In Vivo Quantification of Cardiac Microstructure with Convex Optimized Diffusion Weighted MRI
- Author(s): Aliotta, Eric
- Advisor(s): Ennis, Daniel B.
- et al.
Diffusion weighted imaging (DWI) is a powerful quantitative magnetic resonance imaging (MRI) technique that can probe tissues in vivo at the microscopic level and provide insight into cellular microstructural environment. Cardiac DWI has great potential value in its ability to answer open questions regarding myocardial structure, dynamics, and remodeling. Unfortunately, several technical limitations of current DWI techniques make its application in the beating heart very challenging, which leads to erroneous or inconsistent results. Amongst the challenges are an extreme sensitivity to bulk physiological motion, low signal to noise ratios (SNR), long scan times, and geometric image distortions. In this dissertation, these limitations are addressed with novel technical developments applied to the DWI pulse sequence including convex optimized diffusion gradient waveform design and multi-parametric tissue characterization.
A brief introduction to Nuclear Magnetic Resonance (NMR) and MRI is provided in Chapter 1. This leads into a description of the fundamental components of a DWI acquisition in Chapter 2 and an overview of the current state of cardiac DWI in Chapter 3.
In Chapter 4, a novel DWI strategy called Convex Optimized Diffusion Encoding (CODE) is described. CODE is a mathematical framework that formulates diffusion encoding gradient design as a convex optimization problem and automatically generates motion compensated (MOCO) waveforms that achieve the shortest possible echo times (TE) and thus improve SNR. First and second order moment nulled CODE (CODE-M1M2) permits DWI that is robust to cardiac motion with higher SNR than an existing MOCO technique. First order motion compensated CODE-M1 also improves robustness to cardiac induced motion in liver DWI with higher SNR than M1 nulled bipolar DWI. CODE can also be used for non-motion compensated DWI and improves SNR compared with traditional monopolar DWI in the brain.
In Chapter 5 we present a multi-parametric DWI strategy that simultaneously yields maps of the apparent diffusion coefficient (ADC) and T2 relaxation time constant in the heart (T2+ADC). Typically, DWI protocols include multiple acquisitions with a range of diffusion encoding strengths (b-value), but with constant TE to isolate the effect of diffusion of the signal. The joint T2+ADC approach varies both b-value and TE within the acquisition to facilitate estimation of both ADC and T2 relaxation. T2+ADC permits joint reconstruction with no increase in scan time compared with DWI alone and no effect on ADC measurement.
In Chapter 6 we use CODE-M1M2 diffusion encoding to perform cardiac diffusion tensor imaging (cDTI) and generate maps of myocardial microstructure in healthy volunteers. cDTI can be used to map myocardial fiber and myolaminar sheetlet orientations, which can contribute to our understanding of ventricular microstructure in health and disease and facilitate sophisticated mechanical models of cardiac dynamics. However, it is important to understand the uncertainty underlying these measurements to inform interpretation and define acquisition limitations. We apply a previously described bootstrap technique to measure the uncertainty in the diffusion tensors derived from CODE-M1M2 cDTI and establish achievable levels of precision in clinically feasible scan times.
In Chapter 7 the CODE framework is extended to compensate for the effect of eddy currents, which are a common cause of image distortions in DWI and DTI. Diffusion encoding gradients must be very strong to encode microscopic molecular displacements and these strong gradient pulses induce unwanted eddy currents in conductive MRI hardware components. If not addressed, eddy currents lead to distorted images and corrupted diffusion parameter estimates. We incorporate an eddy current model into the CODE optimization framework to develop eddy current nulled CODE (EN-CODE). EN-CODE accomplishes eddy current nulling with TEs that are comparable to traditional monopolar encoding and much shorter than the established twice refocused spin echo (TRSE) technique for eddy current nulling.
The developments described in this dissertation represent an improvement in the flexibility, efficiency, and robustness of diffusion encoding. The CODE framework can also be easily modified to address additional constraints and thus may prove useful in currently unforeseen applications.