Search

Purpose: A segmented inversion-recovery module combined with the 2D ultrashort echo time radial technique is proposed that allows accurate pixel level T-1 mapping of mouse lung in vivo. Methods: Numerical simulations were performed to estimate T-1 measurement accuracy and precision versus flip angle and signal-to-noise ratio. Phantom measurements were used for protocol validation, where the segmented inversion-recovery ultrashort echo-time sequence was compared with the reference technique (inversion-recovery rapid acquisition with refocused echoes). The in vivo experiments were carried out on free-breathing C57 mice (n = 10), breathing first air and then oxygen. Results: The simulations demonstrated the high potential of the technique for accurate and precise T-1 assessment. Phantom experiments showed good agreement for T-1 values measured with segmented inversion-recovery ultrashort echo-time and the reference technique. The in vivo experiment demonstrated the utility of the technique in oxygen-enhanced assessment, where small T-1 changes were detected with high precision. Conclusion: Segmented inversion-recovery ultrashort echo-time provides accurate, high resolution T-1 mapping of the lung parenchyma. Magn Reson Med 71:2180-2185, 2014. (c) 2013 Wiley Periodicals, Inc.

Tissue microcirculation can be quantified by a deconvolution analysis of concentration–time curves measured by dynamic contrast-enhanced magnetic resonance imaging. However, deconvolution is an ill-posed problem, which requires regularization of the solutions. In this work, four algebraic deconvolution/regularization methods were evaluated: truncated singular value decomposition and generalized Tikhonov regularization (GTR) in combination with the L-curve criterion, a modified LCC (GTR-MLCC), and a response function model that takes a-priori knowledge into account. To this end, dynamic contrast-enhanced magnetic resonance imaging data sets were simulated by an established physiologically reference model for different signal-to-noise ratios and measured on a 1.5-T system in the lung of 10 healthy volunteers and 20 patients. Analysis of both the simulated and measured dynamic contrast-enhanced magnetic resonance imaging datasets revealed that GTR in combination with the L-curve criterion does not yield reliable and clinically useful results. The three other deconvolution/regularization algorithms resulted in almost identical microcirculatory parameter estimates for signal-to-noise ratios > 10. At low signal-to-noise ratios levels (

A segmented inversion-recovery module combined with the 2D ultrashort echo time radial technique is proposed that allows accurate pixel level T(1) mapping of mouse lung in vivo.Numerical simulations were performed to estimate T(1) measurement accuracy and precision versus flip angle and signal-to-noise ratio. Phantom measurements were used for protocol validation, where the segmented inversion-recovery ultrashort echo-time sequence was compared with the reference technique (inversion-recovery rapid acquisition with refocused echoes). The in vivo experiments were carried out on free-breathing C57 mice (n = 10), breathing first air and then oxygen.The simulations demonstrated the high potential of the technique for accurate and precise T(1) assessment. Phantom experiments showed good agreement for T(1) values measured with segmented inversion-recovery ultrashort echo-time and the reference technique. The in vivo experiment demonstrated the utility of the technique in oxygen-enhanced assessment, where small T(1) changes were detected with high precision.Segmented inversion-recovery ultrashort echo-time provides accurate, high resolution T(1) mapping of the lung parenchyma.

Tissue microcirculation can be quantified by a deconvolution analysis of concentration-time curves measured by dynamic contrast-enhanced magnetic resonance imaging. However, deconvolution is an ill-posed problem, which requires regularization of the solutions. In this work, four algebraic deconvolution/regularization methods were evaluated: truncated singular value decomposition and generalized Tikhonov regularization (GTR) in combination with the L-curve criterion, a modified LCC (GTR-MLCC), and a response function model that takes a-priori knowledge into account. To this end, dynamic contrast-enhanced magnetic resonance imaging data sets were simulated by an established physiologically reference model for different signal-to-noise ratios and measured on a 1.5-T system in the lung of 10 healthy volunteers and 20 patients. Analysis of both the simulated and measured dynamic contrast-enhanced magnetic resonance imaging datasets revealed that GTR in combination with the L-curve criterion does not yield reliable and clinically useful results. The three other deconvolution/regularization algorithms resulted in almost identical microcirculatory parameter estimates for signal-to-noise ratios10. At low signal-to-noise ratios levels (10) typically occurring in pathological lung regions, GTR in combination with a modified L-curve criterion approximates the true response function much more accurately than truncated singular value decomposition and GTR in combination with response function model with a difference in accuracy of up to 76%. In conclusion, GTR in combination with a modified L-curve criterion is recommended for the deconvolution of dynamic contrast-enhanced magnetic resonance imaging curves measured in the lung parenchyma of patients with highly heterogeneous signal-to-noise ratios.