Automatic image-based respiratory signal extraction in real-time CMR

TL;DR

This paper presents an automatic method for extracting and determining the direction of respiratory signals from real-time cardiac MRI images, improving robustness and enabling better cardiac function analysis.

Contribution

The paper introduces a novel PCA-based approach with a two-step sign correction procedure for automatic respiratory signal extraction in real-time MRI.

Findings

High correlation with reference signals across all cases

More robust than recent methods

Enables respiration-aware cardiac function assessment

Abstract

Purpose: To develop a fully automatic method for extraction and directionality determination of respiratory signal in free-breathing, real-time (RT) cardiac MRI. Methods: The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. First, the signal polarity of all slices is made consistent with a reference slice. Second, a global sign correction is performed by maximizing the correlation of the respiratory signal with the zeroth-moment center curve. The proposed method is evaluated in multi-slice RT cine from eleven volunteers and two patients. The motion in a manually selected region-of-interest is used as reference. Results: The extracted respiratory signal using the proposed method exhibits high, positive correlation with the reference in all cases and is more robust compared to a recently proposed method. To demonstrate its clinical utility, the method is used to identify heartbeats from inspiratory and expiratory phases and, in turn, to account for respiration-induced changes in the cardiac output. Conclusions: The proposed method enables fully automatic extraction and directionality determinations of respiratory signal from RT cardiac cine images, allowing accurate cardiac function quantification.