A novel pulse sequence scheme is presented that allows the measurement and mapping of myocardial T₁ 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 T₁ using signal intensities from regions of interest and pixel by pixel. T₁ accuracy at different heart rates derived from simulated ECG signals was tested in phantoms. T₁ estimates showed small systematic error for T₁ values from 191 to 1196 ms. In vivo T₁ mapping was performed in two healthy volunteers and in one patient with acute myocardial infarction before and after administration of Gd‐DTPA. T₁ values for myocardium and noncardiac structures were in good agreement with values available from the literature. The region of infarction was clearly visualized. MOLLI provides high‐resolution T₁ maps of human myocardium in native and post‐contrast situations within a single breath‐hold. Magn Reson Med 52:141–146, 2004.

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.

After a reperfused myocardial infarction (MI), dynamic tissue changes occur (edema, inflammation, microvascular obstruction, hemorrhage, cardiomyocyte necrosis, and ultimately replacement by fibrosis). The extension and magnitude of these changes contribute to long-term prognosis after MI. Cardiac magnetic resonance (CMR) is the gold-standard technique for noninvasive myocardial tissue characterization. CMR is also the preferred methodology for the identification of potential benefits associated with new cardioprotective strategies both in experimental and clinical trials. However, there is a wide heterogeneity in CMR methodologies used in experimental and clinical trials, including time of post-MI scan, acquisition protocols, and, more importantly, selection of endpoints. There is a need for standardization of these methodologies to improve the translation into a real clinical benefit. The main objective of this scientific expert panel consensus document is to provide recommendations for CMR endpoint selection in experimental and clinical trials based on pathophysiology and its association with hard outcomes.

The k‐t broad‐use linear acquisition speed‐up technique (BLAST) has become widespread for reducing image acquisition time in dynamic MRI. In its basic form k‐t BLAST speeds up the data acquisition by undersampling k‐space over time (referred to as k‐t space). The resulting aliasing is resolved in the Fourier reciprocal x‐f space (x = spatial position, f = temporal frequency) using an adaptive filter derived from a low‐resolution estimate of the signal covariance. However, this filtering process tends to increase the reconstruction error or lower the achievable acceleration factor. This is problematic in applications exhibiting a broad range of temporal frequencies such as free‐breathing myocardial perfusion imaging. We show that temporal basis functions calculated by subjecting the training data to principal component analysis (PCA) can be used to constrain the reconstruction such that the temporal resolution is improved. The presented method is called k‐t PCA. Magn Reson Med, 2009.