Adaptive RR prediction for cardiac MRI

Abstract

Cardiac magnetic resonance imaging (MRI) is very challenging due to the perpetual heart movements. This movement is pseudo-periodic and implies several issues for image acquisition. Building a single image requires several shots, done on a specific timing of the cardiac cycle. Nowadays the heart rate is estimated before imaging and then it is assumed not to evolve during acquisition. Additionally, in order to remove motion artifacts, the patients are asked to perform breathholds. Unfortunately, while performing a breath-hold, the heart rate is changing in a significant way. To address this problem in the framework of clinical applications, we propose a simple method to predict the out-coming RR interval, in order to compute adapted MR parameters. The RR interval is the time separating two consecutive R waves and corresponds to one cardiac cycle. Due to its simplicity this method is clinically applicable. The prediction is performed by modeling the heart rate variation as a linear combination of different sensors (here, respiratory sensor and amplitude of R wave). We evaluated this method on five healthy subjects in a clinical setup.

Bayesian framework for artifact reduction on ECG IN MRI

“…These estimates could be used for a semi-online filtering of the MR-ECG, where the delayed impulse response estimations are used to filter in real-time the MFG command signals for MFG artifact suppression. An online phase assignment procedure is also conceivable, by using a RR interval prediction [12]. The unification of ECG and MFG models for Bayesian filtering overcomes state-of-the-art MR-ECG signal processing limitations and opens the way of accurate interpretation during a complete MRI examination.…”

Adaptive black blood fast spin echo for end‐systolic rest cardiac imaging

“…2 and 3. The second order has been chosen because it is a good compromise between accurate prediction and computational efficiency (21) and allows real‐time processing. Setting the model to an higher order will improve accuracy of the prediction but lengthen the computation and convergence times.…”

“…Our hypothesis is that most people make small movements while they are holding their breath, yielding thoracic volume variations that could modify the RR interval variation patterns. Moreover, a previous study (21) has reported some prediction improvements when using respiratory signals in breath‐hold.…”

“…This variation has been demonstrated while breathing, denoted by respiratory sinus arrhythmia (19), but also during breath‐hold (20). To model these beat‐to‐beat cardiac cycle changes, an RR interval modeling has been proposed (21).…”

“…Δ n is defined as the difference between the n th and ( n − 1) th R wave time. By predicting only one upcoming RR interval ${\hat \Delta}_{n +1}$ , the DIR‐FSE sequence could be started by considering a fixed T D as already proposed but not validated on humans (21). The underlying hypothesis is that the systole has always the same duration.…”