Jan Petr1,2, Henri J M M Mutsaerts3,4,5,6, Enrico De Vita7,8, Rebecca M E Steketee9, Marion Smits9, Aart J Nederveen5, Frank Hofheinz10, Jörg van den Hoff10,11, Iris Asllani3. 1. Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany. j.petr@hzdr.de. 2. Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA. j.petr@hzdr.de. 3. Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA. 4. Sunnybrook Research Institute, Toronto, Canada. 5. Department of Radiology, Academic Medical Center Amsterdam, Amsterdam, The Netherlands. 6. Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands. 7. Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK. 8. Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, Kings Health Partners, St Thomas Hospital, London, UK. 9. Department of Radiology and Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands. 10. Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany. 11. Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Abstract
OBJECTIVE: Partial volume (PV) correction is an important step in arterial spin labeling (ASL) MRI that is used to separate perfusion from structural effects when computing the mean gray matter (GM) perfusion. There are three main methods for performing this correction: (1) GM-threshold, which includes only voxels with GM volume above a preset threshold; (2) GM-weighted, which uses voxel-wise GM contribution combined with thresholding; and (3) PVC, which applies a spatial linear regression algorithm to estimate the flow contribution of each tissue at a given voxel. In all cases, GM volume is obtained using PV maps extracted from the segmentation of the T1-weighted (T1w) image. As such, PV maps contain errors due to the difference in readout type and spatial resolution between ASL and T1w images. Here, we estimated these errors and evaluated their effect on the performance of each PV correction method in computing GM cerebral blood flow (CBF). MATERIALS AND METHODS: Twenty-two volunteers underwent scanning using 2D echo planar imaging (EPI) and 3D spiral ASL. For each PV correction method, GM CBF was computed using PV maps simulated to contain estimated errors due to spatial resolution mismatch and geometric distortions which are caused by the mismatch in readout between ASL and T1w images. Results were analyzed to assess the effect of each error on the estimation of GM CBF from ASL data. RESULTS: Geometric distortion had the largest effect on the 2D EPI data, whereas the 3D spiral was most affected by the resolution mismatch. The PVC method outperformed the GM-threshold even in the presence of combined errors from resolution mismatch and geometric distortions. The quantitative advantage of PVC was 16% without and 10% with the combined errors for both 2D and 3D ASL. Consistent with theoretical expectations, for error-free PV maps, the PVC method extracted the true GM CBF. In contrast, GM-weighted overestimated GM CBF by 5%, while GM-threshold underestimated it by 16%. The presence of PV map errors decreased the calculated GM CBF for all methods. CONCLUSION: The quality of PV maps presents no argument for the preferential use of the GM-threshold method over PVC in the clinical application of ASL.
OBJECTIVE: Partial volume (PV) correction is an important step in arterial spin labeling (ASL) MRI that is used to separate perfusion from structural effects when computing the mean gray matter (GM) perfusion. There are three main methods for performing this correction: (1) GM-threshold, which includes only voxels with GM volume above a preset threshold; (2) GM-weighted, which uses voxel-wise GM contribution combined with thresholding; and (3) PVC, which applies a spatial linear regression algorithm to estimate the flow contribution of each tissue at a given voxel. In all cases, GM volume is obtained using PV maps extracted from the segmentation of the T1-weighted (T1w) image. As such, PV maps contain errors due to the difference in readout type and spatial resolution between ASL and T1w images. Here, we estimated these errors and evaluated their effect on the performance of each PV correction method in computing GM cerebral blood flow (CBF). MATERIALS AND METHODS: Twenty-two volunteers underwent scanning using 2D echo planar imaging (EPI) and 3D spiral ASL. For each PV correction method, GM CBF was computed using PV maps simulated to contain estimated errors due to spatial resolution mismatch and geometric distortions which are caused by the mismatch in readout between ASL and T1w images. Results were analyzed to assess the effect of each error on the estimation of GM CBF from ASL data. RESULTS: Geometric distortion had the largest effect on the 2D EPI data, whereas the 3D spiral was most affected by the resolution mismatch. The PVC method outperformed the GM-threshold even in the presence of combined errors from resolution mismatch and geometric distortions. The quantitative advantage of PVC was 16% without and 10% with the combined errors for both 2D and 3D ASL. Consistent with theoretical expectations, for error-free PV maps, the PVC method extracted the true GM CBF. In contrast, GM-weighted overestimated GM CBF by 5%, while GM-threshold underestimated it by 16%. The presence of PV map errors decreased the calculated GM CBF for all methods. CONCLUSION: The quality of PV maps presents no argument for the preferential use of the GM-threshold method over PVC in the clinical application of ASL.
Authors: John A D Aston; Vincent J Cunningham; Marie-Claude Asselin; Alexander Hammers; Alan C Evans; Roger N Gunn Journal: J Cereb Blood Flow Metab Date: 2002-08 Impact factor: 6.200
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Authors: Henri J M M Mutsaerts; Jan Petr; David L Thomas; Enrico De Vita; David M Cash; Matthias J P van Osch; Xavier Golay; Paul F C Groot; Sebastien Ourselin; John van Swieten; Robert Laforce; Fabrizio Tagliavini; Barbara Borroni; Daniela Galimberti; James B Rowe; Caroline Graff; Francesca B Pizzini; Elizabeth Finger; Sandro Sorbi; Miguel Castelo Branco; Jonathan D Rohrer; Mario Masellis; Bradley J MacIntosh Journal: J Magn Reson Imaging Date: 2017-05-08 Impact factor: 4.813
Authors: Rebecca M E Steketee; Esther E Bron; Rozanna Meijboom; Gavin C Houston; Stefan Klein; Henri J M M Mutsaerts; Carolina P Mendez Orellana; Frank Jan de Jong; John C van Swieten; Aad van der Lugt; Marion Smits Journal: Eur Radiol Date: 2015-05-31 Impact factor: 5.315
Authors: Henri J M M Mutsaerts; Rebecca M E Steketee; Dennis F R Heijtel; Joost P A Kuijer; Matthias J P van Osch; Charles B L M Majoie; Marion Smits; Aart J Nederveen Journal: PLoS One Date: 2014-08-04 Impact factor: 3.240
Authors: K P A Baas; J Petr; J P A Kuijer; A J Nederveen; H J M M Mutsaerts; K C C van de Ven Journal: AJNR Am J Neuroradiol Date: 2020-11-12 Impact factor: 3.825
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