Diffusion MRI of the prostate at 300 mT/m

Diffusion magnetic resonance imaging gained worldwide recognition as the chief non-invasive mean of mapping microstructural properties of biological tissue. By far, the most common clinical application of this method is diagnosing conditions such as stroke and cancer. Prostate cancer is one of the leading cancer-related causes of deaths among men. Recently, MRI became a triage test in the diagnostic pathway for men suspected of having prostate cancer. While providing a very useful contrast, limited gradient power of clinical MR hardware prevents it from capturing the whole range of microstructural changes. For that reason, one research priority is to provide novel MRI-based biomarkers for increased efficacy of its early diagnosis. Assessing early microstructural changes in the prostate is of fundamental importance, as it can increase patients’ chance of survival. Novel technological developments can be used when trying to address the issues with limited spatial resolution, sensitivity and specificity of the clinical MRI scans. The work presented in this thesis focused on the use of one of the most powerful whole-body MRI scanners - in terms of the gradient amplitude - available worldwide to advance diffusion MRI of the prostate. Advanced image reconstruction, accounting for MR field perturbations, was exploited to address concerns regarding low image quality resulting from limited MR gradient fidelity. In addition, diffusion MRI was probed at sub-millimetre spatial resolution and effectively enhanced spatial resolution of the images which is crucial for accurate localisation of cancerous lesions. The work presented in this thesis showcases the ways to obtain higher spatial resolution, and boosted signal-to-noise ratio in diffusion MRI images of the prostate, as well as increased sensitivity to subtle tissue alterations that can be observed early in prostate cancer. The preliminary results are promising, but further development of methods is required, including exploring more advanced microstructural imaging sequences and methods for analysing the data they allow to obtain

Micro-structure diffusion scalar measures from reduced MRI acquisitions

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP). In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust

In vivo myelin water quantification using diffusion–relaxation correlation MRI: A comparison of 1D and 2D methods

Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion