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Accelerated Multicontrast Volumetric Imaging Using Compressed Sensing Parallel Imaging Reconstruction with Low Rank and Spatially Varying Edge-Preserving Constraints: In-Vivo Preclinical Validation for High-Resolution Myocardial Infarction Characterization
Li Zhang1,2 and Graham Wright1,2

1Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, 2Schulich Heart Research Program and Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada

Synopsis

To improve characterization of myocardial infarction using high-resolution multicontrast volumetric imaging, this work presents a new compressed sensing parallel imaging reconstruction using low rank and spatially varying edge-preserving constraints. The proposed method was validated in vivo in preclinical studies on pigs with chronic myocardial infarction with comparison to histopathology, demonstrating the promise of robust reconstruction of fine image detail from a single breath-hold multicontrast acquisition at an isotropic resolution of 1.5mm.

Background and Purpose

Recent findings from preclinical experiments and clinical patient studies have indicated that an isotropic resolution on the order of 1.5mm would be appropriate for characterizing the peri-infarct region that might be associated with ventricular tachycardia1,2,3. Multi-contrast late enhancement (MCLE)4 images offer better visualization of myocardial infarction (MI) than conventional IR-FGRE. Accelerated three-dimensional (3D) MCLE using a compressed sensing method has been demonstrated in both preclinical studies5,6 and clinical studies7 for high-resolution MR characterization of peri-infarct regions. However, artifacts in the tissue border area may be present, particularly in images with low contrast-to-noise ratios (CNR). In this work, a new Compressed sensing Parallel imaging reconstruction with Low rAnk and Spatially varying Edge-preserving constRaints (CP-LASER) was proposed to improve reconstruction for accelerated 3D MCLE with respect to robustness and consistency.

Theory

3D MCLE acquires multicoil multicontrast data, where an individual coil dataset is a series of multicontrast volumes acquired at different TIs in the diastolic periods using a balanced SSFP readout after an inversion. CS-LASER6 provides a coil-by-coil reconstruction of multicontrast volumes from a highly accelerated 3D MCLE acquisition using low rank and spatially varying edge- preserving constraints. To take advantage of coil sensitivity information in the CS-LASER framework, a compressed sensing parallel imaging reconstruction can be written as

$$$\hat{\mathbf{C}} = \arg\min_{\substack{\mathbf{C}}} \; \{\sum\limits^{\text{#coil}}\limits_{q=1}\|\mathbf{F_{u}(\mathbf{S}_{q}C\hat{\Phi}})-\mathbf{d}_{q}\|_{2}^{2}+\lambda\sum\limits_{l=1}\limits^{L}J(\mathbf{C}^{(l)})\},$$$

where: $$$\mathbf{d}_{q}$$$ is a vector formed by concatenating the undersampled $$$q$$$th-coil k-space data acquired for each TI; $$$\mathbf{S}_{q}$$$ is a diagonal matrix representing the sensitivity of the $$$q$$$th coil; rows of the pre-estimated $$$\hat{\Phi}$$$ and columns of $$$\mathbf{C}$$$ span the temporal and spatial subspaces of the underlying multicontrast volume series respectively; the operator $$$\mathbf{F_{u}}$$$ performs a partial Fourier transform on each contrast-weighted volume represented by a column of $$$\mathbf{S}_{q}\mathbf{C}\hat{\Phi}$$$ and then concatenates the results into a vector; $$$\mathbf{C}^{(l)}$$$ is a 3D matrix formed by reshaping the $$$l$$$th column of $$$\mathbf{C}$$$; the spatially varying edge-preserving constraint $$$J(\cdot)$$$ is formulated using the weighed total variation, as defined in CS-LASER6; and $$$\lambda$$$ is the regularization parameter. The sensitivity maps $$$\{\mathbf{S}_{q}\}$$$ are estimated using an eigenvalue-based method, referred to as ESPIRiT8. CP-LASER also uses the multiscale iterative reconstruction framework6. The underlying multicontrast volume series can then be obtained as the matrix product $$$\hat{\mathbf{C}}\hat{\Phi}$$$.

Methods

Three Yorkshire pigs with six-week-old infarcts were imaged after injection of 0.2mmol/kg Gadolinium-DTPA using an ECG-gated 3D MCLE sequence with a 160x160x10 acquisition matrix over a 1.5cm-thick slab with corresponding resolution of 1.5mm3. The undersampled datasets were prospectively acquired at acceleration rates of 3 and 5 using Variable Density Poisson-disk Sampling patterns with a 16-channel anterior cardiac coil array in a GE 3T scanner. The 3D MCLE acquisition with 3-fold acceleration was performed in a navigator-gated free-breathing scan and the 5-fold acceleration was performed in a single breath-hold. IR-FGRE images were also acquired with the parameters as follows: acquisition matrix = 160x160; FOV = 24cm; slice thickness = 5mm. For comparison, CS-LASER and L1-ESPIRiT8 with wavelet regularization were also implemented. For CP-LASER and L1-ESPIRiT, the multicoil data were compressed to seven virtual channels9 and used to estimate ESPIRiT coil sensitivity maps. At each scale level for CS-LASER, a coil-by-coil reconstruction was performed and the weights were then re-estimated from the combined multicontrast volumes using the sum-of-squares method. For the histological processing, representative slices were stained with Masson’s Trichrome, depicting collagen deposition in blue and healthy myocardium in red.

Results

In FIG. 2, CP-LASER provides the best image quality among various methods at acceleration rates of both 3 and 5 for different TIs; the infarct characteristics from CP-LASER match the best with the findings from the histopathology (FIG. 1d); results from CS-LASER present artificial edges in the images of the early TI, as indicated by the arrows; L1-ESPIRiT produces results with blurry tissue features and residual artifacts. In FIG. 3, CP-LASER presents sharper infarct-myocardium borders than CS-LASER and L1-ESPIRiT for both the high CNR (FIG. 3b) and low CNR cases (FIG. 3a); and the advantage of CP-LASER is pronounced in the low CNR case.

Conclusions

CP-LASER enables robust reconstruction of fine anatomical detail for highly accelerated multicontrast volumetric imaging and can provide consistent performance in low CNR situations. We also successfully demonstrated the feasibility of accelerated 3D MCLE with CP-LASER for characterizing the peri-infarct region in vivo at an isotropic high resolution.

Acknowledgements

Funding support is acknowledged from GE Healthcare and the Canadian Institutes of Health Research.

References

[1] R. Ranjan et. al, Circ Arrhythm Electrophysiol, 2011;4(3):279-86.

[2] R. Ranjan et. al, Circ Arrhythm Electrophysiol, 2012;5:1130-5.

[3] J. Fernandez-Armenta et. al, Circ Arrhythm Electrophysiol, 2013;6(3):528–37.

[4] J. S. Detsky et. al, MRM, 2007;58(2):365-72.

[5] L. Zhang et. al, ISMRM, Singapore, 2016. p.4215.

[6] L. Zhang et. al, MRM, 2016. DOI: 10.1002/mrm.26402.

[7] L. Zhang et. al, ISMRM, Singapore, 2016, p.2542.

[8] M. Uecker et. al, MRM, 2014;71(3):990–1001.

[9] M. Uecker et. al, ISMRM, Utah, USA, 2013, p.2657.

Figures

FIG. 1 a: The reconstructed magnitude image from CP-LASER for a representative short-axis slice of one pig heart. The bright rims indicated by the arrow heads are the infarct regions. b: The signal intensity profile shows the tissue dynamics along the yellow dashed line indicated in a. c: The IR-FGRE image that corresponds to the representative slice at a resolution of 1.5 x 1.5 x 5 mm3. d: a local patch of the corresponding histopathology image shows the same infarct region as indicated by the arrow heads.

FIG. 2 The reconstructed 3D MCLE magnitude images from various methods with acceleration rates of 3 and 5 for the same representative slice at inversion times of 75 ms and 117 ms. The image with the yellow border is a part of the image in FIG. 1a.

FIG. 3 a: The signal intensity profiles taken from the images in the three subfigures of FIG. 2b1-3 along the dashed line indicated in FIG. 1a. b: The signal intensity profiles taken from the images in the three subfigures of FIG. 2c1-3 along the dashed line indicated in FIG. 1a.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)
3874