This lecture will discuss the clinical needs and applications of myocardial relaxometry. We will discuss the need to develop relaxometry imaging biomarkers which are sensitive, specific, predictive and robust. These criteria will need to be fulfilled to make clinical decisions in individual patients. We will also discuss current and emerging clinical applications of myocardial relaxometry.
Michael Salerno MD, PhD, MS
Associate Professor of Medicine Radiology and Biomedical
Engineering
University of Virginia Health System
msalerno@virginia.edu
Over the past few years there has been growing enthusiasm in the research community regarding the use of tissue relaxation properties such as T1, T2, and T2* to provide information regarding a number of cardiac pathologies. However, to data a few number of applications have translated into routine clinical application outside of academic medical centers. There are a number of clinical “needs” which could potentially be addressed by parametric mapping techniques. The majority of current research has focused on using parametric mapping techniques to understand differences between populations of patients. Some examples of this type of research include: (1) Detecting an increase in native T1 in a population of patients with hypertensive left ventricular hypertrophy as compared to a group of patients with hypertension alone. (2) Detecting an increase in Native T2 in patients with cardiac sarcoidosis as compared to a population of normal controls. (3) Predicting risk of heart failure in a group of patients with iron overload and low T2* as compared to a group of subjects with normal T2*. These studies are important because they establish a link between the biomarker and an underlying physiological process (such as inflammation or fibrosis). However the statistics used to detect these differences between groups are related to the sample size of groups. Thus the uncertainty in the measurements in individual subjects can be overcome by having a large sample size as the power of these statistics tend to increase with the square-root of the number of subjects.
The most important clinical need is to have an imaging biomarker which is sensitive, specific, predictive, and robust enough to make clinical decisions in an individual patient. For a biomarker to be sensitive it needs to be able to accurately detect small changes. This requires both a large change in the parameter of interest with pathology as well as a precise measurement. Ideally the marker should identify early, potentially reversible pathology. This remains a challenge as many of the relaxometry changes have a fairly small dynamic range as compared to the uncertainty in the measurements using current techniques. For a biomarker to be specific you would like to have a high signal to background, meaning if the value is abnormal, it is very likely that the patient has a given disease. Ideally the biomarker would differentiate between different pathological states of disease (acute versus chronic injury, fibrosis versus infiltration vs inflammation). This also remains a challenge, as the relaxometry parameters tend to change in a similar direction with various different pathologies (i.e. Native T1 is increased in acute and chronic infarction). The biomarker has to be predictive. Ideally, the severity of abnormality in the parameter would be proportionate to the extent of injury or the risk for an adverse outcome. Finally the marker needs to be robust and simple to use. It needs to work >99% of the time, and when it fails, the user should be able to easily detect the failure. This clinical need, to drive individual patient management will be key to the clinical success of relaxometry techniques.
The third category of clinical need is to have robust biomarkers for development of new therapies and drugs. There are a number of ways relaxometry imaging could be used in the development of new therapeutics. The biomarkers could serve as surrogate endpoints in clinical trials. The technique could serve as a biomarker to identify patients with a particular disease, or to identify people with a disease that may be at a higher risk of an adverse outcome. The technique could potentially help select a therapeutic strategy. This could be both identifying patients who would benefit from a therapy, and identifying patients who are unlikely to benefit from a specific therapy. Finally, the technique could be used to look at a therapeutic response provided that the biomarker changes in a reliable way in response to a therapy. There are a number of current and potential future applications for which relaxometry measurements could be used in clinical practice.
To date, the largest success in parametric mapping is arguably the use of T2* to detect iron overload cardiomyopathy. This has now been used clinically to identify patients with iron overload cardiomyopathy, to select the appropriate timing of chelation therapy, and to predict what patients with iron-overload are at greatest risk of having progression to heart failure. There has been growing interest in using T2* to identify intramyocardial hemorrhage. There is recent data to suggest that the presence of intramyocardial hemorrhage in myocardial infarction could be the substrate for inflammation and could be related to arrhythmias and adverse remodeling. In terms of diffuse pathologies which can be identified with T1 mapping, Amyloid cardiomyopathy results in a relatively large changes in Native T1. The potential for a non-contrast diagnosis of amyloid is important, as this population of subjects often have renal insufficiency and cannot receive contrast. One potential issue with this application is that given the uncertainty in T1 measurements, there may be overlap with patients that may have thick hearts for other reasons (HTN, HCM). An increase in T1 is thus not specific for cardiac amyloidosis. In other diseases characterized by smaller magnitudes of difference in T1, the situation becomes more problematic. In diseases characterized by regional variation in T1 and T2, parametric mapping has already demonstrated clinical utility. Specific examples include detecting of edema in acute pathologies such as myocardial infarction, myocarditis, and takusobo cardiomyopathy. In these pathologies, T1 and T2 mapping techniques overcome limitations of T1 and T2 weighted techniques and are already being used clinically. T1 and T2 have been shown to differ in a number of cardiac pathologies including sarcoid, heart transplantation, heart failure with preserved and reduced EF, and numerous rheumatological diseases which affect the heart. However, whether the differences can be detected robustly in an individual patient, and whether or not they change management remains to be clarified. There is also potential for using T1 and T2 (and T1rho) for detecting abnormalities in congenital heart disease and right-heart pathologies.
There are also a number of open questions regarding parametric mapping which require further research. What spatial resolution is necessary? This will depend on whether or not the process that you want to image is diffuse or focal and whether you are interested in detecting abnormalities in thin walled structures (right ventricle, thin left ventricular walls in dilated cardiomyopathy, or the left atrium). Is whole heart coverage necessary? This obviously depends on whether you have a disease which is purely diffuse or diffuse and focal in nature. For a diffuse disease a single slice may be sufficient and time efficient. For focal diseases multi-slice coverage may be necessary. Do we need “pure” measurements of the relaxation parameters? If a technique is well correlated to the underlying pathology of interest, a “contaminated T1” measurement (i.e. influenced by MT, T2) etc. may be sufficient. To characterize other metrics such as ECV which are derived from T1 this may not be the case. Will non-contrast relaxometry techniques be robust enough to avoid the use of gadolinium contrast agents? With the recent findings of gadolinium deposition in tissue, the desire for robust non-contrast techniques may have increasing desirability.
In summary, relaxometry has a number of potential clinical applications. Creating sensitive, specific, predictive, and robust techniques will be essential to make important management decisions in individual patients which is really required for relaxometry to have real clinical impact.