Tissue characterization: T1, t2 andt2* techniques

Noninvasive characterization of tissue has long been the unique domain of magnetic resonance imaging (MRI) when compared to other imaging modalities. Techniques for such typically emphasize one or more MR-based relaxation parameters and the corresponding image contrast or weighting. With or without administration of an exogenous contrast agent, cardiac MRI affords detailed myocardial tissue characterization via various segmented as well as single heart beat approaches. The workhorse technique for myocardial characterization has been late gadolinium enhancement (LGE); LGE is routinely performed in MRI centers around the world as an integral part of nearly every cardiac MRI exam. While originally developed to characterize infarct scar, LGE has since become an important technique to delineate other features of myocardial disease such as fibrosis in nonischemic cardiomyopathy and infiltrates such as sarcoid granuloma and amyloid protein. LGE usually provides robust myocardial characterization, but has two major limitations. First, it requires administration of gadolinium-based contrast, which may not be suitable for individuals with known allergy to such agents or patients with advanced kidney disease. Second, it may be insensitive to more diffusely diseased myocardium where one loses the ability to “null” normal tissue via this technique's key inversion time parameter. To overcome these limitations, as well as to characterize other myocardial features, imaging techniques that capture intrinsic contrast in T1, T2 and other MR-based relaxation parameters are often incorporated into the cardiac MRI examination. Accruing evidence suggests that quantitative approaches, also known as tissue mapping techniques, are helping to further advance MR-based myocardial characterization. © Springer International Publishing Switzerland 2015

Parallel simulations for QUAntifying RElaxation magnetic resonance constants (SQUAREMR): An example towards accurate MOLLI T1 measurements

Background: T1 mapping is widely used today in CMR, however, it underestimates true T1 values and its measurement error is influenced by several acquisition parameters. The purpose of this study was the extraction of accurate T1 data through the utilization of comprehensive, parallel Simulations for QUAntifying RElaxation Magnetic Resonance constants (SQUAREMR) of the MOLLI pulse sequence on a large population of spins with physiologically relevant tissue relaxation constants. Methods: A CMR protocol consisting of different MOLLI schemes was performed on phantoms and healthy human volunteers. For every MOLLI experiment, the identical pulse sequence was simulated for a large range of physiological combinations of relaxation constants, resulting in a database of all possible outcomes. The unknown relaxation constants were then determined by finding the simulated signals in the database that produced the least squared difference to the measured signal intensities. Results: SQUAREMR demonstrated improvement of accuracy in phantom studies and consistent mean T1 values and consistent variance across the different MOLLI schemes in humans. This was true even for tissues with long T1s and MOLLI schemes with no pause between modified-Look-Locker experiments. Conclusions: SQUAREMR enables quantification of T1 data obtained by existing clinical pulse sequences. SQUAREMR allows for correction of quantitative CMR data that have already been acquired whereas it is expected that SQUAREMR may improve data consistency and advance quantitative MR across imaging centers, vendors and experimental configurations. While this study is focused on a MOLLI-based T1-mapping technique, it could however be extended in other types of quantitative MRI throughout the body. © 2015 Xanthis et al

Validation of a new t2∗ algorithm and its uncertainty value for cardiac and liver iron load determination from MRI magnitude images

Purpose To validate an automatic algorithm for offline T2∗ measurements, providing robust, vendor-independent T2∗, and uncertainty estimates for iron load quantification in the heart and liver using clinically available imaging sequences. Methods A T2∗ region of interest (ROI)-based algorithm was developed for robustness in an offline setting. Phantom imaging was performed on a 1.5 Tesla system, with clinically available multiecho gradient-recalled-echo (GRE) sequences for cardiac and liver imaging. A T2∗ single-echo GRE sequence was used as reference. Simulations were performed to assess accuracy and precision from 2000 measurements. Inter- and intraobserver variability was obtained in a patient study (n = 23). Results Simulations: Accuracy, in terms of the mean differences between the proposed method and true T2∗ ranged from 0-0.73 ms. Precision, in terms of confidence intervals of repeated measurements, was 0.06-4.74 ms showing agreement between the proposed uncertainty estimate and simulations. Phantom study: Bias and variability were 0.26 ± 4.23 ms (cardiac sequence) and -0.23 ± 1.69 ms (liver sequence). Patient study: Intraobserver variability was similar for experienced and inexperienced observers (0.03 ± 1.44 ms versus 0.16 ± 2.33 ms). Interobserver variability was 1.0 ± 3.77 ms for the heart and -0.52 ± 2.75 ms for the liver. Conclusion The proposed algorithm was shown to provide robust T2∗ measurements and uncertainty estimates over the range of clinically relevant T2∗ values. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

Decreased atrioventricular plane displacement after acute myocardial infarction yields a concomitant decrease in stroke volume

Acute myocardial infarction (AMI) can progress to heart failure, which has a poor prognosis. Normally, 60% of stroke volume (SV) is attributed to the longitudinal ventricular shortening and lengthening evident in the atrioventricular plane displacement (AVPD) during the cardiac cycle, but there is no information on how the relationship changes between SV and AVPD before and after AMI. Therefore, the aim of this study was to determine how SV depends on AVPD before and after AMI in two swine models. Serial cardiac magnetic resonance imaging was carried out before and 1-2 h after AMI in a microembolization model (n = 12) and an ischemia-reperfusion model (n = 14). A subset of pigs (n = 7) were additionally imaged at 24 h and at 7 days. Cine and late gadolinium enhancement images were analyzed for cardiac function, AVPD measurements and infarct size estimation, respectively. AVPD decreased (P < 0.05) in all myocardial regions after AMI, with a concomitant SV decrease (P < 0.001). The ischemia-reperfusion model affected SV to a higher degree and had a larger AVPD decrease than the microembolization model (-29 ± 14% vs. -15 ± 18%; P < 0.05). Wall thickening decreased in infarcted areas (P < 0.001), and A-wave AVPD remained unchanged (P = 0.93) whereas E-wave AVPD decreased (P < 0.001) after AMI. We conclude that AVPD is coupled to SV independent of infarct type but likely to a greater degree in ischemia-reperfusion infarcts compared with microembolization infarcts. AMI reduces diastolic early filling AVPD but not AVPD from atrial contraction. These findings shed light on the physiological significance of atrioventricular plane motion when assessing acute and subacute myocardial infarction.NEW & NOTEWORTHY The link between cardiac longitudinal motion, measured as atrioventricular plane displacement (AVPD), and stroke volume (SV) is investigated in swine after acute myocardial infarction (AMI). This cardiac magnetic resonance study demonstrates a close coupling between AVPD and SV before and after AMI in an experimental setting and demonstrates that this connection is present in ischemia-reperfusion and microembolization infarcts, acutely and during the first week. Furthermore, AVPD is equally and persistently depressed in infarcted and remote myocardium after AMI

A new validated T2* analysis method with certainty estimates for cardiac and liver iron load determination

Journal of Cardiovascular Magnetic Resonance 2015, 17:P52. doi:10.1186/1532-429X-17-S1-P5