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Quantitative dynamic MRI in the phase domain: Characterization of coronary arterial morphology using a simple Fourier method to accurately estimate bolus arrival times in DCE-MRI perfusion imaging
Karl P Kunze1, Teresa Vitadello2, Christoph Rischpler1, Markus Schwaiger1, and Stephan G Nekolla1

1Nuclear Medicine, TU Munich, Munich, Germany, 2Cardiology, TU Munich, Munich, Germany

Synopsis

This abstract proposes a simple method to extract the bolus arrival time (BAT) from myocardial DCE-MRI perfusion data using a low-frequency phase reconstruction of the deconvolution of arterial input functions with myocardial tissue curves. A simulation study is performed to test the proposed technique with respect to accuracy, robustness and superiority to existing approaches. A clinical PET/MRI example and a control group are examined to show the robustness of BAT estimation even without post-processing steps like surface coil intensity correction or saturation correction. The interpretation of the BAT as a surrogate for coronary path lengths is supported by coronary angiography.

Background

A major confounder to absolute quantification of myocardial plasma/blood flow (Fp/Fb) using dynamic perfusion MRI as compared to e.g. PET is the lack of simple relationships between image signal and physiologically meaningful quantities. User-interaction and post-processing complexity for e.g. maintaining linear signal-to-contrast agent relationships1 are important obstacles for robustness and clinical applicability of quantitative DCE-MRI. It is the goal of this work to show the potential of parameters such as the bolus arrival time (BAT) that can be quantified with minimal amounts of post-processing, as they depend only on the phase information within dynamic MRI data. While the BAT is needed for DCE-MRI deconvolution modeling, it can also be used directly as a surrogate for the path length of blood through the coronary vasculature. Especially in the context of PET/MRI, this abstract proposes an understanding of PET as inherently quantitative in the magnitude domain and dynamic MRI with its superior time resolution as inherently quantitative in the phase domain.

Methods

A new technique is introduced to estimate bolus delay using the low-frequency phase spectrum of a direct Fourier deconvolution of AIF and tissue curve: First, the tissue curve is padded to double length with a (linearly) decreasing tail to avoid discontinuity effects and shifted a further 10s from the AIF for stability. Both curves are subsequently Fourier transformed and directly deconvolved, i.e. divided in Fourier space. In order to exclude high-frequency noise amplification, the resulting deconvolved phase spectrum is zero-padded for frequencies >0.1 Hz. An inverse Fourier transform of the padded phase spectrum with all magnitudes set to unity results in a low-frequency phase reconstruction of the convolution kernel, i.e. the impulse response R(t). The bolus arrival time can be calculated from its main oscillation by subtracting positive and negative peak position (Fig. 1). A simulation study was performed testing accuracy, precision and superiority to similar approaches such as Time-To-Peak (TTP) analysis: Therefore, the blood-tissue exchange unit of the MMID4 model was used to create a reference tissue curve, which was convolved with a γ-variate kernel to simulate different BATs with and without dispersion. 100 iterations across a simulated BAT range of 0-8s were performed at SNR=40 (regional curves), SNR=30 (single-voxel curves) and without the addition of noise to detect potential biases. In order to provide an in-vivo proof of concept, four subjects without known cardiac disease and one patient with an LAD main branch chronic total occlusion (CTO) were scanned on a 3T PET/MRI scanner (Biograph mMR, Siemens, Erlangen). MRI imaging was performed using a 2D SR-FLASH sequence as described previously.2 The CTO patient received a simultaneous 18F-FDG PET scan to test for myocardial viability. Assessing the argument of post-processing simplicity, the motion corrected in-vivo data were evaluated once with surface coil intensity correction (SCIC) and full nonlinearity correction based on T1-mapping1 and once without any additional post processing.

Results

Figures 2 and 3 visualize the simulation results. It can be seen that TTP analysis is not able to distinguish between delay and dispersion, introducing a bias proportional to the simulated dispersion factor. BAT estimates from the proposed Fourier technique remain virtually unaffected by dispersion (Fig. 2). The standard deviations (SD) from all 100 simulation runs shown in Fig. 3 underscore the vast superiority in robustness of BAT measurements to TTP analysis at both noise levels. The clinical results from the control group show the regionally homogeneous distribution of arrival times despite occasionally higher BAT estimates in the inferior and lateral territories, leading to slightly higher averages there (Fig. 4a). The CTO patient presented with highly inhomogeneous BATs, 18F-FDG PET however confirmed metabolic integrity of all regions despite a small inferior deficit (Fig. 5a). Invasive angiography (Fig. 5d) confirmed collateralization of the septum, bypassing an occluded LAD main branch (long coronary path length for septum) despite a previously reopened LAD diagonal branch (short path length for anterior wall). Fig. 5 also shows the independence of the proposed method from SCIC and saturation correction (c/f), whereas the distribution of quantitative flow estimates greatly depended on these additional post-processing steps (b/e).

Conclusion

A new method has been proposed to extract quantitative information from the phase spectra of DCE-MRI data with minimal additional post-processing. So attained estimates of the BAT are able to accurately and robustly characterize path lengths within the coronary vasculature and represent a quantitative and robust DCE-MRI counterpart to tissue characterization using PET in the context of quantitative multimodality imaging. It is expected that more accurate assessment of the BAT will also benefit model-constrained deconvolution analysis of MRI perfusion data.

Acknowledgements

Funding was provided by DFG Grant 8810001759.

References

1. Broadbent DA, Biglands JD, Ripley DP, et al. Sensitivity of quantitative myocardial contrast-enhanced MRI to saturation pulse efficiency, noise and T1 measurement error: Comparison of nonlinearity correction methods. Magn Reson Med. 2016;75:1290-1300.

2. Kunze KP, Rischpler C, Hayes C, et al. Measurement of extracellular volume and transit time heterogeneity using contrast-enhanced myocardial perfusion MRI in patients after acute myocardial infarction. Magn Reson Med. in press DOI:10.1002/mrm.26320.

Figures

Calculation of the bolus arrival time from a low-frequency phase reconstruction of the response function R(t). The upper panel shows the measured and smoothed AIF, the simulated tissue curves at both noise levels and an example for the variability of time-to peak estimates in the presence of noise. The bottom panel visualizes the appearance and analysis of the low-frequency phase reconstruction (“R(t)-phases”) and its stability to different noise levels.

Noise-free simulation results visualizing the inability of the TTP analysis to discriminate between bolus delay and dispersion. Both plots (a/b) show simulation results without dispersion in the lower panel, and with dispersion in the upper one, using a standard deviation (SD) for the γ-variate kernel of 1/3 of the respective simulated BAT. While the average difference between simulated and estimated BAT is vanishing irrespective of concomitant dispersion (a), results of the TTP analysis are clearly dependent on both dispersion and delay (b).

Summary of the simulation results with respect to precision at noise levels of SNR=40 (a/c) and SNR=30 (b/d). All color-coded values as well the numbers on the right represent the standard deviation of BAT (a/b) and TTP (c/d) estimates across all 100 simulation runs. Irrespective of dispersion and noise level, the proposed method for BAT estimation outperformed TTP analysis by almost an order of magnitude with respect to robustness.

Results from the analysis of the clinical data, showing sector-wise BAT results from the control group (a) and the CTO patient (c) as well as the six regional tissue curves for one example of the control group (b) and the patient (d). The color-coded segmentation in (a) represents average BATs from all four controls for each respective segment, the range of values was 2.53 s (Anterior) to 3.55 s (Inferior). BAT estimates for the patient ranged from 2.0 s (Anterolateral) to 5.0 s (Inferoseptal).

Results of the 18F-FDG PET scan (a) confirming metabolic integrity of the collateralized septal territories with the exception of a small inferior region, also exhibiting a small perfusion defect (b). Panels on the right show results of the DCE-MRI analysis with (b/c) and without (e/f) SCIC and saturation correction. While absolute values for Fp in (e) are globally overestimated due to the saturated AIF and exhibit a strong septal-lateral gradient due to the missing SCIC, BAT results, depending only on the low-frequency phase information, are unaffected (c/f). Angiography results (d) show the septal collateralization.

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