Diffusion tensor cardiovascular magnetic resonance is unique in noninvasively assessing cardiac microstructure. In this session we examine the acquisition and processing techniques used to overcome the difficulties in measuring microscopic diffusion while the heart is moving with the cardiac and respiratory cycles.
At the end of the lecture attendees should understand:
- The challenges involved in acquiring and reconstructing diffusion tensor cardiovascular magnetic resonance (DT-CMR) data.
- The common solutions to these challenges and the advantages/disadvantages of these approaches.
- The key stages in establishing an in-vivo DT-CMR project.
Neurological DTI (see reviews(7,8)) is typically performed with a spin-echo (SE) sequence using diffusion encoding gradients placed either side of the 180˚ pulse(9). To obtain sufficient diffusion contrast (determined by the b-value), diffusion encoding gradients must be relatively long (see equation). Motion during diffusion encoding results in signal loss, which is indistinguishable from diffusion related signal loss and the short T2 of myocardium also results in low signal to noise ratio (SNR).
$$b=\gamma^2G^2\delta^2(\Delta-\frac{\delta}{3})$$
where G and ∂ are the amplitude and duration of the diffusion encoding gradients and ∆ is the time between them (infinitesimal ramp times). Images are acquired with ≥2 b-values and encoding in ≥6 directions to enable reconstruction of the 3x3 diffusion tensor(10–13).
STEAM methods
Stimulated echo acquisition mode (STEAM)(18,19) DT-CMR places monopolar diffusion gradients at identical timings in two successive cardiac cycles, giving ∆=1RR-interval. Magnetization is stored longitudinally between diffusion gradients, resulting in a 50% loss of magnetization available for imaging and the signal also decays with T1 during ∆. However, the long ∆ means that diffusion gradients are very short and TE is ~25ms(20), avoiding severe T2 decay (myocardial T2~45ms).
Cyclical strain during ∆ results in artefactual modulation of the measured diffusion(19,21). Several methods have been proposed to minimize this strain effect including: model-based correction(21); imaging at “sweet-spots” (22); and bipolar STEAM(23). More recently, it has been argued that the strain effect is over-estimated by the model, which assumes that the myocardium is jelly-like(24,25).
Spin-echo based methods
Initially, bipolar diffusion gradients were proposed to provide velocity compensation in SE DT-CMR(15). Successful diastolic imaging has been demonstrated by acquiring bipolar-SE data at several trigger delays around diastasis and retrospectively selecting data with minimum motion artefact(26–30). Recently, acceleration compensated gradient waveforms for SE sequences were proposed(31,32), for imaging during systolic contraction. These motion-compensated SE (MC-SE) sequences avoid the strain effect, the halving of SNR with stimulated echoes and the requirement to image over two cardiac cycles. However, MC-SE techniques assume that the heart moves with constant acceleration during diffusion encoding and distances diffused during ∆ are much smaller than with STEAM.
Diffusion prepared cardiac imaging
Diffusion prepared cardiac imaging adds motion compensated diffusion encoding to a T2-preparation pulse(33,34) and acquires data using segmented bSSFP(34,35) or TSE(36) readouts.
Respiratory motion
DT-CMR is a low SNR technique and multiple averages are therefore acquired. Multiple averages, b-values and encoding directions, make acquisitions long. It is possible to acquire STEAM data using navigator gating and respiratory feedback, but the heart must be in a similar position in both cardiac cycles used to acquire each image(17,20,37). STEAM is, therefore, usually acquired over multiple breath holds, while MC-SE is suited to free-breathing navigator-gated acquisitions.
DT-CMR data, is processed off-line to calculate pixel-wise diffusion tensors and microstructure metrics. While neuroimaging-based DTI toolboxes exist, the geometry, sequences and protocols in the heart mean that specialized DT-CMR tools are necessary.
Data are co-registered, images with motion-related signal loss are removed and images may be filtered or denoised. The effective b-value for STEAM acquisitions is heart rate dependent and must be corrected(38) and diffusion weighting of the “b0” reference images due to spoiler and imaging gradients must be accounted for(39). The tensor is then calculated at each pixel(13).
Maps of tensor invariants including mean diffusivity, fractional anisotropy and tensor mode are calculated from the tensor. Cardiomyocyte orientation is primarily represented by the helix angle(41), calculated from the primary eigenvector(definitions in(42,43)). It is also possible to produce tractograms from multislice data(17,29,40,41). Sheetlet orientation is defined by the second or third eigenvector(42–44), but is there no current standardization.
Validation of DT-CMR has been performed ex-vivo(42,45,46) and in-vivo using STEAM(24) and MC-SE(47).
STEAM is the most established sequence(48–55) and can be used in multiple cardiac phases(56).STEAM has detected sheetlet abnormalities in cardiomyopathies(24,37,43,57), potential disarray in HCM(55,58,59), deranged microstructure in congenital disease(60) and abnormalities in infarcts(40,52,53).
Published clinical results using the relatively new MC-SE sequence are limited(61).
Comparisons show higher SNR using MC-SE than STEAM and that both techniques can be performed during contraction(62,63). DT-CMR results differ between the sequences(62,63) due to the differences in ∆(64). Noise reduces both precision and accuracy of the results(65). The majority of MC-SE studies have used high-performance gradients and diastolic MC-SE is less reliable(62,66).
Diffusion prepared imaging has demonstrated increased diffusivity in myocardial infarction(36) and correlation between extracellular volume fraction and diffusivity(35,67). Diffusion prepared DT-CMR has demonstrated limited success in acquiring tensor data in healthy and heart failure subjects(68).
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