Muhammad Usman1, David Atkinson2, Christoph Kolbitsch1, Tobias Schaeffter1, Claudia Prieto1,3. 1. King's College London, Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, Medical Engineering Centre of Research Excellence, London, UK. 2. University College London, Centre for Medical Imaging, London, UK. 3. Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile.
Abstract
PURPOSE: To present and validate a manifold learning (ML)-based method that can estimate both cardiac and respiratory navigator signals from electrocardiogram (ECG)-free free-breathing cardiac magnetic resonance imaging (MRI) data to achieve self-gated retrospective CINE reconstruction. MATERIALS AND METHODS: In this work the use of the ML method is demonstrated for 2D cardiac CINE to achieve both cardiac and respiratory self-gating without the need of an external navigator or ECG signal. This is achieved by sequentially applying ML to two sets of retrospectively reconstructed real-time images with differing temporal resolutions. A 1D cardiac signal is estimated by applying ML to high temporal resolution real-time images reconstructed from the acquired data. Using the estimated cardiac signal, a 1D respiratory signal was obtained by applying the ML method to low temporal resolution images reconstructed from the same acquired data for each cardiac cycle. Data were acquired in five volunteers with a 2D golden angle radial trajectory in a balanced steady-state free precession (b-SSFP) acquisition. The accuracy of the estimated cardiac signal was calculated as the standard deviation of the temporal difference between the estimated signal and the recorded ECG. The correlation between the estimated respiratory signal and standard pencil beam navigator signal was evaluated. Gated CINE reconstructions (20 cardiac phases per cycle, temporal resolution ∼30 msec) using the estimated cardiac and respiratory signals were qualitatively compared against conventional ECG-gated breath-hold CINE acquisitions. RESULTS: Accurate cardiac signals were estimated with the proposed method, with an error standard deviation in comparison to ECG lower than 20 msec. Respiratory signals estimated with the proposed method achieved a mean cross-correlation of 94% with respect to standard pencil beam navigator signals. Good quality visual scores of 2.80 ± 0.45 (scores from 0, bad, to 4, excellent quality) were observed for the proposed approach in comparison with the conventional ECG-gated breath-hold images (visual score: 3.00 ± 0.71). CONCLUSION: Accurate respiratory and cardiac navigator signals can be estimated using the proposed framework from the acquired data itself, resulting in retrospective self-gated CINE reconstruction with high spatial and temporal quality.
PURPOSE: To present and validate a manifold learning (ML)-based method that can estimate both cardiac and respiratory navigator signals from electrocardiogram (ECG)-free free-breathing cardiac magnetic resonance imaging (MRI) data to achieve self-gated retrospective CINE reconstruction. MATERIALS AND METHODS: In this work the use of the ML method is demonstrated for 2D cardiac CINE to achieve both cardiac and respiratory self-gating without the need of an external navigator or ECG signal. This is achieved by sequentially applying ML to two sets of retrospectively reconstructed real-time images with differing temporal resolutions. A 1D cardiac signal is estimated by applying ML to high temporal resolution real-time images reconstructed from the acquired data. Using the estimated cardiac signal, a 1D respiratory signal was obtained by applying the ML method to low temporal resolution images reconstructed from the same acquired data for each cardiac cycle. Data were acquired in five volunteers with a 2D golden angle radial trajectory in a balanced steady-state free precession (b-SSFP) acquisition. The accuracy of the estimated cardiac signal was calculated as the standard deviation of the temporal difference between the estimated signal and the recorded ECG. The correlation between the estimated respiratory signal and standard pencil beam navigator signal was evaluated. Gated CINE reconstructions (20 cardiac phases per cycle, temporal resolution ∼30 msec) using the estimated cardiac and respiratory signals were qualitatively compared against conventional ECG-gated breath-hold CINE acquisitions. RESULTS: Accurate cardiac signals were estimated with the proposed method, with an error standard deviation in comparison to ECG lower than 20 msec. Respiratory signals estimated with the proposed method achieved a mean cross-correlation of 94% with respect to standard pencil beam navigator signals. Good quality visual scores of 2.80 ± 0.45 (scores from 0, bad, to 4, excellent quality) were observed for the proposed approach in comparison with the conventional ECG-gated breath-hold images (visual score: 3.00 ± 0.71). CONCLUSION: Accurate respiratory and cardiac navigator signals can be estimated using the proposed framework from the acquired data itself, resulting in retrospective self-gated CINE reconstruction with high spatial and temporal quality.
Authors: Ukash Nakarmi; Joseph Y Cheng; Edgar P Rios; Morteza Mardani; John M Pauly; Leslie Ying; Shreyas S Vasanawala Journal: Proc IEEE Int Symp Biomed Imaging Date: 2020-05-22
Authors: Ozan Sayin; Haris Saybasili; M Muz Zviman; Mark Griswold; Henry Halperin; Nicole Seiberlich; Daniel A Herzka Journal: Magn Reson Med Date: 2016-03-10 Impact factor: 4.668
Authors: Jaume Coll-Font; Shi Chen; Robert Eder; Yiling Fang; Qiao Joyce Han; Maaike van den Boomen; David E Sosnovik; Choukri Mekkaoui; Christopher T Nguyen Journal: Magn Reson Med Date: 2021-08-13 Impact factor: 4.668
Authors: Tao Zhang; Joseph Y Cheng; Yuxin Chen; Dwight G Nishimura; John M Pauly; Shreyas S Vasanawala Journal: Magn Reson Med Date: 2015-07-29 Impact factor: 4.668