Fast MR parameter mapping using k‐t principal component analysis

Quantification of magnetic resonance parameters plays an increasingly important role in clinical applications, such as the detection and classification of neurodegenerative diseases. The major obstacle that remains for its widespread use in clinical routine is the long scanning times. Therefore, strategies that allow for significant decreases in scan time are highly desired. Recently, the k‐t principal component analysis method was introduced for dynamic cardiac imaging to accelerate data acquisition. This is done by undersampling k‐t space and constraining the reconstruction of the aliased data based on the k‐t Broad‐use Linear Acquisition Speed‐up Technique (BLAST) concept and predetermined temporal basis functions. The objective of this study was to investigate whether the k‐t principal component analysis concept can be adapted to parameter quantification, specifically allowing for significant acceleration of an inversion recovery fast imaging with steady state precession (TrueFISP) acquisition. We found that three basis functions and a single training data line in central k‐space were sufficient to achieve up to an 8‐fold acceleration of the quantification measurement. This allows for an estimation of relaxation times T₁ and T₂ and spin density in one slice with sub‐millimeter in‐plane resolution, in only 6 s. Our findings demonstrate that the k‐t principal component analysis method is a potential candidate to bring the acquisition time for magnetic resonance parameter mapping to a clinically acceptable level. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

Accelerating MR parameter mapping using sparsity‐promoting regularization in parametric dimension

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several‐fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which uses smoothness of signal evolution in the parametric dimension within compressed sensing framework (p‐CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T₁ mapping and compared favorably to two representative reconstruction approaches, image space‐based total variation regularization and an analytical model‐based reconstruction. The proposed p‐CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model‐based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist. Magn Reson Med 70:1263–1273, 2013. © 2012 Wiley Periodicals, Inc.     

Diffusion‐weighted radial fast spin‐echo for high‐resolution diffusion tensor imaging at 3T

There is a need for an imaging sequence that can provide high‐resolution diffusion tensor images at 3T near air–tissue interfaces. By employing a radial fast spin‐echo (FSE) collection in conjunction with magnitude filtered back‐projection reconstruction, high‐resolution diffusion‐weighted images can be produced without susceptibility artifacts. However, violation of the Carr‐Purcell‐Meiboom‐Gill (CPMG) condition of diffusion prepared magnetization is a prominent problem for FSE trains that is magnified at higher fields. The unique aspect of violating the CPMG condition in trajectories that oversample the center of k‐space and the implications for choosing the solution are examined. For collecting diffusion‐weighted radial‐FSE data at 3T we propose mixed‐CPMG phase cycling of RF refocusing pulses combined with a 300% wider refocusing than excitation slice. It is shown that this approach produces accurate diffusion values in a phantom, and can be used to collect undistorted, high‐resolution diffusion tensor images of the human brain. Magn Reson Med 60:270–276, 2008. © 2008 Wiley‐Liss, Inc.     

Just how much data need to be collected for reliable bootstrap DT‐MRI?

Diffusion tensor MRI (DT-MRI) can provide estimates of fiber orientation derived from the orientational dependence of the diffusivity of water molecules, enabling the reconstruction of white matter fiber pathways using tractography methods. However, noise arising from various sources can introduce uncertainty into the estimates of the elements of the diffusion tensor, resulting in errors in fiber orientation estimates such that tractography reconstructions of fiber pathways potentially can be imprecise and inaccurate. Recently, attempts have been made to characterize the uncertainty in DT-MRI-derived parameters using the bootstrap method; however, several questions remain open regarding the number of repeat measurements and bootstraps required to accurately and precisely reconstruct the probability distributions of the DT-MRI parameters. This study investigates the accuracy and precision of the bootstrap method for characterizing distributions of DT-MRI parameters. A number of experimental bootstrap designs and sampling schemes containing different numbers of isotropically distributed gradient vectors are considered, using an idealized system where the true variability in each parameter is known. This study demonstrates that for most DT-MRI experiments, robust results will be obtained if the minimum number of bootstraps is approximately 500, and that at least five repeat samples of each diffusion-weighted intensity should be used for bootstrapping.     

Breathhold multiecho fast spin‐echo pulse sequence for accurate R2 measurement in the heart and liver

Measurement of proton transverse relaxation rates (R₂) is a generally useful means for quantitative characterization of pathological changes in tissue with a variety of clinical applications. The most widely used R₂ measurement method is the Carr‐Purcell‐Meiboom‐Gill (CPMG) pulse sequence but its relatively long scan time requires respiratory gating for chest or body MRI, rendering this approach impractical for comprehensive assessment within a clinically‐acceptable examination time. The purpose of our study was to develop a breathhold multiecho fast spin‐echo (FSE) sequence for accurate measurement of R₂ in the liver and heart. Phantom experiments and studies of subjects in vivo were performed to compare the FSE data with the corresponding even‐echo CPMG data. For pooled data, the R₂ measurements were strongly correlated (Pearson correlation coefficient = 0.99) and in excellent agreement (mean difference [CPMG – FSE] = 0.10 s^–1; 95% limits of agreement were 1.98 and –1.78 s^–1) between the two pulse sequences. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.     

Comparison of myocardial perfusion estimates from dynamic contrast‐enhanced magnetic resonance imaging with four quantitative analysis methods

Dynamic contrast‐enhanced MRI has been used to quantify myocardial perfusion in recent years. Published results have varied widely, possibly depending on the method used to analyze the dynamic perfusion data. Here, four quantitative analysis methods (two‐compartment modeling, Fermi function modeling, model‐independent analysis, and Patlak plot analysis) were implemented and compared for quantifying myocardial perfusion. Dynamic contrast‐enhanced MRI data were acquired in 20 human subjects at rest with low‐dose (0.019 ± 0.005 mmol/kg) bolus injections of gadolinium. Fourteen of these subjects were also imaged at adenosine stress (0.021 ± 0.005 mmol/kg). Aggregate rest perfusion estimates were not significantly different between all four analysis methods. At stress, perfusion estimates were not significantly different between two‐compartment modeling, model‐independent analysis, and Patlak plot analysis. Stress estimates from the Fermi model were significantly higher (～20%) than the other three methods. Myocardial perfusion reserve values were not significantly different between all four methods. Model‐independent analysis resulted in the lowest model curve‐fit errors. When more than just the first pass of data was analyzed, perfusion estimates from two‐compartment modeling and model‐independent analysis did not change significantly, unlike results from Fermi function modeling. Magn Reson Med 64:125–137, 2010. © 2010 Wiley‐Liss, Inc.