T1 Mapping in Characterizing Myocardial Disease
A Comprehensive Review
Jump to
- Article
- Abstract
- Basic Concepts of T1 Mapping: From Acquisition to Postprocessing
- Normal Ranges
- T1-Mapping Indices: Initial Experience and Histological Correlation
- T1 Mapping in Myocardial Inflammation
- T1 Mapping in Nonischemic Dilative Cardiomyopathy
- T1 Mapping in Hypertrophic Phenotypes
- T1 Mapping in Chronic Ischemic Cardiomyopathy
- T1 Mapping in Acute Myocardial Ischemia and Infarction
- Limitation of State of Art and Avenues of Translation
- Conclusions
- Disclosures
- Footnotes
- References
- Figures & Tables
- Info & Metrics
- eLetters

Abstract
Cardiovascular magnetic resonance provides insights into myocardial structure and function noninvasively, with high diagnostic accuracy and without ionizing radiation. Myocardial tissue characterization in particular gives cardiovascular magnetic resonance a prime role among all the noninvasive cardiovascular investigations. Late gadolinium enhancement imaging is an established method for visualizing replacement scar, providing diagnostic and prognostic information in a variety of cardiac conditions. Late gadolinium enhancement, however, relies on the regional segregation of tissue characteristics to generate the imaging contrast. Thus, myocardial pathology that is diffuse in nature and affecting the myocardium in a rather uniform and global distribution is not well visualized with late gadolinium enhancement. Examples include diffuse myocardial inflammation, fibrosis, hypertrophy, and infiltration. T1 mapping is a novel technique allowing to diagnose these diffuse conditions by measurement of T1 values, which directly correspond to variation in intrinsic myocardial tissue properties. In addition to providing clinically meaningful indices, T1-mapping measurements also allow for an estimation of extracellular space by calculation of extracellular volume fraction. Multiple lines of evidence suggest a central role for T1 mapping in detection of diffuse myocardial disease in early disease stages and complements late gadolinium enhancement in visualization of the regional changes in common advanced myocardial disease. As a quantifiable measure, it may allow grading of disease activity, monitoring progress, and guiding treatment, potentially as a fast contrast-free clinical application. We present an overview of clinically relevant technical aspects of acquisition and processing, and the current state of art and evidence, supporting its clinical use.
Cardiovascular magnetic resonance (CMR) is unique in its ability to noninvasively provide insights into cardiac morphology, myocardial architecture, blood flow, tissue perfusion, and function with high diagnostic accuracy and without needing ionizing radiation. In addition, CMR is also well suited to inform on the presence of subclinical disease and to unravel the complex pathophysiology of various cardiovascular conditions in ways that are not currently possible with other techniques. CMR is thus gaining increasing use in an ever-expanding range of cardiac conditions to diagnose or exclude disease, stratify risk, and guide management. A full CMR examination consists of different protocols that can be performed in various combinations, each targeted to answer specific clinical questions during a single session.1,2 Faster acquisition protocols, increased access to CMR facilities, and greater availability of CMR imaging expertise now allow its routine use early in the diagnostic cascade. Equipment costs remain high but are commonly offset by earlier disease recognition, allowing for efficient disease management, targeted treatment, or rapid discharge after exclusion of disease.
Perhaps the most interesting CMR attribute—unique to CMR alone among all the noninvasive cardiovascular investigative modalities—is its ability to characterize myocardial tissue architecture in great detail. This has been traditionally done using late gadolinium enhancement (LGE) imaging, where contrast-enhanced macroscopic scar imaging with gadolinium contrast agents (GCAs) provides high-quality diagnostic and prognostic information on a variety of cardiac conditions.1,3 However, visualization of cardiac pathology with LGE relies on differential degree of spatial accumulation of GCAs to reveal black and white imaging contrast. LGE is thus only useful in cardiac conditions which have stark regional differences within the myocardium,4 exemplified by ischemic cardiomyopathy, where the postinfarction scar—histologically, replacement fibrosis—is sharply demarcated from the largely unaffected remote myocardium (Figures 1 and 2A). LGE can also be useful in some nonischemic cardiomyopathies (NICM), where areas of diseased tissue aggregate regionally (eg, in dilated cardiomyopathy [DCM] or hypertrophic cardiomyopathy [HCM], myocarditis, and sarcoidosis; Figure 2B–2E), generating characteristic patterns of nonischemic LGE that allow recognition of the underlying pathogenesis.1–8 However, there are multiple situations where LGE is not sufficiently sensitive to detect myocardial disease because it is not yet, or not at all, characterized by sufficient regional accumulation of reparative fibrosis.9–11 In NICMs, where LGE is a recognized marker of irreversible damage and advanced disease, early stages are characterized by a multitude of diffuse interstitial disease processes, including low-grade interstitial inflammation, fibrosis, and infiltration, resulting in an expansion of extracellular space. These processes that underpin the pathophysiology of intrinsic myocardial disease in NICM run a protracted subclinical course ahead of the clinically manifest stages of advanced disease (Figure 3) and are not reliably detected with LGE. Moreover, myocardial disease is often accompanied by systemic comorbidities or toxicities of concomitant medications that might limit use of GCAs. Diffusely diseased myocardium is thus often beyond the scope of characterization with LGE. On the contrary, because these diffuse interstitial disease processes have prognostic and probably therapeutic impact, there is an urgent need for newer methods that can directly identify and quantify them with precision. T1 mapping is one such novel emerging technique for quantitative tissue characterization because T1 values directly reflect intrinsic myocardial tissue properties in both health and disease. In this review, we provide a critical overview of the principles underlying T1 mapping, image acquisition, clinical evidence supporting its use, and the limitations of the present state of art and identify emerging trends for further clinical translation.
Representative images and concept of regional and diffuse myocardial disease. A, An example of postinfarction scar representing the regionally segregated tissue, which differentially accumulates gadolinium contrast agent, giving rise to the imaging contrast, which is detectable with late gadolinium enhancement (LGE). Arrows point to myocardial scar. B, An example of nonischemic cardiomyopathy, where myocardium is involved diffusely. Changes in diffusely diseased myocardium cannot be appreciated with LGE because they represent a continuum of disease without a normative reference within the imaging plane. C and D, A schematic representation of regional and diffuse myocardial involvement with a list of common respective causes.
Representative images of left ventricular (LV) end-diastolic cine (upper) and late gadolinium enhancement (LGE) imaging (lower). A, Ischemic cardiomyopathy with subendocardial LGE in the antero- and inferolateral wall. B, Idiopathic dilated cardiomyopathy with mid-myocardial striae of LGE. C, Acute myocarditis with subepicardial LGE in the infero-lateral wall. D, Cardiac sarcoid with multiple focal areas of LGE (2CH view, particularly, in the inferior wall and apex). E, Hypertrophic cardiomyopathy with diffuse and circumferential LGE. F, Cardiac amyloid with diffuse and subendocardial enhancement. Reproduced from Hinojar et al1 with permission of the publisher. Copyright ©2012, Future Medicine Ltd.
Understanding the differences between ischemic heart disease and nonischemic cardiomyopathy. In ischemic heart disease, myocardial injury occurs via atherothrombotic event, such as acute myocardial infarction, a symptomatic clinical event, characterized by central crushing chest pain, shortness of breath, and ischemic ECG changes. Postinfarction left ventricular (LV) remodeling is a result of an acute loss of myocardium, leading to an abrupt increase in loading conditions that induces a unique pattern of remodeling involving the infarcted border zone and remote noninfarcted myocardium.119,120 On the contrary, nonischemic ventricular remodeling is characterized by a protracted subclinical course ahead of the onset of symptoms in an advanced stage of disease and functional impairment. Typical triggers include genetic, systemic of external noci, which affect myocardium globally. The remodeling is underscored by the several complex interstitial processes which lead to extracellular matrix (ECM) remodeling, and intrinsic myocardial impairment.9 LGE indicates late gadolinium enhancement.
Basic Concepts of T1 Mapping: From Acquisition to Postprocessing
T1 mapping stands for registering the course of recovery of longitudinal magnetization.12 This process entails a prior preparation step with magnetization changing prepulses (for T1 mapping in a nutshell, please see Figure 4). Recovery of longitudinal magnetization follows an exponential course, and registration involves a curve-fitting process of the temporal change in magnitudes of longitudinal magnetization. The T1 value represents the time when recovery of magnetization has reached 63% of its original state. The rate of T1 recovery relates directly to the intrinsic myocardial tissue properties, which are variously altered in the presence of pathological tissue, resulting in different T1 values in health and disease. One major advantage for T1 mapping is the fact that T1 values represent the measurement in the myocardial voxel of interest; they do not need differential spatial GCA contrast between normal and abnormal tissues (like needed with LGE) or even presence of normal tissue for comparison. T1 mapping can be performed in native myocardium (native T1) or in the presence of gadolinium-contrast agents (postcontrast T1). Native T1 values increase in disease (with a few exceptions, where values decrease, such as cardiac iron or fat accumulation), whereas postcontrast T1 values get shorter. Native or postcontrast myocardial T1 measurements represent a wholesome measurement of intra and extracellular space combined. They also support calculation of extracellular volume fraction (ECV), which represents an estimation of extracellular space alone, by gauging the influence of accumulated GCA in the extracellular compartment on the postcontrast T1 measurement. A comprehensive overview of the indices is provided in Table 1.
Overview of the T1-Mapping Indices
T1 mapping in a nutshell. T1-mapping measurement is based on recovery of T1 magnetization (relaxation) after a prior preparation step with either saturation (90°) or inversion (180°) prepulse. This process is followed (mapped) by acquisition of images at several time points (indicated as crosses) during T1 recovery. Subsequent pixel-wise coregistration of images allows an exponential fit of values underlying the quantification of T1 relaxation. T1 value is the time when T1 recovery is 63% complete. Two main approaches for the preparation of magnetization include either inversion recovery (IR) or saturation recovery (SR) prepulses. Myocardial IR sequences are based on the Look-Locker (LL) principle using a nonselective 180° preparation prepulse, which inverts all available magnetization in z direction.121 LL sequences have been used for determination of inversion prepulse delays (inversion time−TI scout) for optimal nulling of the myocardium in late gadolinium enhancement (LGE) acquisitions for years. LL sequence is, however, acquired throughout the cardiac cycle, that is, not corrected for cardiac motion. The moving heart precludes pixel-wise coregistration of myocardial tissue in the single images, which is the basis of the curve-fitting procedure. The first T1-mapping sequence to support cardiac motion correction (by acquisition in the diastolic standstill) was the modified LL (MOLLI) sequence; it is acquired in a single breath-hold but at the expense of a reduced number of images to support curve fitting.18 Subsequent development saw numerous MOLLI variants with various aims, including achieving shorter breath-holds,24 covering the heart fully by free-breathing acquisition,122 or achieving greater T1 accuracy.64 An alternative acquisition approach uses 90° saturation preparation prepulses, allowing a much shorter period of T1 recovery (Saturation recovery single-shot acquisition [SASHA]).123,124 The most recently introduced approach is a combined IR–SR preparation scheme (Saturation pulse prepared heart rate–independent inversion recovery, SAPPHIRE).125 In T1 or longitudinal magnetization, the magnitude is measured as a vector component in z direction (head-feet), whereas in T2 (discussed later), the vector component is measured in transverse or x-y plane. T1 accuracy of a sequence is defined by proximity/distance of a sequence-specific T1 value (T1 estimate) from the true T1 value, obtained by T1-TSE sequence. T2 sensitivity is influenced via sequence readout parameters, such as wider flip angles (FA 50°>FA 35°) and effects of magnetization transfer of intracellular water on sequences. T1 accuracy inversely relates to precision, that is, reproducibility of measurements. MOLLI indicates modified Look-Locker imaging; and T1-TSE, T1 turbo spin-echo.
Myocardial T1 measurements have been reported using several different imaging acquisitions or sequences. These sequences differ considerably in terms of precision of measurements and their ability to detect myocardial disease.13–16 The gold standard T1 mapping is based on the acquisition of single images by a T1 turbo spin-echo sequence, which yields the true T1 values (Figure 4). Although this approach is considered the ultimate T1-mapping method (to track the effects of GCAs12), the need for many long breath-holds and an inherently time-consuming acquisition makes this approach unfit for clinical use. Novel imaging methods that capture the evolution of T1 recovery within a single breath-hold acquisition represent a true translational step change. These novel sequences use 2 main approaches for the preparation of magnetization, either inversion recovery or saturation recovery prepulses (Figure 4). A comprehensive comparative review of the technical details is provided in Higgins and Moon15 and McDiarmid et al17. Owing to continuous modification and optimization of the parameters, there are many variants of these 2 approaches, which are distinguished by different schemes of image acquisition (eg, number of prepulses/images/pauses) and readout parameters (flip angle, staggering time delays). There are further considerable differences in underlying scanner software supporting the execution of these sequences, which are vendor- and generation-specific, making it difficult to harmonize the values of sequences across the field.
For a clinical user of T1 mapping, a good grasp of the following mechanistic insights is important. First, in terms of acquisition, none of the reported cardiac T1-mapping sequences yields the true T1 values, but T1 estimates. A T1 estimate of a sequence will have a certain distance or proximity to the true T1 values, also known as the T1 accuracy13 (Figure 4). The term refers to technical characterization of a sequence by a direct comparison to T1 turbo spin-echo measurements in standardized phantoms. In the clinical environment, T1 accuracy serves as a measure of calibration and quality assurance, as well as supporting verification of transferred sequences between various scanners and centers. Second, T1-mapping sequences differ in picking up the influences of T2 relaxation (the effects of water) characterized by T2 sensitivity. It is increasingly understood that certain modified Look-Locker imaging variants are considerably influenced by the effects of myocardial water (eg, via wider excitation flip angles18,19), as well as the effect of magnetization transfer and fast water exchange of the unbound tissue water.16,20,21 This results in a higher and more homogeneous signal, supporting more precise measurements, especially in native T1 acquisition. T1 accuracy and T2 sensitivity seem to be inversely related. A high T2 sensitivity explains the ability of certain modified Look-Locker imaging sequences to detect clinically relevant myocardial alterations, such as edema or inflammation, without the need for GCA.22 Conversely, highly T1-accurate sequences optimized toward measuring T1 with minimal influence from T2 are well suited to measure the effects of GCAs in postcontrast T1 acquisitions and thus form the basis of ECV calculation. However, T1-accurate sequences tend to have a greater dispersion of measurements (less precision) and a poor discriminative value in native (noncontrast) acquisitions. These 2 effects have an utmost relevance for the clinical use of T1-mapping sequences because their diagnostic accuracy (discrimination between healthy and diseased myocardium) depends on the types of captured influences and the precision of measurements, defining the effect size in various conditions. In practical terms, for native T1 acquisitions, T2-sensitive sequences with a precise measurement yielding a large effect size are preferable. On the contrary, approximation of the effects of GCAs will be better characterized using highly T1-accurate sequences.
Third, in terms of postprocessing, the reported T1 values are derived by T1 measurement of a complete single short-axis slice (usually a midventricular slice) or by inclusion of septal segments only (Figure 5). Septal sampling has been shown to yield the greatest precision and minimizes the effect of considerable variations of regional T1 values because of artifact-prone left ventricular (LV) free wall myocardium.23–25 T1 values in septal segments are highly reproducible, whereas there is high dispersion of values in the lateral segments and thus better suited than averaging the complete short-axis slice to support discrimination between health and disease. The noise of the lateral segments included in short-axis measurement dilutes the physiological meaningful signal and prohibits the detection of interindividual differences, such as between health and mild disease, reducing its diagnostic and prognostic power11,23,26 (Figure 6). Of note, septal T1 values are between 50 and 100 ms higher compared with the lateral wall for native T1 acquisitions and mirrored by inverse effects in postcontrast acquisition.23,26–28 This also creates a difficulty in standardization of segmental values, which hinders accurate registration of regional heterogeneity using native T1 (within the range of noise), although larger differences can be detected, as also shown by visualization of ischemic myocardial scar (Figure 7). Inclusion of partial (voxel) volume, that is, signal from adjacent tissue, such as low signal from the lungs in the lateral wall or high signal from blood, will contaminate the myocardial measurements, explaining the preference for conservative intramyocardial placement of regions of interest.23,24
Postprocessing approaches in T1 mapping. Two main postprocessing approaches include by complete short axis (SAX) coverage (in green) and by conservative septal sampling (orange). The latter accounts for the greatest precision in the septal segments as well as for considerable variations of regional T1 values because of artifact-prone left ventricular (LV) free wall myocardium.23–25 T1 values in septal segments are highly reproducible compared with the dispersion of values in lateral segments (B). Also, septal T1 values are between 50 and 100 ms higher compared with the lateral wall for native T1 acquisitions and mirrored by inverse effects in postcontrast acquisition.23,26–28 MOLLI indicates modified Look-Locker imaging.
T1 mapping indices in health and disease. A schematic representation of tissue drivers of changes in T1 measurements. Lipid or iron accumulation (red dots) reduces native T1 values, irrespective of the T1 accuracy or T2 sensitivity of a given sequence. Accumulation of water (blue dots) leads to increase in native T1, which is more pronounced in T2-sensitive sequences. Similarly, scar tissue leads to increase in native T1. Conversely, accumulation of gadolinium contrast agents (GCAs) in extracellular space (green dots) leads to reduced postcontrast T1, which is stronger in a T1-accurate sequence. ECV indicates extracellular volume fraction.
T1 mapping in model diseases. Acute myocardial infarction (A), hypertrophic cardiomyopathy (B), and cardiac amyloidosis (C), using a MOLLI 3(3)3(3)5 FA 50° sequence at 3.0-T field strength, with respective normal values as reported in Dabir et al.26 Yellow arrows indicate areas of late gadolinium enhancement (LGE). Orange arrows indicate areas of microvascular obstruction in a course of an acute ischemic event.
Fourth, ECV, which was introduced to overcome the limitations of a postcontrast T1 value in indicating diffuse abnormalities, can also be affected by many variables. ECV calculation is reliant on the absence of motion between 2 separate acquisitions typically obtained at least 15 minutes apart.29 Sources of errors include motion (cardiac and respiratory motion and patient movements), arrhythmias, and so on, influencing the coregistration of individual images and goodness of fit of the exponential curve.30 The choice of field strength, type and dose of contrast agent, or injection scheme influence the resulting T1 estimates.26,27,31,32 Postcontrast measurements are additionally influenced by renal clearance and blood flow and need to be corrected for the blood T1, the latter being another important source of considerable variation. ECV is influenced by any error made in the measurement of any of its components (native myocardial and blood T1, postcontrast myocardial and blood T1, hematocrit) or differences between the pre- and postcontrast scans. In theory, the signal of the extracellular space primarily relates to the effects of GCAs on postcontrast T1 measurement. Yet, as a hybrid index, ECV also inherits the T2 influences of the native T1 acquisition. Conversely, even though the GCAs do not accumulate in intact myocardial cells, there is transfer of magnetization as well as transfer of magnetized water between the extra- and intracellular space.33 For the calculation of ECV, it has become practice to use different sequences before and after contrast injection to optimally capture the longer T1 before and the shorter T1 after contrast injection. The use of different sequences for native and postcontrast T1 acquisition, however, renders it debatable whether subtraction of the pre- from the postcontrast signal results in a true representation of the gadolinium effect.34–36 As the majority of these clinically relevant effects cannot be reproduced in phantoms, sequence characterization in human studies is an important part of validation. Taken together, the differences in the types of sequences, the postprocessing approaches, and indices are an important source of variation in the reported effect sizes between different studies and pose technical limitations for clinical application of T1 mapping. Even though these concepts are primarily important for those performing T1 mapping, it is critically important that all CMR imagers as well as clinicians wanting to use T1-mapping results understand the genesis behind these numbers. Strict standardization and quality control helps to recognize and control for many of these effects in routine clinical practice.
Normal Ranges
Each sequence requires determination of sequence-specific normal ranges; these are prerequisite for determining the dispersion of values per given sequences, which influence the diagnostic cut offs between health and disease. Every change of parameters and sequence optimization requires a similar process of validation. The results of studies reporting these in various sequences are summarized in Table 2. The true effects of age and sex remain unclear because studies reported discordant associations: T1 values were shown to decrease,37 increase,38,39 or have no relationship with age.26 Similarly, in some studies, females were shown to have higher T1 values,37,38 whereas others showed no relationship with sex.26 Interestingly, a subanalysis of the38cohort, which included only subjects with low cardiovascular risk, also revealed no age or sex association, in accordance with the findings by Dabir et al.26 Keeping in mind a mixed ability of sequences to phenotype health and disease, some of these findings may also indicate subclinical disease, rather than healthy aging. In the absence of vendor-led initiatives to establish normal ranges, the new starters may find it advantageous to transfer a well-characterized sequence from an experienced site and perform an adequate number of measurements in phantoms, in healthy volunteers, and in selected patient groups to verify the expected effect size.
Overview of Studies Reporting Normative Ranges for T1-Mapping Indices
T1-Mapping Indices: Initial Experience and Histological Correlation
Early studies with T1 mapping were performed in patients with ischemic cardiomyopathy and heart failure (HF). These studies revealed that T1 estimates in a chronic postinfarction scar are different compared with remote myocardium in patients, as well as healthy myocardium in controls.40,41 These studies raised hopes that quantitative myocardial tissue characterization may become the next method to evaluate the extent of myocardial scar and eventually replace the visualization with LGE. More importantly, they revealed abnormal T1 values in the remote, noninfarcted myocardium and a significant relationship with histologically determined collagen volume fraction, allowing an insight into the state of pathological remodeling in what was commonly considered as a normal reference. Later studies reiterated this relationship in patients with HF,42–45 severe aortic stenosis,46–50 and HCM46 (Table 3). Although these studies reported significant relationship with collagen volume fraction for all T1 indices, there is a considerable variation in the strengths of observed relationships. These differences are partially because of technical issues discussed earlier: T1-accurate sequences will show stronger relationship with postcontrast T1 or ECV, but not native T1, and vice versa for T2-sensitive sequences. In some studies, areas of replacement fibrosis (seen as LGE) were included in histological collagen volume fraction, showing stronger relationships,44–46,49 compared with those studies that focused primarily on the diffuse interstitial component.35,41,42,47,50 The majority of studies relied on minute amounts of tissue from endomyocardial biopsy; explanted hearts were analyzed in only 2 studies.44,51 Interestingly, the most recently reported association between CMR and histologically derived ECV (using a tissue-FAXS software analysis)35 yielded an association of r=0.493 (P=0.002), demonstrating the influence of technical and myocardial elements on the measurement (Table 2). Histological evidence on other tissue correlates remains scarce. Several proof-of-concept studies in model diseases of myocardial inflammation and infiltration, however, revealed that native T1-mapping indices are pathologically raised in overt disease, as well as in subclinical stages. The results of many studies are summarized in Table 4. Based on diverse T1-mapping methods, we calculated the Cohen’s d index, a measure of the effect size, to allow comparability across the studies.
Histological Correlations With T1-Mapping Indices in Various Cardiac Conditions
Proof-of-Concept Studies Using T1 Mapping in Health and Disease
T1 Mapping in Myocardial Inflammation
Myocardial inflammation is the most common pathway of myocardial injury in NICMs that in a minority of susceptible subjects leads to LV remodeling and HF.52,53 Although most often associated with a viral infection, inflammatory cardiomyopathy can be also a consequence of conditions with systemic inflammation, such as systemic lupus erythematous, rheumatoid arthritis, and systemic sarcoidosis, as well as chemotherapy. Diagnosis and therapy of myocardial inflammation remain unsolved clinical challenges. Endomyocardial biopsy is a proposed, albeit imperfect gold standard for the diagnosis of definitive myocarditis.52,54 Owing to the variable availability of endomyocardial biopsy expertise, its procedural risks, and a commonly low diagnostic yield, this diagnostic strategy is widely perceived as impractical in most cases of suspected myocarditis.55,56 In clinical practice, a noninvasive diagnostic pathway, which allows appreciation of disease activity/stage, may also support development of specific therapeutic options, both of which are currently limited.57 CMR is commonly used as a noninvasive diagnostic alternative because of its ability to detect edema and typical scar patterns. Currently, a combination of T2-weighted imaging and early enhancement ratio (both edema sensitive), as well as LGE (scar imaging), combining into Lake Louise Criteria, is frequently used. Although this combination provides an excellent positive predictive value, it does not reliably exclude disease. Thus, it is used to increase the pretest likelihood of myocarditis before endomyocardial biopsy.52,58 In the absence of any CMR findings, the diagnosis remains unclear.52,58 Another drawback of this approach is the inability to stage the severity or activity of the disease. This is important: the presence of residual LGE in chronic myocarditis was shown to predict worse remodeling and poor outcome,59 meaning that detecting and treating disease in the early acute stage could potentially reduce the subsequent burden of irreversible injury and thus improve outcome.
Much evidence supports the notion that T1-mapping indices are influenced by the presence of myocardial edema, such as in acute myocardial infarction,60,61 acute62–64 or chronic myocarditis,63,65 or Takotsubo cardiomyopathy.20 The recognition of acute inflammation by T1 mapping was shown to be superior to edema imaging with T2-weighted sequences62–64 and to LGE.54,63 This evidence supports a shift in thinking about the current CMR approach to myocarditis. First, native T1 provides excellent positive and negative predictive values for the presence of or exclusion of myocardial edema, thus supporting confirmation and exclusion of disease. Second, it allows an insight into the stage of disease by distinguishing between acute and chronic myocardial inflammation because resolution of inflammatory disease is paralleled by a normalization of T1 indices.63,66 T1 mapping helped to unravel the low-grade myocardial inflammation and improved understanding of subclinical course of cardiac involvement in patients with systemic inflammation, chronic infection, and drug-induced cardiotoxicity.19,67–70 These occur in the absence of overt cardiac dysfunction or abnormalities on the currently used CMR sequences, adding potential tools of screening in populations at high risk of cardiomyopathy and HF.
T1 Mapping in Nonischemic Dilative Cardiomyopathy
Nonischemic DCM is an increasingly recognized cause of cardiovascular morbidity and mortality.9 Although this is partly the effect of improved management and outcome in ischemic heart disease,71 it is also because of improved pathophysiological insights and means of NICM recognition.9,72 In DCM, several complex pathophysiological processes promote intrinsic myocardial impairment and remodeling, including diffuse extracellular matrix remodeling, myofibroblast transformation, and cardiomyocyte loss, affecting the myocardium globally.73,74 Moreover, it is increasingly understood that much of this process occurs through a protracted subclinical course, whereby the onset of symptoms, most commonly as HF or arrhythmic manifestations, corresponds with advanced disease manifestations (Figure 2).
To date, the lack of an accurate and noninvasive characterization of myocardial disease has limited its early recognition and effective clinical management. In a minority of symptomatic DCM patients, regional myocardial disease can be detected with LGE1,10 (Figure 1). The presence and patterns of nonischemic LGE in DCM have gained clinical relevance for several reasons. LGE allows noninvasive differentiation from ischemic cardiomyopathy5,75 and directly informs on the underlying pathogenesis in some cases.3,76 It is also recognized that DCM patients with LGE have worse prognosis and outcome, including all-cause mortality, HF hospitalizations, and sudden cardiac death,6,11,77–79 supporting independent risk stratification beyond the contribution of ejection fraction.80,81 It relates to increasing age6,78 and the cumulative duration and severity of the underlying pathophysiological process. Histologically, LGE in DCM corresponds to replacement fibrosis,6,51 which seems not modifiable with treatment.82
In patients with DCM, several pathophysiological drivers were found related to the change in T1 indices, including myocardial fibrosis, infiltration, and inflammation; the latter is an increasingly recognized underlying cause of DCM. Because of its ability to recognize subclinical diffuse myocardial involvement, as well as a measure of longitudinal changes of myocardial inflammation, T1 mapping seems promising for detecting subclinical pathophysiological changes, potentially allowing us to modify the course of disease. This is supported by histological data45,51 on accumulation of diffuse fibrosis and the parallel relationships of T1 mapping with progressive ventricular remodeling and stiffness in DCM.11,45,83–86 Abnormal T1-mapping indices were also found in other common causes of DCM, including patients with cardiac amyloidosis,87–89 diabetic cardiomyopathy,34,90 and iron overload cardiomyopathies.91 Native T1 mapping may also become of utility in patients with congenital heart disease and heart transplantation for rejection,44,92,93 whose monitoring currently relies on repetitive invasive investigations. In a multicenter observational study, native T1 and ECV have been shown to be stronger predictors of poor outcome in DCM than classic parameters,11 whereby native T1 was the strongest independent predictor of all-cause mortality and development of HF (Figure 6). Age, sex, New York Heart Association functional class, EF, and LGE were less powerful in predicting survival but remained independently predictive for the HF end point. These findings lend support to the premise that unlike fixed, irreversible injury (seen by LGE), the severity of diffuse disease (as detected by T1 mapping) may be pathophysiologically a more relevant parameter because it is directly related to disease progression and the functional capacity of the remaining myocardium. The continuous nature of T1 values corresponds well with the rate of clinical events: the higher the native T1, the greater the risk of adverse events. Conversely, those with native T1 within or close to the normal range26 have a low likelihood of an adverse outcome. These findings allow refining the current approach to risk stratification in patients with DCM, and there may be a central role for native T1, over and above LGE and EF.
T1 Mapping in Hypertrophic Phenotypes
Differential diagnosis of LV hypertrophy represents a common clinical challenge, in particular between HCM and increased LV wall thickness because of systemic hypertension. Reactive LV hypertrophy, which develops in response to an extrinsic increase in cardiac work, such as in hypertension, is distinguished from LV hypertrophy because of familial HCM, in which the stimulus for increase in LV wall thickness is intrinsic to the genetically altered cardiomyocytes. HCM is characterized by diffuse myocardial disease defined by structurally dysmorphic myocytes, architectural loss of parallel arrangement, and disarray of fibers and fascicles, as well as genetically driven alterations of extracellular matrix with accumulation of interstitial fibrosis. In hypertension, however, the structural change is characterized by the addition of new but structurally normal myofibrils.94–97 CMR is able to discern these fundamentally different pathophysiological pathways by phenotyping the complex underlying pathophysiology using tissue characterization. A considerable number of patients with HCM shows nonischemic-type LGE most commonly as intramyocardial patches in right ventricular insertion points.98 Although not pathognomonic, the presence of these features can separate HCM from hypertension; the latter more commonly presents with ischemic-like LGE.99 LGE in HCM has a prognostic significance for all-cause mortality and HF100,101 and possibly also sudden cardiac death.101 In the absence of LGE, discrimination remains challenging.
Several T1-mapping studies in patients with HCM revealed significantly raised T1 indices in overt disease as well as in a considerable proportion of subexpressed HCM genotype–positive subjects.19,85,102–105 This finding is relevant because many families undergo genetic testing and phenotypic assessment for the presence of HCM without a clear definition of subclinical disease; although a genetic diagnosis helps to identify the presence of relevant sarcomere mutations, it is limited in confirming the presence of subclinical disease or predicting the risk of developing an overt phenotype and the associated adverse features. T1 mapping may serve to identify those with existing subclinical myocardial abnormalities. Because diffuse myocardial remodeling in HCM may be a dynamic process across a spectrum of LV wall thickness, longitudinal assessments by also monitoring native T1 as opposed to only LV wall thickness may facilitate an insight into the rate of disease progression, as well as gauging the true risk of developing an adverse phenotype.
T1-mapping indices are informative for the presence of myocardial infiltration, including myocardial amyloid,87–89 iron or lipid deposition deciphering the subclinical myocardial involvement in iron overload, and Anderson–Fabry disease, respectively.91,106,107 From a clinical perspective, T1 indices can support differentiation of the infiltrative hypertrophic phenocopies in suspected cardiac amyloid infiltration: normal T1 indices can virtually exclude the presence of amyloid disease. On the contrary, significantly raised T1 indices point toward HCM or cardiac amyloid,84,87,89 whereas low T1 values might raise the prospect of Fabry’s disease in the appropriate setting. Further risk stratification can be supported by way of other specific features of either disease, including typical LGE patterns, asymmetrical septal or apical LV hypertrophy, flow obstruction phenomena, pericardial and pleural effusions, and so on.94,108 T1 indices in patients with confirmed cardiac amyloidosis were also shown to relate to worse course even in an already poor prognosis.109
T1 Mapping in Chronic Ischemic Cardiomyopathy
Several single-center studies investigated the role of T1 values in noninfarcted myocardium (Table 5), in providing a noninvasive measure of extracellular space expansion and remodeling.73 These studies were pivotal in showing the markedly reduced postcontrast T1 in remote myocardium in patients with systolic and diastolic HF, respectively.41,43 These studies also indicated an unfavorable relationship of ECV with outcome,34,35,110 as well as the relationship of diabetes mellitus with ECV.111 These studies cumulatively revealed a predictive value for ECV in terms of survival, cardiac mortality, and development of HF. ECV was found to be a significant predictor in univariate models in all studies; however, when combined with clinical parameters in multivariate models, the significance of ECV did not prevail.35 Diabetic patients were found to have an increased ECV, which was predictive of worse outcome.111 However, further evidence and systematic studies are needed. Most studies included patients presenting to clinical CMR via clinical referrals (all-comers), with subsequent exclusion of subjects with HCM or amyloidosis, but retaining patients with ischemic, nonischemic, and valvular heart disease,34,35,110 thus, mixing various types of pathophysiology of LV remodeling. T1 values are systematically higher in patients with nonischemic DCM in comparison to remote myocardium of ischemic cardiomyopathy.112 Prognostic contribution of inducible ischemia to outcome may have also been significant.113,114 Furthermore, because of the primary focus on the imaging parameters (ECV, EF, and LGE), these studies lack comparisons with traditional markers of HF, such as brain natriuretic peptide levels, or other established HF risk scores,115 prohibiting a full head-to-head comparisons with the current HF practice recommendation81,116
Outcome Studies for All-Cause Mortality and Composite Cardiac/Heart Failure End Points
T1 Mapping in Acute Myocardial Ischemia and Infarction
In detecting an acute myocardial infarction and regional injury, T1 mapping harbors a potential to support a contrast-free clinical application.20,40,60,117,118 Native T1 was able to outline the area of ischemic injury or area at risk equivalent to microspheres used as a gold standard in an experimental study.60 In patients with acute myocardial infarction, native T1 is equivalent to T2 and LGE imaging in assessment of severity of myocardial involvement.117 T1-mapping indices are also able to detect myocardial replacement scar,40,118 although the exact delineation of the scar area remains challenging because of the presently insufficient spatial resolution and low signal to noise ratio. Finally, native T1 can reflect an increase in myocardial blood volume at rest as a compensatory mechanism for an increased resistance to blood flow because of a stenotic coronary artery, allowing recognition of ischemic areas.21 These studies highlight the sea of future potential for native T1 in addressing the many pertinent clinical questions in a contrast-free cardiac imaging application.22
Limitation of State of Art and Avenues of Translation
The available evidence to date suggests that T1 mapping with CMR, a unique noninvasive and radiation-free imaging method, may support efficient pathways to screen, intervene, and prevent LV remodeling in several conditions ahead of manifest disease and costly end-stage complications, especially in NICMs. T1-mapping indices and LGE as complementary tools for assessment of diffuse and regional myocardial disease (Figure 6) provide a conceptually novel pathway in clinical assessment of myocardial involvement. The emerging evidence with T1 mapping suggests that the unfavorable course in many conditions may be better underscored by characterization of diffuse myocardial disease. Despite these advances, the assessment of regional disease is likely to remain helpful in providing the insight into underlying pathogenesis,1,3 as well as assessment of the irreversible disease burden and risk stratification.6
Further studies need to demonstrate the value of the various T1-mapping indices not only for risk classification but also for risk modification by guiding therapy in randomized controlled multicentre trials. Mapping techniques have already demonstrated their ability to detect myocardial involvement in subclinical stage of disease.19,68,105 Identification of subjects at higher risk of developing advanced disease creates an opportunity for a greater level of clinical care, ahead of late advanced disease stages and costly interventions, and overall improvement of quality of life. Such screening would support exclusion of low-risk subjects with greater confidence, potentially leading to reduction of healthcare costs by avoiding overinvestigation, unnecessary follow-ups, loss of productivity, and improved patients’ satisfaction and confidence in control of their own health. Science would gain from improved knowledge into the natural course of disease and clearer phenotypes of disease stages, enabling clinical trials with more targeted and efficacious interventions. Finally, native T1 has the potential to allow for a cardiovascular examination without the need for GCAs, which may shift the risk assessment for the application of contrast agents more toward native studies, which will improve its risk benefit ratio and thus acceptability.
Future development and research need to take into account that mapping techniques provide absolute data measured in milliseconds rather than rely on relative gray values or color-encoded visualization (to spot regional disease) as historically obtained by CMR. This requires a more rigorous validation and standardization of procedures, which have consequences for clinicians, researchers, and vendors. Clinicians can only translate the research findings if they use exactly the same sequence as previously validated for accuracy, precision, and—most importantly—effect size and clinical value. Clinical researchers need to report their sequence parameters and postprocessing methods in detail, make them publicly available, and phrase their findings carefully because the findings may only hold true for the exact method applied. Technical researchers and vendors need to modify sequences in a highly controlled matter, documenting each change carefully. Each change requires a new pathway of validation. Vendors need to guarantee the presence of identical sequences over time and avoid hidden software optimizations as conventionally done with each software upgrade. Most of the currently available evidence relies on single-center expertise and single vendor–specific T1-mapping approaches. The aforementioned methodological differences in T1-mapping sequences prohibit an immediate translation of findings across different sequences, field strengths, vendors, and scanner-software generations. In such active and evolving field, the majority of published research is based on sequences that are no longer supported by current software versions, requiring constant revalidation of evidence.
Although T1-mapping sequences measure T1 estimates in millisecond, these units are sequence-specific and do not convey unifiable information. Sequences might be better compared by achieving a certain effect size by Cohen d or z scores, as well as by examining their bioequivalence against a clinically well-understood and validated sequence. Commonly, there is a selective bias in reporting results of a single T1 index, disallowing a full clinical characterization of sequences. Quantifiable imaging is a novel concept, and principles of analytic validation and qualification of sequences to enable a clinical use are a new and not yet accomplished necessity in the CMR imaging field.
Conclusions
Mapping of myocardial T1 relaxation by CMR introduced an important novel concept of quantifiable myocardial tissue characterization and has emerging data show important clinical utility over and beyond what LGE alone can provide. Several approaches to T1-mapping sequences differ in terms of magnetization preparation and readout parameters, resulting in imaging approaches, which differ in terms of T1 accuracy and sensitivity to T2 relaxation. These differences influence the precision of T1-mapping indices, as well as ability to detect a clinically meaningful signal, with consequent relevance for diagnostic and prognostic accuracy. A growing body of evidence suggests that T1-mapping indices allow recognition of interstitial myocardial disease with a considerable effect size in overt disease, as well as detection of subclinical involvement in several cardiac conditions. Outcome studies support strong predictive associations with adverse clinical events, allowing risk stratification within the methodological constraints of the available evidence. Prospective studies examining the value of T1 mapping against the standard of care is required to test its value in informing clinical management decisions. These insights may help to overcome an important gap in the early recognition of diffuse myocardial disease and discovery of targeted therapies, providing a basis for improved clinical management in a host of cardiac diseases.
Disclosures
No funding or industry disclosures. V.O. Puntmann and E. Nagel hold a patent of invention for a method for differentiation of normal myocardium from diffuse disease using T1 mapping in nonischemic cardiomyopathies and others (based on PR-MS 33.297, PR-MS 33.837, PR-MS 33.654; with no financial interest). The other authors report no conflicts.
Footnotes
This Review is in a thematic series on Cardiovascular Imaging, which includes the following articles:
T1 Mapping in Characterizing Myocardial Disease: A Comprehensive Review
Fractional Flow Reserve and Coronary Computed Tomographic Angiography: A Review and Critical Analysis
Prognostic Determinants of Coronary Atherosclerosis in Stable Ischemic Heart Disease: Anatomy, Physiology, or Morphology?
Noninvasive Molecular Imaging of Disease Activity in Atherosclerosis
Transcathether Valve Replacement and Valve Repair: Review of Procedures and Intraprocedural Echocardiographic Imaging
Advances in Echocardiographic Imaging in Heart Failure With Reduced and Preserved Ejection Fraction
Viability: Is it Still Attractive?
Guest Editors: Jagat Narula and Y. Chandrashekhar
- Nonstandard Abbreviations and Acronyms
- CMR
- cardiovascular magnetic resonance
- DCM
- dilated cardiomyopathy
- ECV
- extracellular volume fraction
- GCAs
- gadolinium contrast agents
- HCM
- hypertrophic cardiomyopathy
- HF
- heart failure
- LGE
- late gadolinium enhancement
- NICM
- nonischemic cardiomyopathies
- Received February 21, 2016.
- Revision received April 29, 2016.
- Accepted May 20, 2016.
- © 2016 American Heart Association, Inc.
References
- 1.↵
- 2.↵
- 3.↵
- 4.↵
- 5.↵
- Assomull RG,
- Shakespeare C,
- Kalra PR,
- Lloyd G,
- Gulati A,
- Strange J,
- Bradlow WM,
- Lyne J,
- Keegan J,
- Poole-Wilson P,
- Cowie MR,
- Pennell DJ,
- Prasad SK
- 6.↵
- 7.↵
- Mahrholdt H,
- Goedecke C,
- Wagner A,
- Meinhardt G,
- Athanasiadis A,
- Vogelsberg H,
- Fritz P,
- Klingel K,
- Kandolf R,
- Sechtem U
- 8.↵
- Patel MR,
- Cawley PJ,
- Heitner JF,
- Klem I,
- Parker MA,
- Jaroudi WA,
- Meine TJ,
- White JB,
- Elliott MD,
- Kim HW,
- Judd RM,
- Kim RJ
- 9.↵
- 10.↵
- 11.↵
- 12.↵
- 13.↵
- 14.↵
- 15.↵
- 16.↵
- 17.↵
- McDiarmid AK,
- Broadbent DA,
- Higgins DM,
- Swoboda PP,
- Kidambi A,
- Ripley DP,
- Erhayiem B,
- Musa TA,
- Dobson LE,
- Greenwood JP,
- Plein S
- 18.↵
- 19.↵
- Puntmann VO,
- D’Cruz D,
- Smith Z,
- Pastor A,
- Choong P,
- Voigt T,
- Carr-White G,
- Sangle S,
- Schaeffter T,
- Nagel E
- 20.↵
- Ferreira VM,
- Piechnik SK,
- Dall’Armellina E,
- Karamitsos TD,
- Francis JM,
- Choudhury RP,
- Friedrich MG,
- Robson MD,
- Neubauer S
- 21.↵
- Liu A,
- Wijesurendra RS,
- Francis JM,
- Robson MD,
- Neubauer S,
- Piechnik SK,
- Ferreira VM
- 22.↵
- 23.↵
- 24.↵
- Piechnik SK,
- Ferreira VM,
- Dall’Armellina E,
- Cochlin LE,
- Greiser A,
- Neubauer S,
- Robson MD
- 25.↵
- 26.↵
- 27.↵
- Kawel N,
- Nacif M,
- Zavodni A,
- Jones J,
- Liu S,
- Sibley CT,
- Bluemke DA
- 28.↵
- Rogers T,
- Puntmann VO
- 29.↵
- Moon JC,
- Messroghli DR,
- Kellman P,
- Piechnik SK,
- Robson MD,
- Ugander M,
- Gatehouse PD,
- Arai AE,
- Friedrich MG,
- Neubauer S,
- Schulz-Menger J,
- Schelbert EB
- 30.↵
- 31.↵
- 32.↵
- McDiarmid AK,
- Swoboda PP,
- Erhayiem B,
- Ripley DP,
- Kidambi A,
- Broadbent DA,
- Higgins DM,
- Greenwood JP,
- Plein S
- 33.↵
- Coelho-Filho OR,
- Mongeon FP,
- Mitchell R,
- Moreno H Jr.,
- Nadruz W Jr.,
- Kwong R,
- Jerosch-Herold M
- 34.↵
- Wong TC,
- Piehler K,
- Meier CG,
- Testa SM,
- Klock AM,
- Aneizi AA,
- Shakesprere J,
- Kellman P,
- Shroff SG,
- Schwartzman DS,
- Mulukutla SR,
- Simon MA,
- Schelbert EB
- 35.↵
- Kammerlander AA,
- Marzluf BA,
- Zotter-Tufaro C,
- Aschauer S,
- Duca F,
- Bachmann A,
- Knechtelsdorfer K,
- Wiesinger M,
- Pfaffenberger S,
- Greiser A,
- Lang IM,
- Bonderman D,
- Mascherbauer J
- 36.↵
- Salerno M,
- Janardhanan R,
- Jiji RS,
- Brooks J,
- Adenaw N,
- Mehta B,
- Yang Y,
- Antkowiak P,
- Kramer CM,
- Epstein FH
- 37.↵
- 38.↵
- Liu CY,
- Liu YC,
- Wu C,
- Armstrong A,
- Volpe GJ,
- van der Geest RJ,
- Liu Y,
- Hundley WG,
- Gomes AS,
- Liu S,
- Nacif M,
- Bluemke DA,
- Lima JA
- 39.↵
- von Knobelsdorff-Brenkenhoff F,
- Prothmann M,
- Dieringer MA,
- Wassmuth R,
- Greiser A,
- Schwenke C,
- Niendorf T,
- Schulz-Menger J
- 40.↵
- 41.↵
- Iles L,
- Pfluger H,
- Phrommintikul A,
- Cherayath J,
- Aksit P,
- Gupta SN,
- Kaye DM,
- Taylor AJ
- 42.↵
- 43.↵
- Mascherbauer J,
- Marzluf BA,
- Tufaro C,
- et al
- 44.↵
- Miller CA,
- Naish JH,
- Bishop P,
- Coutts G,
- Clark D,
- Zhao S,
- Ray SG,
- Yonan N,
- Williams SG,
- Flett AS,
- Moon JC,
- Greiser A,
- Parker GJ,
- Schmitt M
- 45.↵
- aus dem Siepen F,
- Buss SJ,
- Messroghli D,
- Andre F,
- Lossnitzer D,
- Seitz S,
- Keller M,
- Schnabel PA,
- Giannitsis E,
- Korosoglou G,
- Katus HA,
- Steen H
- 46.↵
- Flett AS,
- Hayward MP,
- Ashworth MT,
- Hansen MS,
- Taylor AM,
- Elliott PM,
- McGregor C,
- Moon JC
- 47.↵
- Bull S,
- White SK,
- Piechnik SK,
- Flett AS,
- Ferreira VM,
- Loudon M,
- Francis JM,
- Karamitsos TD,
- Prendergast BD,
- Robson MD,
- Neubauer S,
- Moon JC,
- Myerson SG
- 48.↵
- Fontana M,
- White SK,
- Banypersad SM,
- Sado DM,
- Maestrini V,
- Flett AS,
- Piechnik SK,
- Neubauer S,
- Roberts N,
- Moon JC
- 49.↵
- de Meester de Ravenstein C,
- Bouzin C,
- Lazam S,
- Boulif J,
- Amzulescu M,
- Melchior J,
- Pasquet A,
- Vancraeynest D,
- Pouleur A-C,
- Vanoverschelde J-LJ,
- Gerber BL
- 50.↵
- 51.↵
- Iles LM,
- Ellims AH,
- Llewellyn H,
- Hare JL,
- Kaye DM,
- McLean CA,
- Taylor AJ
- 52.↵
- Caforio AL,
- Pankuweit S,
- Arbustini E,
- et al
- 53.↵
- 54.↵
- Lurz P,
- Luecke C,
- Eitel I,
- Föhrenbach F,
- Frank C,
- Grothoff M,
- de Waha S,
- Rommel KP,
- Lurz JA,
- Klingel K,
- Kandolf R,
- Schuler G,
- Thiele H,
- Gutberlet M
- 55.↵
- Cooper LT,
- Baughman KL,
- Feldman AM,
- Frustaci A,
- Jessup M,
- Kuhl U,
- Levine GN,
- Narula J,
- Starling RC,
- Towbin J,
- Virmani R
- 56.↵
- Lurz P,
- Eitel I,
- Adam J,
- Steiner J,
- Grothoff M,
- Desch S,
- Fuernau G,
- de Waha S,
- Sareban M,
- Luecke C,
- Klingel K,
- Kandolf R,
- Schuler G,
- Gutberlet M,
- Thiele H
- 57.↵
- 58.↵
- 59.↵
- Grün S,
- Schumm J,
- Greulich S,
- Wagner A,
- Schneider S,
- Bruder O,
- Kispert EM,
- Hill S,
- Ong P,
- Klingel K,
- Kandolf R,
- Sechtem U,
- Mahrholdt H
- 60.↵
- Ugander M,
- Oki AJ,
- Hsu LY,
- Kellman P,
- Greiser A,
- Aletras AH,
- Sibley CT,
- Chen MY,
- Bandettini WP,
- Arai AE
- 61.↵
- Dall’Armellina E,
- Ferreira VM,
- Kharbanda RK,
- Prendergast B,
- Piechnik SK,
- Robson MD,
- Jones M,
- Francis JM,
- Choudhury RP,
- Neubauer S
- 62.↵
- Ferreira VM,
- Piechnik SK,
- Dall’Armellina E,
- Karamitsos TD,
- Francis JM,
- Ntusi N,
- Holloway C,
- Choudhury RP,
- Kardos A,
- Robson MD,
- Friedrich MG,
- Neubauer S
- 63.↵
- Hinojar R,
- Foote L,
- Arroyo Ucar E,
- Jackson T,
- Jabbour A,
- Yu CY,
- McCrohon J,
- Higgins DM,
- Carr-White G,
- Mayr M,
- Nagel E,
- Puntmann VO
- 64.↵
- Radunski UK,
- Lund GK,
- Stehning C,
- Schnackenburg B,
- Bohnen S,
- Adam G,
- Blankenberg S,
- Muellerleile K
- 65.↵
- Bohnen S,
- Radunski UK,
- Lund GK,
- Kandolf R,
- Stehning C,
- Schnackenburg B,
- Adam G,
- Blankenberg S,
- Muellerleile K
- 66.↵
- 67.↵
- Ntusi NA,
- Piechnik SK,
- Francis JM,
- Ferreira VM,
- Matthews PM,
- Robson MD,
- Wordsworth PB,
- Neubauer S,
- Karamitsos TD
- 68.↵
- Ntusi NA,
- Piechnik SK,
- Francis JM,
- Ferreira VM,
- Rai AB,
- Matthews PM,
- Robson MD,
- Moon J,
- Wordsworth PB,
- Neubauer S,
- Karamitsos TD
- 69.↵
- Holloway CJ,
- Ntusi N,
- Suttie J,
- Mahmod M,
- Wainwright E,
- Clutton G,
- Hancock G,
- Beak P,
- Tajar A,
- Piechnik SK,
- Schneider JE,
- Angus B,
- Clarke K,
- Dorrell L,
- Neubauer S
- 70.↵
- Tham EB,
- Haykowsky MJ,
- Chow K,
- Spavor M,
- Kaneko S,
- Khoo NS,
- Pagano JJ,
- Mackie AS,
- Thompson RB
- 71.↵
- Perk J,
- De Backer G,
- Gohlke H,
- et al
- 72.↵
- Maron BJ,
- Towbin JA,
- Thiene G,
- Antzelevitch C,
- Corrado D,
- Arnett D,
- Moss AJ,
- Seidman CE,
- Young JB
- 73.↵
- 74.↵
- 75.↵
- McCrohon JA,
- Moon JC,
- Prasad SK,
- McKenna WJ,
- Lorenz CH,
- Coats AJ,
- Pennell DJ
- 76.↵
- 77.↵
- Iles L,
- Pfluger H,
- Lefkovits L,
- Butler MJ,
- Kistler PM,
- Kaye DM,
- Taylor AJ
- 78.↵
- Neilan TG,
- Coelho-Filho OR,
- Danik SB,
- Shah RV,
- Dodson JA,
- Verdini DJ,
- Tokuda M,
- Daly CA,
- Tedrow UB,
- Stevenson WG,
- Jerosch-Herold M,
- Ghoshhajra BB,
- Kwong RY
- 79.↵
- Leyva F,
- Taylor RJ,
- Foley PW,
- Umar F,
- Mulligan LJ,
- Patel K,
- Stegemann B,
- Haddad T,
- Smith RE,
- Prasad SK
- 80.↵
- Russo AM,
- Stainback RF,
- Bailey SR,
- Epstein AE,
- Heidenreich PA,
- Jessup M,
- Kapa S,
- Kremers MS,
- Lindsay BD,
- Stevenson LW
- 81.↵
- McMurray JJV,
- Adamopoulos S,
- Anker SD,
- Auricchio A,
- Böhm M,
- Dickstein K,
- Falk V,
- Filippatos G,
- Fonseca C,
- Gomez-Sanchez MA,
- Jaarsma T,
- Køber L,
- Lip GYH,
- Maggioni AP,
- Parkhomenko A,
- Pieske BM,
- Popescu BA,
- Rønnevik PK,
- Rutten FH,
- Schwitter J,
- Seferovic P,
- Stepinska J,
- Trindade PT,
- Voors AA,
- Zannad F,
- Zeiher A
- Bax JJ,
- Baumgartner H,
- Ceconi C,
- Dean V,
- Deaton C,
- Fagard R,
- Funck-Brentano C,
- Hasdai D,
- Hoes A,
- Kirchhof P,
- Knuuti J,
- Kolh P,
- McDonagh T,
- Moulin C,
- Reiner Z,
- Sirnes PA,
- Tendera M,
- Torbicki A,
- Vahanian A,
- Windecker S
- Sechtem U,
- Bonet LA,
- Avraamides P,
- Ben Lamin HA,
- Brignole M,
- Coca A,
- Cowburn P,
- Dargie H,
- Elliott P,
- Flachskampf FA,
- Guida GF,
- Hardman S,
- Iung B,
- Merkely B,
- Mueller C,
- Nanas JN,
- Nielsen OW,
- Orn S,
- Parissis JT,
- Ponikowski P
- 82.↵
- Xie M,
- Burchfield JS,
- Hill JA
- 83.↵
- 84.↵
- Puntmann VO,
- Voigt T,
- Chen Z,
- Mayr M,
- Karim R,
- Rhode K,
- Pastor A,
- Carr-White G,
- Razavi R,
- Schaeffter T,
- Nagel E
- 85.↵
- Dass S,
- Suttie JJ,
- Piechnik SK,
- Ferreira VM,
- Holloway CJ,
- Banerjee R,
- Mahmod M,
- Cochlin L,
- Karamitsos TD,
- Robson MD,
- Watkins H,
- Neubauer S
- 86.↵
- Mordi I,
- Carrick D,
- Bezerra H,
- Tzemos N
- 87.↵
- 88.↵
- Banypersad SM,
- Sado DM,
- Flett AS,
- Gibbs SD,
- Pinney JH,
- Maestrini V,
- Cox AT,
- Fontana M,
- Whelan CJ,
- Wechalekar AD,
- Hawkins PN,
- Moon JC
- 89.↵
- Karamitsos TD,
- Piechnik SK,
- Banypersad SM,
- Fontana M,
- Ntusi NB,
- Ferreira VM,
- Whelan CJ,
- Myerson SG,
- Robson MD,
- Hawkins PN,
- Neubauer S,
- Moon JC
- 90.↵
- Jellis CL,
- Sacre JW,
- Wright J,
- Jenkins C,
- Haluska B,
- Jeffriess L,
- Martin J,
- Marwick TH
- 91.↵
- 92.↵
- Broberg CS,
- Chugh SS,
- Conklin C,
- Sahn DJ,
- Jerosch-Herold M
- 93.↵
- Dusenbery SM,
- Jerosch-Herold M,
- Rickers C,
- Colan SD,
- Geva T,
- Newburger JW,
- Powell AJ
- 94.↵
- Elliott PM,
- Anastasakis A,
- Borger MA,
- et al
- 95.↵
- 96.↵
- 97.↵
- 98.↵
- 99.↵
- Rudolph A,
- Abdel-Aty H,
- Bohl S,
- Boyé P,
- Zagrosek A,
- Dietz R,
- Schulz-Menger J
- 100.↵
- Bruder O,
- Wagner A,
- Jensen CJ,
- Schneider S,
- Ong P,
- Kispert EM,
- Nassenstein K,
- Schlosser T,
- Sabin GV,
- Sechtem U,
- Mahrholdt H
- 101.↵
- Chan RH,
- Maron BJ,
- Olivotto I,
- et al
- 102.↵
- Ho CY,
- Abbasi SA,
- Neilan TG,
- Shah RV,
- Chen Y,
- Heydari B,
- Cirino AL,
- Lakdawala NK,
- Orav EJ,
- González A,
- López B,
- Díez J,
- Jerosch-Herold M,
- Kwong RY
- 103.↵
- 104.↵
- Ellims AH,
- Iles LM,
- Ling LH,
- Chong B,
- Macciocca I,
- Slavin GS,
- Hare JL,
- Kaye DM,
- Marasco SF,
- McLean CA,
- James PA,
- du Sart D,
- Taylor AJ
- 105.↵
- Hinojar R,
- Varma N,
- Child N,
- et al
- 106.↵
- Sado DM,
- White SK,
- Piechnik SK,
- et al
- 107.↵
- Pica S,
- Sado DM,
- Maestrini V,
- et al
- 108.↵
- Maceira AM,
- Joshi J,
- Prasad SK,
- Moon JC,
- Perugini E,
- Harding I,
- Sheppard MN,
- Poole-Wilson PA,
- Hawkins PN,
- Pennell DJ
- 109.↵
- Banypersad SM,
- Fontana M,
- Maestrini V,
- Sado DM,
- Captur G,
- Petrie A,
- Piechnik SK,
- Whelan CJ,
- Herrey AS,
- Gillmore JD,
- Lachmann HJ,
- Wechalekar AD,
- Hawkins PN,
- Moon JC
- 110.↵
- Schelbert EB,
- Piehler KM,
- Zareba KM,
- et al
- 111.↵
- Wong TC,
- Piehler KM,
- Kang IA,
- Kadakkal A,
- Kellman P,
- Schwartzman DS,
- Mulukutla SR,
- Simon MA,
- Shroff SG,
- Kuller LH,
- Schelbert EB
- 112.↵
- Puntmann VO,
- Arroyo Ucar E,
- Hinojar Baydes R,
- Ngah NB,
- Kuo YS,
- Dabir D,
- Macmillan A,
- Cummins C,
- Higgins DM,
- Gaddum N,
- Chowienczyk P,
- Plein S,
- Carr-White G,
- Nagel E
- 113.↵
- Tonino PA,
- De Bruyne B,
- Pijls NH,
- Siebert U,
- Ikeno F,
- van’ t Veer M,
- Klauss V,
- Manoharan G,
- Engstrøm T,
- Oldroyd KG,
- Ver Lee PN,
- MacCarthy PA,
- Fearon WF
- 114.↵
- Hussain ST,
- Paul M,
- Plein S,
- McCann GP,
- Shah AM,
- Marber MS,
- Chiribiri A,
- Morton G,
- Redwood S,
- MacCarthy P,
- Schuster A,
- Ishida M,
- Westwood MA,
- Perera D,
- Nagel E
- 115.↵
- Pocock SJ,
- Ariti CA,
- McMurray JJ,
- Maggioni A,
- Køber L,
- Squire IB,
- Swedberg K,
- Dobson J,
- Poppe KK,
- Whalley GA,
- Doughty RN
- 116.↵
- Yancy CW,
- Jessup M,
- Bozkurt B,
- et al
- 117.↵
- Dall’Armellina E,
- Piechnik SK,
- Ferreira VM,
- Si QL,
- Robson MD,
- Francis JM,
- Cuculi F,
- Kharbanda RK,
- Banning AP,
- Choudhury RP,
- Karamitsos TD,
- Neubauer S
- 118.↵
- Kali A,
- Choi EY,
- Sharif B,
- Kim YJ,
- Bi X,
- Spottiswoode B,
- Cokic I,
- Yang HJ,
- Tighiouart M,
- Conte AH,
- Li D,
- Berman DS,
- Choi BW,
- Chang HJ,
- Dharmakumar R
- 119.↵
- Abonnenc M,
- Nabeebaccus AA,
- Mayr U,
- et al
- 120.↵
- Sutton MG,
- Sharpe N
- 121.↵
- 122.↵
- 123.↵
- 124.↵
- 125.↵
- Roujol S,
- Weingärtner S,
- Foppa M,
- Chow K,
- Kawaji K,
- Ngo LH,
- Kellman P,
- Manning WJ,
- Thompson RB,
- Nezafat R
- 126.
- White SK,
- Sado DM,
- Fontana M,
- Banypersad SM,
- Maestrini V,
- Flett AS,
- Piechnik SK,
- Robson MD,
- Hausenloy DJ,
- Sheikh AM,
- Hawkins PN,
- Moon JC
- 127.
- 128.
- Chin CW,
- Semple S,
- Malley T,
- White AC,
- Mirsadraee S,
- Weale PJ,
- Prasad S,
- Newby DE,
- Dweck MR
- 129.
- Singh A,
- Horsfield MA,
- Bekele S,
- Khan JN,
- Greiser A,
- McCann GP
- 130.
- Kuruvilla S,
- Janardhanan R,
- Antkowiak P,
- Keeley EC,
- Adenaw N,
- Brooks J,
- Epstein FH,
- Kramer CM,
- Salerno M
- 131.
- Treibel TA,
- Zemrak F,
- Sado DM,
- Banypersad SM,
- White SK,
- Maestrini V,
- Barison A,
- Patel V,
- Herrey AS,
- Davies C,
- Caulfield MJ,
- Petersen SE,
- Moon JC
- 132.
- Ellims AH,
- Iles LM,
- Ling LH,
- Hare JL,
- Kaye DM,
- Taylor AJ
- 133.
- Ntusi NA,
- Piechnik SK,
- Francis JM,
- Ferreira VM,
- Matthews PM,
- Robson MD,
- Wordsworth PB,
- Neubauer S,
- Karamitsos TD
- 134.
This Issue
Jump to
- Article
- Abstract
- Basic Concepts of T1 Mapping: From Acquisition to Postprocessing
- Normal Ranges
- T1-Mapping Indices: Initial Experience and Histological Correlation
- T1 Mapping in Myocardial Inflammation
- T1 Mapping in Nonischemic Dilative Cardiomyopathy
- T1 Mapping in Hypertrophic Phenotypes
- T1 Mapping in Chronic Ischemic Cardiomyopathy
- T1 Mapping in Acute Myocardial Ischemia and Infarction
- Limitation of State of Art and Avenues of Translation
- Conclusions
- Disclosures
- Footnotes
- References
- Figures & Tables
- Info & Metrics
Article Tools
- T1 Mapping in Characterizing Myocardial DiseaseValentina O. Puntmann, Elif Peker, Y. Chandrashekhar and Eike NagelCirculation Research. 2016;119:277-299, originally published July 7, 2016https://doi.org/10.1161/CIRCRESAHA.116.307974
Citation Manager Formats