Unsupervised respiratory signal extraction from ungated cardiac magnetic resonance imaging at rest and during exercise.

Unsupervised respiratory signal extraction from ungated cardiac magnetic resonance imaging at rest and during exercise. Phys Med Biol. 2019 Jan 29;: Authors: Novillo F, Van Eyndhoven S, Moeyersons J, Bogaert J, Claessen G, La Gerche A, Van Huffel S, Claus P Abstract We propose and evaluate a method to estimate a respiratory signal from ungated cardiac magnetic resonance (CMR) images. 
 Ungated CMR images were acquired in 5 subjects who performed exercise at different intensity levels under different physiological conditions while breathing freely. The respiratory motion was estimated by applying principal components analysis (PCA). A sign correction procedure was developed to correctly define inspiration and expiration, based on either tracking of the diaphragmatic motion or estimation of the lung volume or a combination of both. Evaluation was done using a plethysmograph signal as reference.
 There was a good correspondence between the plethysmograph and the estimated respiratory signals. Respiratory motion was effectively captured by one of the PCA components in 88% of the cases. Moreover, the proposed method successfully estimated the respiratory phase in 91% of the evaluated slices. The pipeline is robust, admitting a slight decline in performance with increased exercise intensity. Respiratory motion was accurately estimated by means of PCA and the application of a sign correction procedure. Our method showed prom...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research