Free-Breathing, Self-Navigated Isotropic 3-D CINE Imaging of the Whole Heart Using Cartesian Sampling

Jens Wetzl^{1,2}, Felix Lugauer^{1}, Michaela Schmidt^{3}, Andreas Maier^{1,2}, Joachim Hornegger^{1,2}, and Christoph Forman^{3}

The current gold standard for the evaluation of cardiac function is 2-D CINE imaging, commonly acquired in multiple breath-holds, featuring high in-plane resolution, but thick slices. Recently, 3-D CINE acquisitions with isotropic resolution have been proposed, e.g. free-breathing radial 3-D CINE [1], which requires a computationally expensive reconstruction, or single breath-hold Cartesian 3-D CINE [2], which requires patient cooperation and breath-hold capability. To address these limitations, we propose a method for free-breathing, isotropic Cartesian 3-D CINE imaging.

Free-breathing 3-D CINE imaging in short-axis (SA) orientation was performed in 7 healthy volunteers ($$$1$$$ female, age $$$30{}\pm{}13$$$) on a $$$1.5\,\text{T}$$$ clinical MR scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). One acquisition covered just the left ventricle (LV), another covered the whole heart (WH). A 3-D volume-selective, ECG-gated, bSSFP prototype imaging sequence with the following parameters was used: TR = $$$2.8\,\text{ms}$$$, TE = $$$1.2\,\text{ms}$$$, $$$\alpha=38^\circ$$$, FOV for LV = $$$395{}\times{}(237{}\pm{}10){}\times{}(110\pm{}11)\,\text{mm}^3$$$, FOV for WH = $$$395{}\times{}(237{}\pm{}10){}\times{}(153\pm{}16)\,\text{mm}^3$$$, acquired voxel size $$$1.9\times{}2.1\times{}2.5\,\text{mm}^3$$$, interpolated to $$$(1.9\,\text{mm})^3$$$, temporal resolution $$$42\,\text{ms}$$$, fixed acceleration factor of $$$2.6$$$ compared to the fully-sampled matrix and a receiver bandwidth of $$$1000\,\text{Hz/px}$$$. For signal reception, 18$$$+$$$12 elements of an anterior $$$+$$$ posterior local coil matrix were used. For reference, a $$$12$$$-slice SA 2-D bSSFP acquisition with $$$\alpha=54^\circ$$$ and retrospective ECG gating in multiple breath-holds was performed to cover the LV with similar temporal resolution, identical in-plane resolution and a slice thickness of $$$8\,\text{mm}$$$.

Incoherent sub-sampling of the Cartesian phase-encoding plane was achieved with a spiral spokes sampling pattern, where the starting points for readouts within each phase are chosen along a spiral arm, and subsequent spiral arms are rotated by the golden angle (see Figure 1). As the first sample of each spoke is the k-space center, the sequence is suitable for respiratory self-gating [3]. In every such readout, the lung-liver boundary was tracked to obtain a 1-D respiratory signal (see Figure 2). Retrospective respiratory self-gating to end-expiration resulted in an effective undersampling factor of $$$11\pm{}6$$$ for LV and $$$14\pm{}7$$$ for WH 3-D CINE.

After a Fourier transform along the fully sampled readout, prototype image reconstruction for each phase-encoding plane was then performed using the mFISTA algorithm [4] with spatiotemporal wavelet regularization and incorporating two coil sensitivity maps (CSM) per receive channel to deal with wrapping in the phase-encoding direction [5]:

$$\{\hat{\boldsymbol{x}}_1,\hat{\boldsymbol{x}}_2\}=\underset{\{\boldsymbol{x}_1,\boldsymbol{x}_2\}}{\operatorname{argmin}}\sum_c\left\Vert\boldsymbol{A}\boldsymbol{F}\left(\textstyle\sum_{i=1}^2\boldsymbol{S}_{c,i}\boldsymbol{x}_i\right)-\boldsymbol{y}_c\right\Vert_2^2+\lambda\textstyle\sum_{i=1}^2\Vert\boldsymbol{W}\boldsymbol{x}_i\Vert_1,$$

where $$$\boldsymbol{A}$$$ is the sampling pattern, $$$\boldsymbol{F}$$$ is the Fourier transform, $$$\boldsymbol{S}_{c,i}$$$ is the $$$i^\text{th}$$$ CSM belonging to coil $$$c$$$, $$$\boldsymbol{y}_c$$$ is the measured data of coil $$$c$$$, $$$\boldsymbol{W}$$$ is the wavelet transform and $$$\lambda$$$ is the regularization parameter. This image reconstruction was fully integrated into the scanner software and multi-GPU-accelerated. The optimization was run for $$$40$$$ iterations with $$$\lambda=2\cdot{}10^{-3}$$$ of the maximum intensity.

For evaluation, we compared the acquisition and reconstruction times, contrast-to-noise ratio (CNR) as well as ventricular function (VF) parameters computed manually from the images of the gold standard 2-D CINE and our proposed 3-D CINE in corresponding slices of both LV data sets.

Qualitative results of the LV and WH 3-D CINE are shown in Figure 3. Quantitative results for acquisition time, reconstruction time and CNR are given in Table 1 and for VF parameters in Figure 4.

The high effective undersampling enabled free-breathing isotropic 3-D CINE with an acquisition time similar to the reference 2-D CINE. The lower CNR for 3-D CINE is due to the lower flip angle (because of SAR restrictions) and inflow effects, the latter causing the further drop from LV to WH 3-D CINE. A slight underestimation of the end-diastolic volume in the 3-D CINE, $$$1.8\,\text{ml}$$$ on average, is caused by prospective ECG triggering compared to retrospective ECG gating used by the 2-D CINE [6]. The end-systolic volume is overestimated by $$$1.6\,\text{ml}$$$ on average, most likely due to temporal regularization during reconstruction.

[1] S. Coppo *et al*. "Free-running 4D whole-heart self-navigated golden angle MRI: Initial results". Magn. Reson. Med. 74(5):1306-1316, 2015.

[2] J. Wetzl *et al*. "Isotropic 3-D CINE Imaging with Sub-2mm Resolution in a Single Breath-Hold".
Proc. ISMRM \#1011, 2015.

[3] D. Piccini *et al*. "Respiratory self-navigation for whole-heart bright-blood coronary MRI: methods for robust isolation and automatic segmentation of the blood pool". Magn. Reson. Med. 68(2):571-9, 2012.

[4] J. Liu *et al*. "Dynamic cardiac MRI reconstruction with weighted redundant Haar wavelets". Proc. ISMRM \#178, 2012.

[5] M. Uecker *et al*. "ESPIRiT – an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA". Magn. Reson. Med. 71:990–1001, 2014.

[6] G. Vincenti *et al*. "Compressed Sensing Single-Breath-Hold CMR for Fast Quantification of LV Function, Volumes, and Mass". JACC, 7(9):882-92, 2014.

Figure 1: Undersampling of the Cartesian phase-encoding plane using spiral spokes, in this case for 3 cardiac phases (red, green, blue) with 8 spokes each. Each spoke is the sampled version of a spiral arm, with starting angles of subsequent spokes being offset by the golden angle. Sampling always begins at the center of k-space.

Figure 2: The Fourier transform of k-space center lines acquired over time allows tracking of the lung-liver boundary for retrospective respiratory gating to end-expiration. The red line indicates the tracked boundary, green dots show the accepted sampling locations.

Figure 3: Comparison of 2-D CINE (left column) and 3-D CINE (middle column) short axis slices in diastole (first row) and systole (second row), as well as 3-D CINE images reformatted to horizontal long axis view (right column) for LV (first row) and WH coverage (second row).

Table 1: Comparison of quantitative results for reference 2-D CINE and LV/WH 3-D CINE. The acquisition time for 2-D CINE includes $$$10\,\text{s}$$$ breaks between each of the 6 breath-holds. Reconstruction was performed on the CPU for 2-D CINE and on the dual-GPU for 3-D CINE. Contrast-to-noise ratio
was measured between the blood pool and myocardium.

Figure 4: Bland-Altman plots of end-diastolic volume (EDV, left) and end-systolic volume (ESV, right) for the reference 2-D CINE and free-breathing 3-D CINE. Red lines denote the mean difference $$$E(D/S)V_\text{2-D}-E(D/S)V_\text{3-D}$$$, dashed blue lines represent the $$$95\,\%$$$ confidence intervals.

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

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