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15 March 2019 Fourier decomposition free-breathing 1H MRI perfusion maps in asthma
Alexander M. Matheson, Dante P. I. Capaldi, Fumin Guo, Rachel L. Eddy, David G. McCormack, Grace Parraga
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Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094912 (2019) https://doi.org/10.1117/12.2512436
Event: SPIE Medical Imaging, 2019, San Diego, California, United States
Abstract
Objective: We aimed to develop a user-friendly image-analysis pipeline to simultaneously generate perfusion and ventilation maps derived from Fourier decomposition of free-breathing pulmonary 1H magnetic resonance imaging (FDMRI). Methods: Free-breathing 1H MR images were non-rigidly deformed to a 1H reference image selected halfway between inspiration and expiration, using modality independent neighbourhood descriptor-based registration. The 1H reference image was segmented using multi-region coupled continuous max-flow. The co-registered image sequence was Fourier transformed on a voxel-by-voxel basis to generate images of the voxel-wise power spectrum. The two largest intensity peaks in the power spectrum corresponded to respiratory and cardiac frequencies, which were used to generate ventilation and perfusion maps, respectively. Perfusion and ventilation defects were measured using fuzzy c-means clustering in 15 asthmatics who provided written-informed-consent to pulmonary function tests and MRI. Results: The proposed FDMRI pipeline was used to generate perfusion maps in 15 asthma patients for direct comparison with 3He and FDMRI ventilation maps. FDMRI perfusion measurements were significantly correlated with FDMRI (r2=0.48, p=0.03) and 3He MRI ventilation (r2=0.44, p=0.05). Conclusion: Ventilation and perfusion free-breathing 1H MRI maps were generated in asthmatics with clinicallyacceptable accuracy and minimal user interaction using a pipeline compatible with high throughput clinical workflows.
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Alexander M. Matheson, Dante P. I. Capaldi, Fumin Guo, Rachel L. Eddy, David G. McCormack, and Grace Parraga "Fourier decomposition free-breathing 1H MRI perfusion maps in asthma", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094912 (15 March 2019); https://doi.org/10.1117/12.2512436
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KEYWORDS
Magnetic resonance imaging
Lung
Image segmentation
Tissues
Algorithm development
Helium
Visualization
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