4751

Cardiac and Respiratory Motion-Resolved 5D Imaging Using a Free-Running Framework: Comparison of Cartesian and Radial Trajectories
Christopher W Roy1, Jerome Yerly1,2, Jessica AM Bastiaansen1, Nemanja Masala1, Lorenzo Di Sopra1, Jens Wetzl3, Christoph Forman3, Davide Piccini1,4,5, and Matthias Stuber1,2

1Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 2Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 3Siemens Healthcare GmbH, Erlangen, Germany, 4Advanced Clinical Imaging Technology (ACIT), Siemens Healthcare AG, Lausanne, Switzerland, 5LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Synopsis

Recent advances have enabled high resolution cardiac imaging using continuous acquisitions that do not require external gating devices and can be reconstructed in arbitrary dimensions. Here, we extend the use of this Free-running framework to a fully self-gated free-breathing 3D Cartesian trajectory with spiral profile ordering for cardiac and respiratory motion resolved 5D imaging. We demonstrate the feasibility of this Cartesian approach by reconstructing and comparing images from both radial and Cartesian sequences with matching scan parameters in healthy volunteers. Overall, Cartesian images demonstrated comparable cardiac and respiratory motion albeit with more residual artifacts present in the Cartesian images.

Introduction

Whole-heart MRI requires acquisitions and reconstructions that are robust to cardiac and respiratory motion. In conventional examinations, a combination of ECG-gating and breath-holding or respiratory navigation is used to freeze physiological motion and produce sharp images. However, external gating devices add to patient setup time, and respiratory control can be difficult in some patients. Recent advances have enabled high-resolution imaging using continuous radial phyllotaxis acquisitions that do not require external devices and can be reconstructed exploiting the intrinsic similarities of arbitrary dimensions (Free-running framework) [1-6]. This is accomplished using a repeated readout along the superior-inferior (SI) direction for retrospective extraction of respiratory and cardiac signals, which informs the compressed sensing reconstruction to suppress undersampling artifact. In this work, we investigate the feasibility of applying the Free-running framework to a Cartesian trajectory with spiral profile reordering for cardiac and respiratory resolved 5D imaging [7-9]. Cartesian sampling is a potentially useful alternative to radial as it is less sensitive to trajectory errors, the imaging volume can more easily be tailored to specific applications allowing for a trade-off between scan time, contrast, and resolution, and the reconstruction can be performed without computationally demanding re-gridding. We demonstrate the feasibility of Free-running Cartesian 5D imaging in healthy volunteers and both qualitatively and quantitatively compare image reconstructions of radial and Cartesian acquisitions.

Methods

Data were acquired in 8 healthy volunteers (5 male, ages 26-33 years) on a 1.5T clinical MRI scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). Two prototype slab-selective 3D bSSFP sequences with radial (Fig. 1b) and Cartesian (Fib. 1c) sampling were acquired using golden angle phyllotaxis [6] and spiral profile reordering [8], respectively. To match the self-navigation and flexible binning of the radial acquisition, the Cartesian sequence was modified to acquire data independent of the ECG, and one readout oriented along the SI direction was repeated per interleave for subsequent extraction of physiological signals. All imaging parameters, listed in Fig. 1, were consistent between the two sequences except for the field-of-view which was isotropic (220 mm3) for the radial sequence and rectangular for Cartesian (220 x 380 x 165 mm3). Additionally, the RF excitation angle was lowered in some volunteers due to SAR limitations. Data was retrospectively sorted into 16-25 cardiac phases of 50ms window-width and 4 respiratory motion states [3]. The resulting 5D datasets were reconstructed offline using a k-t SPARSE SENSE algorithm with regularization along both temporal dimensions [1, 3]. Qualitative comparison of images was performed by visual inspection across all reconstructed slices and temporal dimensions. Quantitative comparison of contrasts measurements was performed using the ratio of manually selected regions of interest containing the blood pool and myocardium. Measurements of image sharpness were performed using the signal gradient along the blood-myocardium, heart-lung, and heart-liver boundaries [10]. All measurements were taken during end-systole at end-expiration and statistical analysis was performed using paired t-tests.

Results

Cardiac and respiratory signals were successfully extracted (Fig. 2) from all 8 volunteers and used for reconstructions of radial and Cartesian acquisitions. In general, Cartesian reconstructions took less time (3-5 hours) using a CPU Fourier transform than their radial counterparts (5-8 hours) which used a GPU non-uniform Fourier Transform. Figs. 3 and 4 show two example reconstructions. Visual comparison of radial and Cartesian images shows that dynamic cardiac and respiratory motion is correctly resolved. However, Cartesian images contain noticeably more residual artifact, leading to an apparent decrease in contrast between the myocardium and blood pool, which was confirmed to be statistically higher (p<0.05) for radial images (Fig. 5a). Quantitative comparison of sharpness also yielded statistically higher (p<0.05) measurements along the blood-myocardium and heart-lung boundaries for radial images, but there was no significant difference in sharpness at the heart-liver boundary.

Discussion and Conclusions

5D imaging using a Free-running framework is a promising technique for easily assessing the structure and function of the whole-heart in one single scan. It can be performed without ECG during free-breathing and without extensive image planning relative to the standard clinical 2D CINE approach. Here, we show that Cartesian sampling may be used as an alternative to radial phyllotaxis sampling within the Free-running framework. This study is to our knowledge, the first comparison of 5D imaging between radial and Cartesian acquisitions. To provide a baseline comparison, acquisition and reconstruction parameters were nearly identical. Overall the Cartesian sequence produced similar images to the radial sequence. Still, additional optimization of the Cartesian approach is required to improve image quality. In particular, the use of ESPIRiT for coil sensitivities may help reduce residual fold-over artifacts in the Cartesian 5D images [11].

Acknowledgements

No acknowledgement found.

References

[1] S. Coppo, D. Piccini, G. Bonanno, J. Chaptinel, G. Vincenti, H. Feliciano, R.B. van Heeswijk, J. Schwitter, M. Stuber. Free-Running 4D Whole-Heart Self-Navigated Golden Angle MRI: Initial Results. Magn. Reson. Med. 2015; 74:1306–1316.

[2] L. Feng, L. Axel, H. Chandarana, K. T. Block, D. K. Sodickson, and R. Otazo, “XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing,” Magn. Reson. Med. 2016; 75:775–788

[3] L. Feng, S. Coppo, D. Piccini, J. Yerly, R.P. Lim, P.G. Masci, M. Stuber, D.K. Sodickson, R. Otazo. 5D whole-heart sparse MRI. Magn. Reson. Med. 2018; 79:826–838

[4] J. Yerly, D. Piccini, L. Di Sopra, J. AM Bastiannsen, S. Coppo, and M. Stuber “Improved Time Efficiency and Workflow for Fully Self-Gated Non-Contrast 5D Imaging of the Heart,” in Proc. Intl. Soc. Mag. Reson. Med. 2017, 2874

[5] L. Di Sopra, D. Piccini, S. Coppo, J. A.M. Bastiaansen, M. Stuber, and J. Yerly, “Motion-resolved 5d imaging of the heart: time to get rid of the ECG?,” in Proc. Intl. Soc. Mag. Reson. Med. 2017, 3148

[6] D. Piccini, A. Littmann, S. Nielles-Vallespin, and M. O. Zenge, “Spiral phyllotaxis: the natural way to construct a 3D radial trajectory in MRI.,” Magn. Reson. Med. 2011; 66: 1049–56

[7] C. Forman, D. Piccini, R. Grimm, J. Hutter, J. Hornegger, and M. O. Zenge, “High-resolution 3D whole-heart coronary MRA: a study on the combination of data acquisition in multiple breath-holds and 1D residual respiratory motion compensation,” Magn. Reson. Mater. Physics, Biol. Med. 2014 27; 435–443

[8] J. Wetzl, F. Lugauer, M. Schmidt, A. Maier, J. Hornegger, and C. Forman, “Free-Breathing, Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart Using Cartesian Sampling,” Proc. Intl. Soc. Mag. Reson. Med. 2016, 411

[9] T. Correia, G. Ginami, G. Cruz, R. Neji, R. Rashid, R.M. Botnar, C. Prieto. Optimized respiratory-resolved motion-compensated 3D Cartesian coronary MR angiography. Magn Reson Med. 2018;80(6):2618–2629

[10] R. Ahmad, Y. Ding, O.P. Simonetti. Edge sharpness assessment by parametric modeling: Application to magnetic resonance imaging. Concepts in Magnetic Resonance. 2015; 44:138–149.

[11] M. Uecker et al., “ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA.,” Magn. Reson. Med. 2014; 71:990–1001

Figures

Fig. 1 Acquisition scheme for fully self-gated 5D MRI. a) Data is acquired by repeating interleaves independent of the underlying physiological cycles, including one readout oriented along the superior-inferior direction for subsequent signal extraction and sorting of data in to cardiac phases and respiratory states. b) 3D radial phyllotaxis and Cartesian spiral data are acquired by rotating the spiral patterns for each interleave of data by the golden angle enabling flexible reconstruction of arbitrary motion states. Acquisition parameters: RF excitation angle: 40-60°, acquired lines: 126478, lines per interleave: 22, TR/TE: 3.93/1.91 ms, spatial resolution: (1.15 mm)3, acquisition time: 8 minutes.

Fig. 2 Overview of self-gating signals. Comparison of SI projections versus time (top) for Cartesian (left) and radial (right) data. Respiratory (middle) and cardiac (signals) were successfully extracted from each volunteer and acquisition type allowing for subsequent binning of the data.

Fig. 3 Animated figure showing cardiac and respiratory motion-resolved images using the Free-running framework from volunteer 1. Radial (top) and Cartesian (bottom) acquisitions in transverse, coronal, and sagittal views are shown. Note that the FOV in the radial images is cropped to match their Cartesian counterparts. Cardiac and respiratory motion is well demonstrated by both imaging sequences although residual artifacts are more noticeable in the Cartesian images.

Fig. 4 Animated figure showing cardiac and respiratory motion-resolved images using the Free-running framework from volunteer 2. Radial (top) and Cartesian (bottom) acquisitions in transverse, coronal, and sagittal views are shown. Cardiac and respiratory motion is again well demonstrated by both Cartesian and radial acquisitions, but residual artifacts are slightly more noticeable in the Cartesian case.

Fig. 5 Quantitative comparisons of contrast and sharpness between radial and Cartesian images. a) Contrast measured between the blood and myocardium was also statistically higher for radial (* = p<0.05). b) Sharpness measurements of the blood-myocardium and heart-lung boundaries were higher for radial images (* = p<0.05) not statistically different at the heart-liver boundary. For each measurement, the mean and standard deviation across all 8 volunteers is shown.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
4751