Fabio Nery1, Charlotte E Buchanan2, Anita A Harteveld3, Aghogho Odudu4, Octavia Bane5, Eleanor F Cox2, Katja Derlin6, H Michael Gach7, Xavier Golay8, Marcel Gutberlet6, Christoffer Laustsen9, Alexandra Ljimani10, Ananth J Madhuranthakam11, Ivan Pedrosa11, Pottumarthi V Prasad12, Philip M Robson5, Kanishka Sharma13, Steven Sourbron13, Manuel Taso14, David L Thomas8, Danny J J Wang15, Jeff L Zhang16, David C Alsop14, Sean B Fain17, Susan T Francis2, María A Fernández-Seara18. 1. Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, UK. 2. Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK. 3. Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. 4. Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. 5. Translational and Molecular Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 6. Department of Radiology, Hannover Medical School, Hannover, Germany. 7. Departments of Radiation Oncology, Radiology, and Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA. 8. Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK. 9. MR Research Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. 10. Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 11. Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA. 12. Department of Radiology, Center for Advanced Imaging, NorthShore University Health System, Evanston, IL, USA. 13. Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK. 14. Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA. 15. Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA. 16. A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA. 17. Departments of Medical Physics, Radiology, and Biomedical Engineering, University of Wisconsin, Madison, Madison, USA. 18. Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain. mfseara@unav.es.
Abstract
OBJECTIVES: This study aimed at developing technical recommendations for the acquisition, processing and analysis of renal ASL data in the human kidney at 1.5 T and 3 T field strengths that can promote standardization of renal perfusion measurements and facilitate the comparability of results across scanners and in multi-centre clinical studies. METHODS: An international panel of 23 renal ASL experts followed a modified Delphi process, including on-line surveys and two in-person meetings, to formulate a series of consensus statements regarding patient preparation, hardware, acquisition protocol, analysis steps and data reporting. RESULTS: Fifty-nine statements achieved consensus, while agreement could not be reached on two statements related to patient preparation. As a default protocol, the panel recommends pseudo-continuous (PCASL) or flow-sensitive alternating inversion recovery (FAIR) labelling with a single-slice spin-echo EPI readout with background suppression and a simple but robust quantification model. DISCUSSION: This approach is considered robust and reproducible and can provide renal perfusion images of adequate quality and SNR for most applications. If extended kidney coverage is desirable, a 2D multislice readout is recommended. These recommendations are based on current available evidence and expert opinion. Nonetheless they are expected to be updated as more data become available, since the renal ASL literature is rapidly expanding.
OBJECTIVES: This study aimed at developing technical recommendations for the acquisition, processing and analysis of renal ASL data in the human kidney at 1.5 T and 3 T field strengths that can promote standardization of renal perfusion measurements and facilitate the comparability of results across scanners and in multi-centre clinical studies. METHODS: An international panel of 23 renal ASL experts followed a modified Delphi process, including on-line surveys and two in-person meetings, to formulate a series of consensus statements regarding patient preparation, hardware, acquisition protocol, analysis steps and data reporting. RESULTS: Fifty-nine statements achieved consensus, while agreement could not be reached on two statements related to patient preparation. As a default protocol, the panel recommends pseudo-continuous (PCASL) or flow-sensitive alternating inversion recovery (FAIR) labelling with a single-slice spin-echo EPI readout with background suppression and a simple but robust quantification model. DISCUSSION: This approach is considered robust and reproducible and can provide renal perfusion images of adequate quality and SNR for most applications. If extended kidney coverage is desirable, a 2D multislice readout is recommended. These recommendations are based on current available evidence and expert opinion. Nonetheless they are expected to be updated as more data become available, since the renal ASL literature is rapidly expanding.
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