Introduction

Cardiac DWI has a potential to achieve improved diagnosis through novel micro-structural and functional assessment_¹. The intravoxel incoherent motion (IVIM) theory, proposed by Le Bihan et al._². enables evaluation of living tissue diffusion movement and micro-vessel perfusion in vivo using multi-b-value DWI, and myocardial IVIM may play an important role assessment of various heart diseases_^3-6. In previous studies, we have introduced a turbo spin-echo based^₇ cardiac motion-compensated (MoCo-TSE-DWI)_^8,9 with fast elastic image registration (FEIR) for myocardial IVIM-DWI_⁹, it demonstrated improved robustness of quality of IVIM mapping without image distortion. However, MoCo-TSE-DWI due to its low signal-to-noise ratio (SNR). Recently, it has been reported the usefulness of EPI-DWI with deep-learning constrained Compressed SENSE (EPICS-AI) to enhance the SNR while reducing the image distortion by using higher reduction factor_^10-12. In this study, we attempted to combine all promising techniques towards robust cardiac IVIM-DWI, including second order (acceleration) motion-compensated MPG scheme_^13,14 (aMoCo), EPICS-AI and FEIR. The purpose of this study was to evaluate the robustness of IVIM mapping using aMoCo-EPICS-AI-DWI-FEIR.

Methods

Ten healthy volunteers (age range: 22-48 years) underwent MoCo-TSE-DWI and aMoCo-EPICS-AI-DWI (Fig.1) on a 3.0T MR clinical imager (Ingenia Elition X, Philips Healthcare) for image comparison. Imaging parameters for aMoCo-EPICS-AI-DWI-IVIM: FOV=300x300mm2, actual matrix size = 1.38 x 1.38 mm, AI Compressed SENSE-factor = 4.0, TE = 72ms, TR = 2beats, slice thickness = 8 mm, acquisition time = 1m53s, and three directions with b-values 0, 20, 40, 60, 80, 100, 200, 300, 400 s/mm2. Free-breathing acquisition with navigator respiratory gating and tracking is applied. The actual trigger delay (TD) and data acquisition window were visually determined by TD scout scan¹⁵. For quantitative image evaluation, three slices of left ventricular short-axis images were divided into 16 segments. The ADC, D*, D and f values of each segment were measured. These four parameters were defined by the following formula. The bi-exponential IVIM model is expressed as: SDWI /S0 = f＊exp ( -bD*) + (1-f) ＊exp (-bD) where S0 is SDWI at b = 0 seconds/mm2, SDWI the signal intensity at a given b-value, D the self-diffusion coefficient, f the self-diffusion fraction, and D* the pseudo-diffusion coefficient. The coefficient of variation (CV), mean, and standard deviations for a total 96 of four parameters were calculated and compared among the two IVIM-DWI imaging. Statistical analysis was performed with Wilcoxon signed-rank test and judged the difference as significant at p<0.05.

Results and Discussion

Representative DWI source images and corresponding IVIM maps of MoCo-TSE-DWI-FEIR (Fig.2) and aMoCo-EPICS-AI-DWI-FEIR (Fig.3) are shown. Table 1 shows the comparison of IVIM quantitative values. There was significant difference in ADC, D*, D and f values CVs between MoCo-TSE-DWI-FEIR and aMoCo-EPICS-AI-DWI-FEIR. CVs for all four parameters were significantly smaller in aMoCo-EPICS-AI-DWI-FEIR than in MoCo-TSE-DWI. aMoCo-EPICS-AI-DWI-FEIR demonstrated reduced individual and site-specific differences in the parameters obtained from IVIM-DWI imaging, compared with MoCo-TSE-DWI-FEIR. ADC values of aMoCo-EPICS-AI-DWI-FEIR approximated the D value, while MoCo-TSE-DWI-FEIR showed a discrepancy between ADC and D values. Figure 4 shows the representative ADC maps of MoCo-TSE-DWI-FEIR and aMoCo-EPICS-AI-DWI-FEIR. The signal uniformity of the cardiac wall was improved with aMoCo-EPICS-AI-DWI-FEIR compared with MoCo-TSE-DWI-FEIR. These results suggest that IVIM parameters obtained from aMoCo-EPICS-AI-DWI-FEIR could be more accurate.

Conclusion

aMoCo-EPICS-AI-DWI-FEIR might be the best method for myocardial IVIM-DWI with better image quality, motion robustness and improved quantitative accuracy.

Acknowledgements

No acknowledgement found.

References

1. Nielles-Vallespin S, et.al, Cardiac Diffusion: Technique and Practical Application. J Magn Reson Imaging. 2020 Aug;52(2):348-368. doi: 10.1002/jmri.26912. 2. Le Bihan, D. et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988;168, 497–505, https://doi.org/10.1148/radiology.168.2.3393671. 3. Spinner GR, et al. Bayesian intravoxel incoherent motion parameter mapping in the human heart. J Cardiovasc Magn Reson. 2017 Nov 6;19(1):85. doi: 10.1186/s12968-017-0391-1. 4. Xiang SF, et al. STROBE-A preliminary investigation of IVIM-DWI in cardiac imaging. Medicine (Baltimore). 2018 Sep;97(36):e11902. doi: 10.1097/MD.0000000000011902. 5. Spinner GR, et al. On probing intravoxel incoherent motion in the heart-spin-echo versus stimulated-echo DWI. Magn Reson Med. 2019 Sep;82(3):1150-1163. doi: 10.1002/mrm.27777. 6. Xiang SF, et al. Intravoxel Incoherent Motion Magnetic Resonance Imaging with Integrated Slice-specific Shimming for old myocardial infarction: A Pilot Study. Scientific Reports 2019:9:19766. 7. Goto Y, et.al, Simple and robust cardiac diffusion weighted imaging using single-shot Turbo Spin-Echo with peripheral pulse gating. Proc. ISMRM:2016.3476). 8. Goto Y, et al, Improvement of distortion-free cardiac diffusion weighted imaging (DWI) using motion-compensated single-shot turbo spin-echo (MoCo-TSE) DWI. Proc. ISMRM:2020.2077. 9. Goto Y, et al, Improvement of distortion-free cardiac IVIM-DWI using motion-compensated single-shot turbo spin echo DWI with fast elastic image registration. Proc. ISMRM:2022.4562. 10. Yoneyama M, et al, SNR boost in whole-body DWIBS utilizing deep learning constrained Compressed SENSE reconstruction. Proc. ISMRM:2022.3655. 11. Yoneyama M, et al, SNR enhancement in rapid high b-value prostate single-shot DW-EPI utilizing deep learning constrained Compressed SENSE reconstruction. Proc. ISMRM:2022.3712. 12. Yoneyama M, et al, Pseudo-3D whole-brain ultra-thin-slice diffusion-weighted imaging of the brain utilizing deep learning constrained Compressed SENSE. Proc. ISMRM:2022.4732. 13. Chen R, et al, Second-order motion compensated single-shot spin echo planar imaging sequence using Compressed SENSE: a pilot study. Proc. ISMRM:2023.4290. 14. Luo W, et al, Computed DWI with 2nd Order motion Compensated DWI for Diagnosis of the Myocardial Infarction without Contrast Agents. Proc. ISMRM:2023.4291. 15. Moulin K, et al. Probing cardiomyocyte mobility with multi-phase cardiac diffusion tensor MRI. PLoS One. 2020 Nov 12;15(11):e0241996. doi: 10.1371/journal.pone.0241996.