A novel pulse sequence scheme is presented that allows the measurement and mapping of myocardial T 1 in vivo on a 1.5 Tesla MR system within a single breath-hold. Two major modifications of conventional Look-Locker (LL) imaging are introduced: 1) selective data acquisition, and 2) merging of data from multiple LL experiments into one data set. Each modified LL inversion recovery (MOLLI) study consisted of three successive LL inversion recovery (IR) experiments with different inversion times. We acquired images in late diastole using a single-shot steady-state free-precession (SSFP) technique, combined with sensitivity encoding to achieve a data acquisition window of <200 ms duration. We calculated T 1 using signal intensities from regions of interest and pixel by pixel. T 1 accuracy at different heart rates derived from simulated ECG signals was tested in phantoms. Key words: spin-lattice relaxation time; Look-Locker; heart; myocardium Despite recent technological advances, in vivo T 1 quantification of the myocardium with modern magnetic resonance (MR) systems remains a challenge because of severe time constraints due to cardiac and respiratory motion. While myocardial T 1 is shorter and therefore relatively easier to measure at low field strengths, it has a value of ϳ1000 ms at a field strength of 1.5 T, exceeding the duration of the cardiac cycle (ϳ600 -1200 ms) in most subjects (1,2). Since standard inversion recovery (IR) measurements require a relaxation period of four to five times T 1 to allow for full magnetization recovery after each 180°pulse (3), only four to five such single-point IR experiments can be performed within one breath-hold (ca. 20 s). To achieve accurate T 1 estimates from a three-parameter curve-fitting procedure, as is commonly employed, data from at least six to 10 time points should be available (4). The multipoint approach, as first described by Look and Locker (5), samples the relaxation curve multiple times after an initial preparation pulse (6). This technique has been shown theoretically to be highly efficient (7), and has been widely used for T 1 measurements of the brain (8 -11). It is not suitable for pixel-by-pixel T 1 mapping of the heart because data acquisition is performed continuously throughout the cardiac cycle without regard for cardiac motion, which means that T 1 values can only be derived for regions of interest (ROIs) that must be defined manually for every frame (1). The resultant T 1 values may consequently be subject to inaccuracy caused by misregistration effects.In this work we present a pulse sequence scheme that allows for accurate in vivo T 1 measurements and T 1 mapping of myocardium with high spatial resolution and within a single breath-hold. To overcome the limitations of the conventional LL approach for cardiac applications, we propose a modified LL IR scheme (MOLLI), which introduces two principles to the standard LL sequence: 1) selective data acquisition at a given time of the cardiac cycle over successive heartbeats, and (2) merging of image sets...

Pulsatile blood flow through the cavities of the heart and great vessels is time-varying and multidirectional. Access to all regions, phases and directions of cardiovascular flows has formerly been limited. Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) has enabled more comprehensive access to such flows, with typical spatial resolution of 1.5×1.5×1.5 – 3×3×3 mm3, typical temporal resolution of 30–40 ms, and acquisition times in the order of 5 to 25 min. This consensus paper is the work of physicists, physicians and biomedical engineers, active in the development and implementation of 4D Flow CMR, who have repeatedly met to share experience and ideas. The paper aims to assist understanding of acquisition and analysis methods, and their potential clinical applications with a focus on the heart and greater vessels. We describe that 4D Flow CMR can be clinically advantageous because placement of a single acquisition volume is straightforward and enables flow through any plane across it to be calculated retrospectively and with good accuracy. We also specify research and development goals that have yet to be satisfactorily achieved. Derived flow parameters, generally needing further development or validation for clinical use, include measurements of wall shear stress, pressure difference, turbulent kinetic energy, and intracardiac flow components. The dependence of measurement accuracy on acquisition parameters is considered, as are the uses of different visualization strategies for appropriate representation of time-varying multidirectional flow fields. Finally, we offer suggestions for more consistent, user-friendly implementation of 4D Flow CMR acquisition and data handling with a view to multicenter studies and more widespread adoption of the approach in routine clinical investigations.

Recent theoretical advances in the field of compressive sampling-also referred to as compressed sensing (CS)-hold considerable promise for practical applications in MRI, but the fundamental condition of sparsity required in the CS framework is usually not fulfilled in MR images. However, in dynamic imaging, data sparsity can readily be introduced by applying the Fourier transformation along the temporal dimension assuming that only parts of the field-of-view (FOV) change at a high temporal rate while other parts remain stationary or change slowly. The second condition for CS, random sampling, can easily be realized by randomly skipping phase-encoding lines in each dynamic frame. In this work, the feasibility of the CS framework for accelerated dynamic MRI is assessed. Simulated datasets are used to compare the reconstruction results for different reduction factors, noise, and sparsity levels. In vivo cardiac cine data and Fourier-encoded velocity data of the carotid artery are used to test the reconstruction performance relative to k-t broad-use linear acquisition speed-up technique (k-t BLAST) reconstructions. Given sufficient data sparsity and base signal-to-noise ratio (SNR), CS is demonstrated to result in improved temporal fidelity compared to k-t BLAST reconstructions for the example data sets used in this work. Magn Reson Med 59:365-373, 2008.