Myocardial Local Frequency Shift Mapping
Junmin Liu1, James W Goldfarb2, and Maria Drangova1,3

1Imaging Research Laboratories, Robarts Research Institute, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada, 2Department of Research and Education, Saint Francis Hospital, Roslyn, NY, United States, 3Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada

### Synopsis

Local Frequency Shift (LFS) mapping of the myocardium may provide information about the integrity and organization of myofibers, which contribute anisotropic magnetic susceptibility. We present a myocardial LFS mapping method by explicitly removing the unwanted phase terms caused by B0 inhomogeneity and chemical-shift (CS) between fat and water. The proposed method was tested with human data and compared with the established high-pass filtering technique. The results demonstrate a gradient across the myocardial wall suggesting that LFS maps of the myocardium may enable visualization of myofiber orientation.

### PURPOSE

Local frequency shift (LFS) mapping techniques have shown potential for visualizing white matter microstructure (1). Extending these techniques to the heart may provide important information about the integrity and organization of myofibers. However, LFS values associated with myofibers (< 0.1 ppm) (2) are at least one-order of magnitude smaller than confounding variations in magnetic field homogeneity (ΔB0, ~ 1 ppm) and the chemical shift (CS) between fat and water (~ 3.5 ppm). Therefore joint estimation of myofiber LFS, ΔB0 and CS is challenging (3-4). We present a method to extract myocardial LFS maps by explicitly removing the ΔB0 and the CS-related components from multi-echo data (B0CS-LFS) and compare results with those from a previously described cardiac SWI method (5).

### METHODS

Data acquisition: Data were acquired at 1.5 T with a dark blood double inversion recovery gradient echo sequence (1 slice per breath-hold, repetition time 20 ms; 12 echo times, 2.4 – 15.5 ms (1.2 ms spacing); flip angle 20°; bandwidth 1860 Hz/pixel; in-plane resolution 2.3 ×1.7 mm2, slice thickness 8 mm; flow compensation in read and slice directions). The bipolar multi-echo data were split into odd- and even-echo data sets; the first echo was excluded from the odd-echo group because of significant image corruption caused by eddy currents (6).

The B0CS-LFS approach comprises three steps. First, a field (Δfb0) map and fat-fraction (FF) map are generated from the even- and odd-echo groups separately. For this purpose, we used the unwrapping-based B0 mapping technique B0-NICE (7), which also estimates a T2* map from all echoes. To mitigate issues related to the presence of local field information in the B0 map, we performed low-pass filtering of the B0 complex image, which is the Hermitian product between the sixth and the second echoes (even-echo) or the seventh and the third echoes (odd-echo). Both Hanning and moving-average filters were evaluated.

The second step is to remove the unwanted constant, B0, and CS phase components. To remove the constant phase term, the Hermitian products between echoes were calculated as follows:

$Ij,hp =Ij×I2*, even,$ [1a]

$Ij,hp =Ij×I3*, odd,$ [1b]

where Ij is the complex image at the jth echo and * denotes the complex conjugate. The LFS at each individual echo (fj) were estimated by removing the B0- and CS-related phase terms:

$fj =angle(Ij,hp×exp(-φb0,j)×exp(-φCS,j))/(2π×ΔTEj)$ [2]

where φb0,j is equal to 2π×Δfb0×ΔTEj; φCS,j is calculated using the six-peak fat model and FF map determined from step 1; ΔTEj is equal to (TEj – TE2) and (TEj – TE3) for the even and odd echoes, respectively.

The third step is to calculate the final LFS map, defined as the mean over all included echoes on a pixel-by-pixel basis.

While no reference standard exists for myocardial LFS mapping, we compared the B0CS-LFS results to a high-pass filtered approach (HPF-LFS) adapted from the cardiac SWI method described in (5), where filtered phase was scaled to frequency and averaged over the last nine echoes.

### RESULTS

Successful Δfb0 and FF maps were generated (Fig. 1), as demonstrated by the lack of fat-water swaps in the heart. More importantly, the circled region in the T2* map matches well with an infarct identified in a corresponding LGE image (not shown), while the field map in the region remains smooth. A decrease in LFS values (calculated using both HPF-LFS and B0CS-LFS approaches) is also observed in the infarct region (Fig. 2). Interestingly, spatial LFS gradients of approximately 4 Hz are observed across the myocardium in the vertical long axis (VLA) plane (Fig. 3), but are not observed in the short axis (SA) or horizontal long axis (HLA) planes (Fig. 4). Overall the HPF-LFS is smoother (less tissue textures) while the B0CS-LFS method increases visibility of edges, clearly separating tissue from blood pool and surrounding tissue.

### DISCUSSION

An acute hemorrhagic infarct-related LFS hypo-intensity was clearly seen in Fig. 2, as expected. The magnitude of the measured LFS gradient agrees with the ex vivo values (2). Variations of the fiber orientation with respect to B0 are likely responsible for the fact that the gradient is visualized in the VLA (Fig. 3) but not in the SA and HLA planes. Because the orientation-dependent LFS values are also very sensitive to the types of filters as well as the kernel-sizes used, caution should be exercised when using LFS for quantitative applications. Myocardial LFS mapping promises to be an effective and rapid tool for quantifying myofiber orientation, compared to alternative techniques, such as DTI.

### CONCLUSION

Myocardial LFS mapping can identify hemorrhagic infarct zones and has potential to depict myofiber orientation.

### Acknowledgements

M.D. is supported by a Career Investigator award from the Heart and Stroke Foundation of Ontario.

### References

[1]. Wharton S, Bowtell R. Effects of white matter microstructure on phase and susceptibility maps. Magn Reson Med 2015;73(3):1258-1269.

[2]. Dibb R, Qi Y, Liu C. Magnetic susceptibility anisotropy of myocardium imaged by cardiovascular magnetic resonance reflects the anisotropy of myocardial filament alpha-helix polypeptide bonds. J Cardiovasc Magn Reson 2015;17:60.

[3]. Dimov AV, Liu T, Spincemaille P, Ecanow JS, Tan H, Edelman RR, Wang Y. Joint estimation of chemical shift and quantitative susceptibility mapping (chemical QSM). Magn Reson Med 2015;73(6):2100-2110.

[4]. Sharma SD, Hernando D, Horng DE, Reeder SB. Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload. Magn Reson Med 2015;74(3):673-683.

[5]. Goldfarb JW, Hasan U, Zhao W, Han J. Magnetic resonance susceptibility weighted phase imaging for the assessment of reperfusion intramyocardial hemorrhage. Magn Reson Med 2014;71(3):1210-1220. [6]. Hernando D, Hines CD, Yu H, Reeder SB. Addressing phase errors in fat-water imaging using a mixed magnitude/complex fitting method. Magn Reson Med 2012;67(3):638-644.

[7]. Liu J, Drangova M. Method for B0 off-resonance mapping by non-iterative correction of phase-errors (B0-NICE). Magn Reson Med 2015;74(4):1177-1188.

### Figures

B0-NICE results in the VLA plane: (a), (b) and (c) are the T2*, even-echo B0 and even-echo FF maps, respectively. The ellipse in (a) indicates an acute hemorhagic infarct region. The odd-echo results (not shown) are similar.

The LFS map: (a) generated using the HPF-LFS method (64-point Hanning); (b) and (c) are obtained using the B0CS-LFS method with the Hanning and 17×17 moving-average filters, respectively. (b) and (c) are very similar. The ellipse is the same as in the T2* image in Figure 1.

Magnitude (a), HPF-LFS (b) and B0CS-LFS (c); the line profiles along the line-segment in (a) are plotted in (d). An epi- to endo-myocardium gradient is observed in both LFS maps; the B0CS method facilitates delineation of the tissue boundary (myocardium shaded in grey).

The LFS maps in the SA ((a) and (b)) and HLA ((c) and (d)) planes by using the HPF-LFS and the B0CS-LFS methods.

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