Magnetic Resonance Fingerprinting with short relaxation intervals.

PURPOSE: The aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions. MATERIAL AND METHODS: A calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T1 and T2 values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated. RESULTS: The proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T1 values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T2 values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps. CONCLUSIONS: Reducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially resolved MRF.

Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data.

An iterative reconstruction method for undersampled magnetic resonance fingerprinting data is presented. The method performs the reconstruction entirely in k-space and is related to low rank matrix completion methods. A low dimensional data subspace is estimated from a small number of k-space locations fully sampled in the temporal direction and used to reconstruct the missing k-space samples before MRF dictionary matching. Performing the iterations in k-space eliminates the need for applying a forward and an inverse Fourier transform in each iteration required in previously proposed iterative reconstruction methods for undersampled MRF data. A projection onto the low dimensional data subspace is performed as a matrix multiplication instead of a singular value thresholding typically used in low rank matrix completion, further reducing the computational complexity of the reconstruction. The method is theoretically described and validated in phantom and in-vivo experiments. The quality of the parameter maps can be significantly improved compared to direct matching on undersampled data.

Resting myocardial blood flow quantification using contrast-enhanced magnetic resonance imaging in the presence of stenosis: A computational fluid dynamics study.

PURPOSE: The extent to which atherosclerotic plaques affect contrast agent (CA) transport in the coronary arteries and, hence, quantification of myocardial blood flow (MBF) using magnetic resonance imaging (MRI) is unclear. The purpose of this work was to evaluate the influence of plaque induced stenosis both on CA transport and on the accuracy of MBF quantification. METHODS: Computational fluid dynamics simulations in a high-detailed realistic vascular model were employed to investigate CA bolus transport in the coronary arteries. The impact of atherosclerosis was analyzed by inserting various medium- to high-grade stenoses in the vascular model. The influence of stenosis morphology was examined by varying the stenosis shapes but keeping the area reduction constant. Errors due to CA bolus transport were analyzed using the tracer-kinetic model MMID4. RESULTS: Dispersion of the CA bolus was found in all models and for all outlets, but with a varying magnitude. The impact of stenosis was complex: while high-grade stenoses amplified dispersion, mild stenoses reduced the effect. Morphology was found to have a marked influence on dispersion for a small number of outlets in the post-stenotic region. Despite this marked influence on the concentration-time curves, MBF errors were less affected by stenosis. In total, MBF was underestimated by -7.9% to -44.9%. CONCLUSIONS: The presented results reveal that local hemodynamics in the coronary vasculature appears to have a direct impact on CA bolus dispersion. Inclusion of atherosclerotic plaques resulted in a complex alteration of this effect, with both degree of area reduction and stenosis morphology affecting the amount of dispersion. This strong influence of vascular transport effects impairs the accuracy of MRI-based MBF quantification techniques and, potentially, other bolus-based perfusion measurement techniques like computed tomography perfusion imaging.

Contrast agent bolus dispersion in a realistic coronary artery geometry: influence of outlet boundary conditions.

Myocardial blood flow (MBF) quantification using contrast-enhanced first-pass magnetic resonance imaging relies on the precise knowledge of the arterial input function (AIF). Due to vascular transport processes, however, the shape of the AIF may change from the left ventricle where the AIF is measured to the myocardium. We employed computational fluid dynamics simulations in a realistic model of the left circumflex artery to investigate the degree to which this effect corrupts MBF quantification. Different outlet boundary conditions were applied to examine their influence on the solution. Our results indicate that vascular transport processes in realistic coronary artery geometries give rise to non-negligible systematic errors in the MBF values. The magnitude of these errors differs considerably between the outlets of the 3D model. Moreover, outlet boundary conditions are shown to have a significant influence on the outflows at the outlets of the 3D model. In particular, the employed boundary conditions respond differently to an artificially inserted stenosis and to hyperemia condition. Finally, outlet boundary conditions are shown to have an influence on the resulting MBF value. Since MBF errors are different under rest and under hyperemia conditions, overestimation of myocardial perfusion reserve values may occur as well.

Toward online reconstruction of quantitative susceptibility maps: superfast dipole inversion.

Magnetic susceptibility is an intrinsic tissue property that recently became measureable in vivo by a magnetic-resonance based technique called quantitative susceptibility mapping (QSM). Although QSM may be performed without additional acquisition time, for example, in the course of the well-established susceptibility weighted imaging, the applicability of QSM is currently hampered by the numerical complexity and computational cost associated with the reconstruction procedure. This work introduces a novel QSM framework called superfast dipole inversion which allows rapid online reconstruction of susceptibility maps from wrapped raw gradient-echo phase data. The algorithm relies on the extension and combination of several recent algorithms involving the precalculation of convolution kernels and the correction of inversion artifacts. Reconstruction of three-dimensional high resolution susceptibility maps of the human brain was achieved with superfast dipole inversion in less than 20 s on a conventional workstation computer. Thus, superfast dipole inversion opens the door to an implementation of QSM on MR scanner hardware as well as to the routine reconstruction of large cohorts of datasets.

Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain.

Quantitative susceptibility mapping (QSM) is a novel magnetic resonance-based technique that determines tissue magnetic susceptibility from measurements of the magnetic field perturbation. Due to the ill-posed nature of this problem, regularization strategies are generally required to reduce streaking artifacts on the computed maps. The present study introduces a new algorithm for calculating the susceptibility distribution utilizing a priori information on its regional homogeneity derived from gradient echo phase images and analyzes the impact of erroneous a priori information on susceptibility map fidelity. The algorithm, Homogeneity Enabled Incremental Dipole Inversion (HEIDI), was investigated with a special focus on the reconstruction of subtle susceptibility variations in a numerical model and in volunteer data and was compared with two recently published approaches, Thresholded K-space Division (TKD) and Morphology Enabled Dipole Inversion (MEDI). HEIDI resulted in susceptibility maps without streaking artifacts and excellent depiction of subtle susceptibility variations in most regions. By investigating HEIDI susceptibility maps acquired with the volunteers' heads in different orientations, it was demonstrated that the apparent magnetic susceptibility distribution of human brain tissue considerably depends on the direction of the main magnetic field.

Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study.

Quantitative susceptibility mapping (QSM) is a novel technique which allows determining the bulk magnetic susceptibility distribution of tissue in vivo from gradient echo magnetic resonance phase images. It is commonly assumed that paramagnetic iron is the predominant source of susceptibility variations in gray matter as many studies have reported a reasonable correlation of magnetic susceptibility with brain iron concentrations in vivo. Instead of performing direct comparisons, however, all these studies used the putative iron concentrations reported in the hallmark study by Hallgren and Sourander (1958) for their analysis. Consequently, the extent to which QSM can serve to reliably assess brain iron levels is not yet fully clear. To provide such information we investigated the relation between bulk tissue magnetic susceptibility and brain iron concentration in unfixed (in situ) post mortem brains of 13 subjects using MRI and inductively coupled plasma mass spectrometry. A strong linear correlation between chemically determined iron concentration and bulk magnetic susceptibility was found in gray matter structures (r=0.84, p<0.001), whereas the correlation coefficient was much lower in white matter (r=0.27, p<0.001). The slope of the overall linear correlation was consistent with theoretical considerations of the magnetism of ferritin supporting that most of the iron in the brain is bound to ferritin proteins. In conclusion, iron is the dominant source of magnetic susceptibility in deep gray matter and can be assessed with QSM. In white matter regions the estimation of iron concentrations by QSM is less accurate and more complex because the counteracting contribution from diamagnetic myelinated neuronal fibers confounds the interpretation.