In vivo cardiac diffusion-weighted magnetic resonance imaging: quantification of normal perfusion and diffusion coefficients with intravoxel incoherent motion imaging.

OBJECTIVES

Diffusion-weighted imaging (DWI) and the introduction of the intravoxel incoherent motion (IVIM) model have provided a unique method for evaluating perfusion and diffusion within a tissue without the need for a contrast agent. Despite its relevance, cardiac DWI has thus far been limited by low b values because of signal loss induced by physiological motion. The goal of this study was to develop a methodology for estimating IVIM parameters of in vivo cardiac magnetic resonance imaging using an efficient DWI acquisition framework. This was achieved by investigating various acquisition strategies (principal component analysis [PCA] filtering and temporal maximum intensity projection [PCATMIP] and single trigger delay [TD]) and fitting methods.

MATERIAL AND

METHODS

Simulations were performed on a synthetic dataset of diffusion-weighted signal intensity (SI) to determine the fitting method that would yield IVIM parameters with the greatest accuracy. The required number of b values to correctly estimate IVIM parameters was also investigated. Breath-hold DWI scans were performed for 12 volunteers to collect several TD values during diastole. Thirteen b values ranging from 0 to 550 s/mm were used. The IVIM parameters derived using the data from all the acquired TDs (PCATMIP technique) were compared with those derived using a single acquisition performed at an optimized diastolic time point (1TD).

RESULTS

The main result of this study was that PCATMIP, when combined with a fitting model that accounted for T1 and T2 relaxation, provided IVIM parameters with less variability. However, an acquisition performed with 1 optimized diastolic TD provided results that were as good as those provided using PCATMIP if the R-R variability during the acquisition was sufficiently low (± 5%). Furthermore, the use of only 9 b values (that could be acquired in 2 breath-holds), instead of 13 b values (requiring 3 breath-holds), was sufficient to determine the IVIM parameters.

CONCLUSIONS

This study demonstrates that IVIM is technically feasible in vivo and reports for the first time the perfusion fraction, f, and the diffusion coefficients, D and D*, for the cardiac DWI of healthy volunteers. Motion-induced signal loss, which is the main problem associated with cardiac DWI, could be avoided with the combined use of sliding acquisition during the cardiac cycle and image postprocessing with the PCATMIP algorithm. This study provides new perspectives for perfusion imaging without a contrast agent and demonstrates that IVIM parameters can act as promising tools to further characterize microvascular abnormalities or dysfunction.