los Angeles, CA, United States

The design of post-labeling delays (PLDs) directly affects the accuracy of CBF and ATT quantifications using multi-delay ASL. In this study, we optimized PLDs in 3D pCASL based upon different ATT distributions including normal distribution directly derived from in vivo ASL data and uniform distribution. Evenly spaced PLDs were also applied for comparison. Our results showed that optimal PLDs based on ATT normal distribution had the best performance in CBF and ATT quantifications with the smallest errors.  

Lena Vaclavu¹, Dilek Betül Arslan², Lydiane Hirschler¹, Carles Falcon^3,4, Esin Ozturk-Isik², Juan Domingo Gispert^3,4, Paula Montesinos⁵, Kim van de Ven⁶, and Matthias JP van Osch^1
1Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands, ²Biomedical Engineering Institute, Boğaziçi University, Istanbul, Turkey, ³BarcelonaBeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain, ⁴CIBER-BBN, Madrid, Spain, ⁵Philips Healthcare Iberia, Madrid, Spain, ⁶Philips Healthcare, Best, Netherlands

Physiologic fluctuations can lead to the appearance of artefactual angiographic signal in the perfusion image of time-encoded pCASL previously dubbed “shine-through effect”. The aim of this study was to investigate the potential mechanisms inducing this artefact by characterizing the noise in time-encoded pCASL signal from individual Hadamard columns and rows as well as without labeling. We observed higher temporal standard deviation in the arteries compared to gray matter, and found that the shine-through effect was present even without labeling, suggesting that it is caused by the selective background suppression pulse applied close to acquisition time and the associated fresh inflow.  

Shota Ishida¹, Hirohiko Kimura², Naoyuki Takei³, Yasuhiro Fujiwara⁴, Tsuyoshi Matsuda⁵, Yuki Matta¹, Masayuki Kanamoto¹, Nobuyuki Kosaka², and Eiji Kidoya^1
1Radiological center, University of Fukui Hospital, Eiheiji, Japan, ²Department of Radiology, Faculty of Medical Sciences, University of Fukui, Eiheiji, Japan, ³Global MR Applications and Workflow, GE Healthcare Japan, Hino, Japan, ⁴Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University, Chuo-ku, Japan, ⁵Division of Ultra-high Field MRI, Institute for Biomedical Science, Iwate Medical University, Shiwa-gun, Japan

To verify the characteristics of DANTE for vascular suppression, T₂ values of the ASL signal under the application of DANTE were determined. While T₂ values without DANTE decreased as the PLD increased, T₂ values with DANTE (T_{2_DANTE}) did not change among the PLDs. Moreover, T_{2_DANTE} were equivalent to those of the reference. The positive correlation between T₂ values and the ATT without DANTE was not observed when DANTE was used. T_{2_DANTE} were neither dependent on the ATT nor PLD. Therefore, DANTE separates the spin compartments in ASL by selective elimination of intra-vascular signals from total ASL signals.

Logan Xin Zhang¹ and Michael A Chappell^1,2,3
1Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom, ²Radiological Sciences, Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ³Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

Optimised pCASL protocols may still be sensitive to macrovascular contamination (MVC), leading to high CBF estimation error. In this study, we conducted a systematic investigation of CBF sensitivity to MVC from a comprehensive set of optimal pCASL protocols. The general kinetic model was only robust for CBF estimation when tissue ATT was less than 1.5s. An extended kinetic model could reduce CBF sensitivity to up to 2.3s. The results of CBF sensitivity maps could be used for examining a protocol for possible MVC effects before scanning, or data interpretation afterwards. 

Anish Lahiri¹, Jeffrey Fessler¹, and Luis Hernandez-Garcia^2
1Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, United States, ²FMRI Laboratory and Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

This work proposes a quantitative perfusion imaging using Velocity Selective Inversion pulses combined with an MR Fingerprinting ASL framework that allows for the alleviation of several nuisance parameters in the model, and provides hemodynamic estimates over an extensive region of the brain in a single scan. Preliminary in-vivo experiments indicate that the obtained hemodynamic estimates in gray and white matter agree with values typically found in literature.

Joseph G. Woods¹, Eric C. Wong¹, David D. Shin², and Divya Bolar^1
1Department of Radiology, University of California San Diego, San Diego, CA, United States, ²GE Healthcare, Menlo Park, CA, United States

A novel framework that minimizes eddy current mismatch between tag and control acquisitions in velocity-selective (VS) arterial spin labeling is introduced. The framework is based on minimizing the 0th-order gradient moment in the presence of eddy currents and is applicable to any VS module design, without suffering a time penalty in module duration. Simulation and empirical data are presented and demonstrate reductions in eddy current artifacts in both phantom and in vivo data.

Verónica Aramendía-Vidaurreta^1,2, Pedro M. Gordaliza^3,4, Marta Vidorreta⁵, Rebeca Echeverría-Chasco^1,2, Gorka Bastarrika^1,2, Arrate Muñoz-Barrutia^3,4, and María A. Fernández-Seara^1,2
1Radiology, Clínica Universidad de Navarra, Pamplona, Spain, ²IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain, ³Universidad Carlos III de Madrid, Madrid, Spain, ⁴Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain, ⁵Siemens Healthineers, Madrid, Spain

This work investigated the impact of three different breathing strategies, named breathhold (BH), synchronized-breathing (SB) and free-breathing (FB), together with motion detection and correction algorithms in myocardial arterial spin labeling (ASL) images. Results indicate the superiority of FB combined with pairwise registration, which showed higher accuracy (in synthetic images) and higher intrasession reproducibility together with lower variability across subjects (in in vivo images). BH and SB after motion detection provided similar results, but their practical application is more complicated as it demands the subject's collaboration to follow the respiratory pattern in SB or perform the apneas in BH.

Isabell Katrin Bones¹, Clemens Bos¹, Chrit Moonen¹, Jeroen Hendrikse², and Marijn van Stralen^1
1Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands

ASL-MRI quantification involves kidney segmentation and cortex-medulla differentiation to obtain cortical renal blood flow, requiring time consuming manual interaction hampering clinical adoption. We applied machine learning to automat renal ASL-MRI quantification. A cascade of three U-nets was constructed to replace manual segmentation steps. Automatic segmentation yielded a dice score of 0.78, which was similar to the inter-observer variability of 0.77. Moreover, good agreement for cortical RBF was found between automatic and manual segmentations on group and individual level; 211±31 and 208±31mL/min/100g, respectively. Our proposed method automates quantification without compromising performance. This makes renal ASL-MRI more attractive for clinical application. 

Qinyang Shou¹, Chaitanya Gupte², Danny JJ Wang¹, and Hosung Kim^2
1Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States, ²Neuroimaging with Deep Learning Lab (NIDLL), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States

The purpose of this study was to develop a deep learning algorithm for 3D spatial-temporal denoising of ASL perfusion image series. 162 datasets from the Pediatric Template of Brain Perfusion database were used for model training and testing. The results showed that the proposed method can achieve higher Peak Signal-to-Noise Ratio (PSNR) and higher Structural Similarity Index (SSIM) than averaging of the time series and using traditional Principal Component Analysis (PCA) denoising. This result was robust when reducing the input measurements to one quarter of the total measurements, which shows the potential to reduce the scan time for ASL imaging.

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

Pseudo-continuous arterial spin labeling (pcASL) at 7T suffers from insufficient B₁⁺-amplitudes and specific absorption rate (SAR) constraints. Even with B₁⁺ phase-only or phase/amplitude shimming for the labeling the resulting inversion is suboptimal. In this work, we performed Bloch simulations to examine the impact of thicker pcASL labeling slices by adjusting the slice-selective gradient of a B₁⁺-shimmed labeling train exploiting Variable-Rate Selective Excitation. The findings were evaluated experimentally in 5 healthy volunteers. Using lower slice-selective gradients and subsequently longer exposure of the spins to the pcASL labeling train, the perfusion images improved according to gray matter temporal signal-to-noise ratio by 35%.

Seon-Ha Hwang¹, SoHyun Han², Seong-Gi Kim², Jaeseok Park^3,4, and Sung-Hong Park^1
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, ²Center for Neuroscience Imaging Research, Institute of Basic Science, Suwon, Korea, Republic of, ³Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of, ⁴Department of Intelligent Precision Healthcare Convergence, Suwon, Korea, Republic of

Due to high specific absorption rate (SAR), it has not been easy to apply pseudo-continuous arterial spin labeling (pCASL) at 7T human MRI, especially with 3D readouts. In this study, non-segmented 3D-EPI-pCASL is proposed for whole-brain perfusion mapping. SAR was reduced by multiple low-flip-angle water-excitation rectangular RF pulses for 3D EPI. The proposed 3D-EPI-pCASL produced consistent perfusion maps at both 3T and 7T compared to 2D-EPI-pCASL which was available only at 3T because of SAR. The high temporal resolution of the proposed non-segmented 3D-EPI-pCASL enabled us to get a whole-brain 3D pCASL fMRI map at 7T for the first time.

Gael Saib¹, Alan Koretsky¹, and S Lalith Talagala^2
1NINDS/LFMI, National Institutes of Health, Bethesda, MD, United States, ²NINDS/NMRF, National Institutes of Health, Bethesda, MD, United States

At 7T, it is known that B₁⁺ and B₀ inhomogeneities reduce the PCASL labeling efficiency. Further, the short T₂ of blood also reduces the maximum blood labeling efficiency that can be achieved in practice. In this work, we employed a PCASL-prepared FLASH sequence to measure vessel-specific labeling efficiency of PCASL with B₀ off-resonance correction. At 7T, experimental data indicates a maximal labeling efficiency of ~0.6. This study also shows that optimization of PCASL labeling efficiency is possible by tailoring the average gradient according to the blood velocity range and B₁ at the feeding arteries. 

Jack Highton¹, Enrico De Vita², and David Thomas^3,4,5
1UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ²School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom, ³Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁴Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, University College London, London, United Kingdom, ⁵Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom

A retrospective motion correction method is presented, for multi-shot 3D GRASE ASL. ESPIRiT is used to calculate coil sensitivity fields, using raw ASL k-space data. These are used to reconstruct complete images from the interleaved fractions of k-space sampled during each shot using SENSE. Therefore, typical image registration can be used to correct inter-shot motion. The method was tested using a simulation of 3D GRASE ASL and an equivalent experiment, where the subject was trained to nod or keep still. The motion correction reduced artefacts, and increased the correlation between cerebral blood flow measurements acquired with and without motion.

Qinyang Shou¹, Xingfeng Shao¹, and Danny JJ Wang^1
1Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States

The goal of this study is to optimize and evaluate super-resolution simultaneous multislice (SMS) Arterial Spin Labeling (ASL) using the Slice Dithered Enhanced Resolution (SLIDER). Different numbers (2/3/4) of shifted slices for SLIDER SMS ASL were evaluated for spatial and temporal SNR. The results showed that the SNR efficiency of SLIDER SMS ASL increased with a greater number of shifted slices which may be attributed to reduced g-factor for SMS imaging of thicker slices as well as the presence of physiological noise.

Zihan Ning¹, Shuo Chen¹, Zhensen Chen¹, Huiyu Qiao¹, Hualu Han¹, Rui Shen¹, Dandan Yang¹, and Xihai Zhao^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine Tsinghua University, Beijing, China

A SAturated Look-Locker Flow-sensitive Alternating Inversion Recovery (SALL-FAIR) sequence combined with the Four-Phase One-Compartment Kety (FPOCK) kinetic model was proposed for simultaneous acquisition of CBF, aBAT, tBAT and T1 map with a single scan. The T1 maps of SALL-FAIR were verified by standard IR-SE on phantom, and higher accuracy of perfusion quantification from SALL-FAIR with FPOCK model was proved by comparing with the Buxton’s and the single-TI model in vivo.

Manuel Taso¹, Fanny Munsch¹, and David C Alsop^1
1Division of MRI research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States

Neuroscientific and clinical research has seen tremendous advances over the past 25 years. While hardware, pulse-sequences and reconstruction developments have been an immense driver for this, image processing tools have also been crucial.While anatomical templates such as the MNI₁₅₂ have become widely used and are a hallmark of neuroimaging analyses, functional contrast-specific templates for group studies are much scarcer but of great interest. We therefore report here the initial development of BATS – the Boston ASL Template and Simulator, highlighting its development and initial use. 

Aaron Oliver-Taylor¹, Thomas Hampshire¹, Nadia A S Smith², Michael Stritt³, Jan Petr⁴, Johannes Gregori³, Matthias Günther^3,5, Henk J Mutsaerts⁶, and Xavier Golay^1,7
1Gold Standard Phantoms Limited, London, United Kingdom, ²National Physical Laboratory, Teddington, United Kingdom, ³mediri GmbH, Heidelberg, Germany, ⁴Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, ⁵Fraunhofer MEVIS, Bremen, Germany, ⁶Amsterdam University Medical Center, Amsterdam, Netherlands, ⁷Institute of Neurology, University College London, London, United Kingdom

ASLDRO is digital reference object software for Arterial Spin Labelling. Here we present the development and demonstration of the DRO software, and its use in a sensitivity and uncertainty analysis of the single-subtraction equation for ASL perfusion quantification.The DRO software was written in python, and can generate synthetic ASL control, label and M0 data in ASL BIDS format. Pulsed and continuous labelling are supported, and patient motion and instrument noise are accurately simulated. It can be used both for testing and validation of image processing software, and for investigating ASL quantification models.

Yiming Wang¹, Limin Zhou¹, Durga Udayakumar^1,2, and Ananth J. Madhuranthakam^1,2
1Radiology, UT Southwestern Medical Center, Dallas, TX, United States, ²Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States

Arterial spin labelling (ASL) is a non-invasive and non-contrast perfusion imaging technique that can serve as an imaging biomarker to assess tissue blood flow characteristics. A perfusion phantom is valuable to evaluate newly developed ASL sequences, test consistency and to compare sequence reproducibility across various scanners. In this study, we performed a longitudinal assessment of the reproducibility and repeatability of perfusion measurement using 2D pCASL sequence with 20 PLDs over 5 weeks duration with a previously developed 3D printed perfusion phantom. Intra-class correlation coefficient (ICC) of measured perfusion and T1 are 0.96 and 0.94 respectively, indicating good reproducibility and repeatability.