Cardiac Diffusion Weighed & Tensor Imaging

Kévin Moulin¹
¹Boston Children's Hospital, Boston, MA, United States

Synopsis

Keywords: Contrast mechanisms: Diffusion, Cardiovascular: Cardiac

This educational presentation will focus on the technical advancements in the emerging field of cardiac diffusion imaging. Initially, we'll explore the impact of cardiac motion on the diffusion signal and examine the various diffusion encoding techniques devised to mitigate it. Additionally, we'll investigate respiratory motion and explore methods for imaging during free breathing. Subsequently, we'll delve into the fundamental principles underlying cardiac diffusion tensor reconstructions and the primary diffusion markers derivable from them. Lastly, we'll explore several clinical applications of cardiac diffusion imaging.

Target Audience

Clinicians, technologists, and scientists interested in learning the technical background on cardiac diffusion imaging.

Objectives

The basic principles behind diffusion imaging and the diffusion/motion interactions will be described.
Differences between cardiac diffusion spin-echo and Stimulated Echo sequences will be highlighted.
Respiratory management strategies will be reviewed.
Basic Tensor reconstruction and principal diffusion metrics will be presented.
Several clinical applications of cardiac Diffusion imaging will be reviewed.

Introduction

Cardiac diffusion imaging is an advanced imaging technique that allows one to delve deep into the cellular micro-architecture of the heart. Water molecules move in the tissue due to the diffusion process, but their movements are also hindered and restricted due to cardiac cellular organization. Being able to measure cardiac diffusion in vivo would provide precious insights into the cellular microstructure and its components.
The key challenge of performing cardiac diffusion in vivo is evidently cardiac motion. Traditional diffusion encoding methods are sensible to motion and lead to a corrupted diffusion signal when used in the beating heart. Two approaches, a stimulated echo EPI¹ and a spin-echo EPI techniques^2,3, have recently emerged as reliable approaches to mitigate cardiac motion in vivo. Both approaches are effective in reducing the variability due to cardiac motion but require significantly different respiratory management strategies. Another difference between stimulated and spin-echo is the diffusion encoding time which leads to different measures of diffusivity^4,5.
In this presentation, we will understand the basic principles behind diffusion encoding and its sensitivity to motion. We will look at the differences between the two main strategies to mitigate cardiac motion in diffusion encoding. We will describe the respiratory management strategies for both approaches. Then we will study basic tensor reconstruction and the diffusion metrics that can be derived from it. Lastly, we'll explore the first clinical applications of cardiac diffusion imaging.

Diffusion Signal and Cardiac Motion

Fundamentally, the diffusion encoding experiment is motion encoding exactly like flow encoding. Two gradients of opposite polarity spaced apart will encode and then decode the position of spins⁶. If spins have moved between the encoding and the decoding step, they will accumulate a phase proportional to the distance they traveled. In the flow experiment, one can simply look at the phase of spins to estimate their velocities. In the diffusion experiment, larger gradients are employed leading to a sensitivity to microscopic displacement. The nature of the motion encoded is also different, water molecules moving due to diffusion have a random, Brownian displacement pattern leading to Gaussian intra-voxel phase distribution. This intra-voxel phase distribution has an overall nulled phase and reduced magnitude, this is why diffusion is encoded in the magnitude and not in the phase like traditional motion encoding techniques. Cardiac motion which is a motion 3 order of magnitude bigger than diffusion motion can corrupt the diffusion signal and generate unwanted signal loss^7,8.

Stimulated Echo and Spin Echo sequence

Two approaches have emerged to mitigate cardiac motion during diffusion encoding. The first one, STimulated Echo Acquisition Mode (STEAM), encodes diffusion using two short diffusion encoding gradients divided over two consecutive heartbeats thus reducing its sensitivity to motion¹. It can achieve high diffusion sensitivity without requiring a high gradient system. The second one is a spin-echo-based method which uses a motion-compensated diffusion encoding design^2,3. The diffusion encoding gradients are designed such their first and second moments are nulled which nullifies the phase contribution of the water molecule with constant velocity or constant acceleration during the diffusion encoding, effectively mitigating the effect of cardiac motion. This approach is robust but requires a high gradient system.

Respiratory management

STEAM and spin-echo require different respiratory motion management. Through-plane motion due to breathing can cause signal loss with STEAM which is thus usually acquired during breath-holding⁹. For spin-echo, respiratory motion doesn’t generate signal loss which can then be used in free-breathing. Free-breathing navigated expiratory gating or triggering can be used to reduce through-plane motion but for a scanning efficiency of only ~20% of the respiratory cycle, leading to extended scan times. Another navigator-based approach, named slice-following¹⁰, allows 100% scanning efficiency by tracking the heart throughout the respiratory cycle while adapting the slice position and thus mitigating through-plane motion.

Reconstruction and Diffusion metrics

Several diffusion models can be explored with cardiac diffusion imaging. From simple Apparent Diffusion Coefficient (ADC) to Intra-voxel incoherent motion (IVIM) model10. The cardiac diffusion tensor imaging (DTI) model is the most widespread in cardiac diffusion literature. It requires the diffusion experiment to be repeated along several diffusion directions. The diffusion signal across these directions can be fitted to a tensor model from which tensor parameters can be extracted such as the mean Diffusivity (MD) or the fraction of anisotropy (FA) of the tissue. The vectors representing the principal and secondary orientation of the cardiac cells organization can also be extracted from the DTI model and parametesr such as Helix Angle (HA)¹¹ or the E2A¹² which are angular representations of the cardiac microstructure.

First clinical applications

Cardiac diffusion imaging has been evaluated in several clinical studies on Hypertrophic cardiomyopathy (HCM)^12–14, Dilated cardiomyopathy (DCM)¹⁵, Amyloidosis¹⁶, and in acute¹⁷ and chronic infarcts¹⁸.

Conclusions

Cardiac diffusion imaging is a challenging but rewarding approach. In recent years, key technical developments have enabled cardiac diffusion in vivo which is now deployed in clinical settings around the world.

Acknowledgements

No acknowledgement found.

References

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11. Moulin K, Verzhbinsky IA, Maforo NG, Perotti LE, Ennis DB. Probing cardiomyocyte mobility with multi-phase cardiac diffusion tensor MRI. Lionetti V, ed. PLoS ONE. 2020;15(11):e0241996. doi:10.1371/journal.pone.0241996

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17. Moulin K, Viallon M, Romero W, et al. MRI of Reperfused Acute Myocardial Infarction Edema: ADC Quantification versus T1 and T2 Mapping. Radiology. 2020;295(3):542-549. doi:10.1148/radiol.2020192186

18. Das A, Kelly C, Teh I, et al. Acute Microstructural Changes after ST-Segment Elevation Myocardial Infarction Assessed with Diffusion Tensor Imaging. Radiology. 2021;299(1):86-96. doi:10.1148/radiol.2021203208

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)