A Novel Concept for Motion Suppression Applied to Free-Breathing 3D Whole-Heart Coronary MRA: Respiratory Motion-Resolved Reconstruction
Davide Piccini1,2, Li Feng3, Gabriele Bonanno2, Simone Coppo2, Jérôme Yerly2,4, Ruth P. Lim5, Juerg Schwitter6, Daniel K. Sodickson3, Ricardo Otazo3, and Matthias Stuber2,4

1Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland, 2Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York City, NY, United States, 4Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 5Department of Radiology, Austin Health and The University of Melbourne, Melbourne, Australia, 6Division of Cardiology and Cardiac MR Center, University Hospital of Lausanne (CHUV), Lausanne, Switzerland


We hypothesize that sparse reconstruction algorithms can be exploited to reconstruct respiratory motion-resolved 3D MRA images of the heart without the need for breath-holding, navigators, or self-navigated respiratory motion correction. Phantom, volunteer, and patient acquisitions were performed and image quality was compared to 1D self-navigation for vessel sharpness, length and diagnostic quality. Respiratory motion-resolved reconstruction effectively suppresses respiratory motion artifacts with superior results with respect to self-navigation. Instead of discarding data or enforcing motion models for motion correction, motion-resolved reconstruction makes constructive use of all respiratory phases to improve image quality, and may lead coronary MRA closer to clinical practice.


Coronary MR angiography (MRA) has recently shown promising results in relatively large patient cohorts [1]. However, it still remains mostly confined to research use at a small number of experienced academic centers. Free-breathing whole-heart coronary MRA commonly uses navigators to counteract the effects of respiratory motion [2], but suffers from lengthy and unpredictable acquisition times. Conversely, self-navigation (SN) [3-5] promises 100% scan efficiency, but requires motion correction over a broad range of respiratory displacements, which may introduce image artifacts. We hypothesize that sparse reconstruction algorithms can be exploited to reconstruct respiratory motion-resolved 3D images of the heart without the need for breath-holding, navigators, self-navigated respiratory correction, or complex 3D motion correction.


First, a proof of principle acquisition on an in-house-built moving phantom (sinusoidal motion amplitude=3cm) was performed to assess the performance of the proposed motion-resolved sparse reconstruction. Subsequently, examinations in N=11 healthy volunteers and M=7 patients were performed on a 1.5T clinical MRI scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). In all cases, a T2-prepared, fat-saturated prototype 3D radial phyllotaxis bSSFP imaging sequence [6] was acquired segmented and ECG-triggered. Parameters: TR/TE=3.1/1.56ms, FOV=(220mm)3, matrix=1923, voxel size=(1.15mm)3, RF excitation angle 115°, and receiver bandwidth 898 Hz/Px. A total of ~12,000 radial readouts were acquired in 400-600 heartbeats during free-breathing with 100% scan efficiency. Using a respiratory signal directly extracted from the imaging data via modulations of the k-space center amplitude [7], individual signal-readouts were binned according to the respiratory state at which they were acquired (Fig. 1). The resulting series of undersampled 3D respiratory states were reconstructed using an eXtra-Dimensional Golden-angle RAdial Sparse Parallel imaging (XD-GRASP) [8] algorithm, which exploits sparsity along the newly created respiratory-state dimension. Datasets for 4 and 6 respiratory states (phases) were reconstructed. Image quality of the end-expiratory phase was compared to 1D respiratory self-navigation for coronary vessel sharpness [9], visible length and diagnostic quality on a scale from 0=non-visible to 2=diagnostic. Patient datasets were visually assessed in comparison to 1D self-navigation and, when available, to X-ray angiography.


Respiratory motion-resolved XD-GRASP reconstruction (both 4- and 6-phase) effectively suppresses respiratory motion artifacts both in the phantom acquisition and in free-breathing whole-heart coronary MRA (Fig.2). In the phantom, the average of 10 measurements resulted in a 2.73±0.3cm displacement between the two extreme states. In volunteers, average vessel sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (p<0.05) for the left main coronary artery (LM), the proximal- and mid-left anterior descending artery (LAD) (e.g. sharpness of mid-LAD 40.8±9.1% vs 34.9±10.2%), and the mid-right coronary artery (RCA). LM+LAD length was also significantly increased with respect to 1D self-navigation. The ratio of diagnostic coronary segments increased from 41/88 to 61/88 and 56/88 for the 4- and 6-phase reconstructions (Fig. 3a). The 4-phase XD-GRASP reconstruction reached 100% diagnostic quality for LM, proximal-LAD, and proximal-RCA. All numerical results are reported in Fig 4. Example reconstructions from patient datasets are shown in Fig 3b.


Respiratory motion-resolved XD-GRASP reconstruction provides a powerful alternative to conventional navigators or self-navigation. Although a larger number of motion states (6-phase versus 4-phase) can better resolve respiratory motion and reduce blurring, this is also associated with higher degrees of undersampling. The numerical results show comparable image quality for both motion-resolved reconstructions. The high correlation along the respiratory direction enables improved performance when compared to conventional sparse reconstructions exploiting spatial correlation only. This confirms that sparse reconstruction is typically more effective with dynamic than with static data (movies are easier to compress than images). The golden-angle arrangement of the 3D radial phyllotaxis trajectory intrinsically facilitates the extraction of the respiratory motion signal from the image data (all readouts cross the k-space center) and provides flexibility in data sorting (quasi-uniform spatial sampling over time –and therefore over respiratory bins– is facilitated). Simultaneously, the golden angle rotation ensures incoherence along the respiratory dimension, required for XD-GRASP reconstruction (the data are sufficiently distinct in different motion states, since the same k-space profile is never acquired twice). Although the end-expiratory 3D volume of the XD-GRASP reconstruction was selected for analysis, the motion-resolved datasets contain abundant anatomic information across the whole respiratory cycle, which may be further exploited (Fig.5).


XD-GRASP provides a highly promising alternative to navigator approaches for respiratory motion artifact suppression. Instead of discarding data or enforcing motion models for motion correction, XD-GRASP makes constructive use of all respiratory phases to improve image quality, and achieves superior quality compared to 1D respiratory self-navigation. The phyllotaxis trajectory and XD-GRASP reconstruction provide a synergistic combination that may lead coronary MRA closer to clinical practice.


The authors would like to acknowledge Dr. Florian Knoll from NYU School of Medicine for support with the GPU implementation of 3D NUFFT. This work was supported in part by the Center for Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net), a NIBIB Biomedical Technology Resource Center (NIH P41 EB017183) and in part by Grant #143923 from the Swiss National Science Foundation (SNF).


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Figure 1: Data sorting procedure for XD-GRASP reconstruction. The whole-heart coronary MRA dataset is binned into n respiratory phases from end-expiration (top) to end-inspiration (bottom), using the motion signal (a). The binning procedure maintains an equal number of readouts in each respiratory phase, leading to different bin widths. Due to the golden angle acquisition, uniform k-space coverage with distinct sampling patterns in each phase is achieved (b,c).

Figure 2: Example from the phantom experiment and from a representative volunteer comparing conventional gridding with 4-phase XD-GRASP reconstruction. For the volunteer, all phases are shown in axial (top) and coronal planes (bottom). Respiratory motion is resolved by sorting the data into different motion states, while streaking artifacts due to undersampling and residual intra-bin motion are effectively removed with XD-GRASP reconstruction.

Figure 3: (a) Example volunteer dataset where 1D SN did not lead to diagnostic quality. All coronaries are better depicted with XD-GRASP. Coronary segments graded as “visible” in 1D SN reach “diagnostic quality” with the proposed method (arrows). (b) Images from a selected patient dataset. 1D SN and the XD-GRASP reconstruction are compared to standard 2D X-ray angiography. A significant stenosis of the proximal-LAD is visualized in all datasets (arrow). The vessel appears sharper with XD-GRASP.

Figure 4: Quantitative results and diagnostic quality grading: Vessel sharpness, visible length and average values of the diagnostic quality grades for the 11 volunteers, using 1D respiratory self-navigation as compared with the proposed XD-GRASP reconstruction. A clear improvement can be seen in the respiratory-resolved XD-GRASP reconstructions.

Figure 5: Coronal reformat showing part of the whole-heart respiratory motion-resolved dataset. The animated cine scrolls through both slices and respiratory phases. The motion of both liver and heart clearly show how respiratory motion is resolved by the XD-GRASP algorithm and how the sharpness of the end-expiratory volume is maximized having the smallest residual intra-bin motion component.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)