Petter Dyverfeldt1, Tino Ebbers2, Toste Länne3. 1. Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. Electronic address: petter.dyverfeldt@liu.se. 2. Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Media and Information Technology, Department of Science and Technology/Swedish e-Science Research Centre (SeRC), Linköping University, Linköping, Sweden. 3. Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Department of Cardiovascular Surgery, Linköping University Hospital, County Council of Östergötland, Linköping, Sweden.
Abstract
PURPOSE: The objective of this study was to compare multiple methods for estimation of PWV from 4D flow MRI velocity data and to investigate if 4D flow MRI-based PWV estimation with piecewise linear regression modeling of travel-distance vs. travel time is sufficient to discern age-related regional differences in PWV. METHODS: 4D flow MRI velocity data were acquired in 8 young and 8 older (age: 23±2 vs. 58±2 years old) normal volunteers. Travel-time and travel-distance were measured throughout the aorta and piecewise linear regression was used to measure global PWV in the descending aorta and regional PWV in three equally sized segments between the top of the aortic arch and the renal arteries. Six different methods for extracting travel-time were compared. RESULTS: Methods for estimation of travel-time that use information about the whole flow waveform systematically overestimate PWV when compared to methods restricted to the upslope-portion of the waveforms (p<0.05). In terms of regional PWV, a significant interaction was found between age and location (p<0.05). The age-related differences in regional PWV were greater in the proximal compared to distal descending aorta. CONCLUSION: Care must be taken as different classes of methods for the estimation of travel-time produce different results. 4D flow MRI-based PWV estimation with piecewise linear regression modeling of travel-distance vs. travel time can discern age-related differences in regional PWV well in line with previously reported data.
PURPOSE: The objective of this study was to compare multiple methods for estimation of PWV from 4D flow MRI velocity data and to investigate if 4D flow MRI-based PWV estimation with piecewise linear regression modeling of travel-distance vs. travel time is sufficient to discern age-related regional differences in PWV. METHODS: 4D flow MRI velocity data were acquired in 8 young and 8 older (age: 23±2 vs. 58±2 years old) normal volunteers. Travel-time and travel-distance were measured throughout the aorta and piecewise linear regression was used to measure global PWV in the descending aorta and regional PWV in three equally sized segments between the top of the aortic arch and the renal arteries. Six different methods for extracting travel-time were compared. RESULTS: Methods for estimation of travel-time that use information about the whole flow waveform systematically overestimate PWV when compared to methods restricted to the upslope-portion of the waveforms (p<0.05). In terms of regional PWV, a significant interaction was found between age and location (p<0.05). The age-related differences in regional PWV were greater in the proximal compared to distal descending aorta. CONCLUSION: Care must be taken as different classes of methods for the estimation of travel-time produce different results. 4D flow MRI-based PWV estimation with piecewise linear regression modeling of travel-distance vs. travel time can discern age-related differences in regional PWV well in line with previously reported data.
Authors: Kelly Jarvis; Michael B Scott; Gilles Soulat; Mohammed S M Elbaz; Alex J Barker; James C Carr; Michael Markl; Ann Ragin Journal: J Magn Reson Imaging Date: 2022-01-10 Impact factor: 5.119
Authors: Thekla H Oechtering; Grant S Roberts; Nikolaos Panagiotopoulos; Oliver Wieben; Alejandro Roldán-Alzate; Scott B Reeder Journal: Abdom Radiol (NY) Date: 2021-11-27
Authors: M Markl; S Schnell; C Wu; E Bollache; K Jarvis; A J Barker; J D Robinson; C K Rigsby Journal: Clin Radiol Date: 2016-03-02 Impact factor: 2.350
Authors: Petter Dyverfeldt; Malenka Bissell; Alex J Barker; Ann F Bolger; Carl-Johan Carlhäll; Tino Ebbers; Christopher J Francios; Alex Frydrychowicz; Julia Geiger; Daniel Giese; Michael D Hope; Philip J Kilner; Sebastian Kozerke; Saul Myerson; Stefan Neubauer; Oliver Wieben; Michael Markl Journal: J Cardiovasc Magn Reson Date: 2015-08-10 Impact factor: 5.364