Pascal Spincemaille1, Zhe Liu1,2, Shun Zhang1,3, Ilhami Kovanlikaya1, Matteo Ippoliti4, Marcus Makowski4, Richard Watts5, Ludovic de Rochefort6, Vijay Venkatraman7, Patricia Desmond7, Mathieu D Santin8, Stéphane Lehéricy8,9, Brian H Kopell10,11,12,13, Patrice Péran14, Yi Wang1,2. 1. Department of Radiology, Weill Medical College of Cornell University, New York, NY. 2. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY. 3. Department of Radiology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 4. Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany. 5. Department of Psychology, Yale University, New Haven, CT. 6. Aix Marseille Univ, CNRS, CRMBM - UMR 7339, Marseille, France. 7. Department of Medicine and Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia. 8. Inserm U 1127, CNRS UMR 7225, Centre for NeuroImaging Research, ICM (Brain & Spine Institute), Sorbonne University, Paris, France. 9. Neuroradiology, Hôpital Pitié-Salpêtrière, Paris, France. 10. Division of Movement Disorders, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY. 11. Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY. 12. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY. 13. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY. 14. Toulouse NeuroImaging Center, Université de Toulouse Inserm, Toulouse, France.
Abstract
BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment. METHODS: A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm3 reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION: Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.
BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment. METHODS: A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm3 reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION: Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.
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