Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. This is the second review on the topic of g-ratio mapping using MRI. As such, it summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. Using simulations based on recently published data, this review demonstrates the relevance of the calibration step for three myelin-markers (macromolecular tissue volume, myelin water fraction, and bound pool fraction). It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest Editor

Advances in Quantitative MRI: Acquisition, Estimation, and Application

Quantitative magnetic resonance imaging (QMRI) produces images of potential MR biomarkers: measurable tissue properties related to physiological processes that characterize the onset and progression of specific disorders. Though QMRI has potential to be more diagnostic than conventional qualitative MRI, QMRI poses challenges beyond those of conventional MRI that limit its feasibility for routine clinical use. This thesis first seeks to address two of those challenges. It then applies these solutions to develop a new method for myelin water imaging, a challenging application that may be specifically indicative of certain white matter (WM) disorders. One challenge that presently precludes widespread clinical adoption of QMRI involves long scan durations: to disentangle potential biomarkers from nuisance MR contrast mechanisms, QMRI typically requires more data than conventional MRI and thus longer scans. Even allowing for long scans, it has previously been unclear how to systematically tune the "knobs" of MR acquisitions to reliably enable precise biomarker estimation. Chapter 4 formalizes these challenges as a min-max optimal acquisition design problem and solves this problem to design three fast steady-state (SS) acquisitions for precise T1/T2 estimation, a popular QMRI application. The resulting optimized acquisition designs illustrate that acquisition design can enable new biomarker estimation techniques from established MR pulse sequences, a fact that subsequent chapters exploit. Another QMRI challenge involves the typically nonlinear dependence of MR signal models on the underlying biomarkers: these nonlinearities cause conventional likelihood-based estimators to either scale very poorly with the number of unknowns or risk producing suboptimal estimates due to spurious local minima. Chapter 5 instead introduces a fast, general method for dictionary-free QMRI parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. Chapter 5 demonstrates PERK for T1/T2 estimation using one of the acquisitions optimized in Chapter 4. Simulations as well as single-slice phantom and in vivo experiments demonstrated that PERK and two well-suited maximum-likelihood (ML) estimators produce comparable T1/T2 estimates, but PERK is consistently at least 140x faster. Similar comparisons to an ML estimator in a more challenging problem (Chapter 6) suggest that this 140x acceleration factor will increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel. Chapter 6 applies ideas developed in previous chapters to design a new fast method for imaging myelin water content, a potential biomarker for healthy myelin. It first develops a two-compartment dual-echo steady-state (DESS) signal model and then uses a Bayesian variation of acquisition design (Chapter 4) to optimize a new DESS acquisition for precise myelin water imaging. The precision-optimized acquisition is as fast as conventional SS myelin water imaging acquisitions, but enables 2-3x better expected coefficients of variation in fast-relaxing fraction estimates. Simulations demonstrate that PERK (Chapter 5) and ML fast-relaxing fraction estimates from the proposed DESS acquisition exhibit comparable root mean-squared errors, but PERK is more than 500x faster. In vivo experiments are to our knowledge the first to demonstrate lateral WM myelin water content estimates from a fast (3m15s) SS acquisition that are similar to conventional estimates from a slower (32m4s) MESE acquisition.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147486/1/gnataraj_1.pd

High efficiency, low distortion 3D diffusion tensor imaging with variable density spiral fast spin echoes (3D DW VDS RARE)

We present an acquisition and reconstruction method designed to acquire high resolution 3D fast spin echo diffusion tensor images while mitigating the major sources of artifacts in DTI-field distortions, eddy currents and motion. The resulting images, being 3D, are of high SNR, and being fast spin echoes, exhibit greatly reduced field distortions. This sequence utilizes variable density spiral acquisition gradients, which allow for the implementation of a self-navigation scheme by which both eddy current and motion artifacts are removed. The result is that high resolution 3D DTI images are produced without the need for eddy current compensating gradients or B_0 field correction. In addition, a novel method for fast and accurate reconstruction of the non-Cartesian data is employed. Results are demonstrated in the brains of normal human volunteers

3D single breath-hold MR methodology for measuring cardiac parametric mapping at 3T

Mención Internacional en el título de doctorOne of the foremost and challenging subfields of MRI is cardiac magnetic resonance imaging (CMR). CMR is becoming an indispensable tool in cardiovascular medicine by acquiring data about anatomy and function simultaneously. For instance, it allows the non-invasive characterization of myocardial tissues via parametric mapping techniques. These mapping techniques provide a spatial visualization of quantitative changes in the myocardial parameters. Inspired by the need to develop novel high-quality parametric sequences for 3T, this thesis's primary goal is to introduce an accurate and efficient 3D single breath-hold MR methodology for measuring cardiac parametric mapping at 3T. This thesis is divided into two main parts: i) research and development of a new 3D T1 saturation recovery mapping technique (3D SACORA), together with a feasibility study regarding the possibility of adding a T2 mapping feature to 3D SACORA concepts, and ii) research and implementation of a deep learning-based post-processing method to improve the T1 maps obtained with 3D SACORA. In the first part of the thesis, 3D SACORA was developed as a new 3D T1 mapping sequence to speed up T1 mapping acquisition of the whole heart. The proposed sequence was validated in phantoms against the gold standard technique IR-SE and in-vivo against the reference sequence 3D SASHA. The 3D SACORA pulse sequence design was focused on acquiring the entire left ventricle in a single breath-hold while achieving good quality T1 mapping and stability over a wide range of heart rates (HRs). The precision and accuracy of 3D SACORA were assessed in phantom experiments. Reference T1 values were obtained using IR-SE. In order to further validate 3D SACORA T1 estimation accuracy and precision, T1 values were also estimated using an in-house version of 3D SASHA. For in-vivo validation, seven large healthy pigs were scanned with 3D SACORA and 3D SASHA. In all pigs, images were acquired before and after administration of MR contrast agent. The phantom results showed good agreement and no significant bias between methods. In the in-vivo experiments, all T1-weighted images showed good contrast and quality, and the T1 maps correctly represented the information contained in the T1-weighted images. Septal T1s and coefficients of variation did not considerably differ between the two sequences, confirming good accuracy and precision. 3D SACORA images showed good contrast, homogeneity and were comparable to corresponding 3D SASHA images, despite the shorter acquisition time (15s vs. 188s, for a heart rate of 60 bpm). In conclusion, the proposed 3D SACORA successfully acquired a whole-heart 3D T1 map in a single breath-hold at 3T, estimating T1 values in agreement with those obtained with the IR-SE and 3D SASHA sequences. Following the successful validation of 3D SACORA, a feasibility study was performed to assess the potential of modifying the acquisition scheme of 3D SACORA in order to obtain T1 and T2 maps simultaneously in a single breath-hold. This 3D T1/T2 sequence was named 3D dual saturation-recovery compressed SENSE rapid acquisition (3D dual-SACORA). A phantom of eight tubes was built to validate the proposed sequence. The phantom was scanned with 3D dual-SACORA with a simulated heart rate of 60 bpm. Reference T1 and T2 values were estimated using IR-SE and GraSE sequences, respectively. An in-vivo study was performed with a healthy volunteer to evaluate the parametric maps' image quality obtained with the 3D dual-SACORA sequence. T1 and T2 maps of the phantom were successfully obtained with the 3D dual-SACORA sequence. The results show that the proposed sequence achieved good precision and accuracy for most values. A volunteer was successfully scanned with the proposed sequence (acquisition duration of approximately 20s) in a single breath-hold. The saturation time images and the parametric maps obtained with the 3D dual-SACORA sequence showed good contrast and homogeneity. The septal T1 and T2 values are in good agreement with reference sequences and published work. In conclusion, this feasibility study's findings open the door to the possibility of using 3D SACORA concepts to develop a successful 3D T1/T2 sequence. In the second part of the thesis, a deep learning-based super-resolution model was implemented to improve the image quality of the T1 maps of 3D SACORA, and a comprehensive study of the performance of the model in different MR image datasets and sequences was performed. After careful consideration, the selected convolutional neural network to improve the image quality of the T1 maps was the Residual Dense Network (RDN). This network has shown outstanding performance against state-of-the-art methods on benchmark datasets; however, it has not been validated on MR datasets. In this way, the RDN model was initially validated on cardiac and brain benchmark datasets. After this validation, the model was validated on a self-acquired cardiac dataset and on improving T1 maps. The RDN model improved the images successfully for the two benchmark datasets, achieving better performance with the brain dataset than with the cardiac dataset. This result was expected as the brain images have more well-defined edges than the cardiac images, making the resolution enhancement more evident. On the self-acquired cardiac dataset, the model also obtained an enhanced performance on image quality assessment metrics and improved visual assessment, particularly on well-defined edges. Regarding the T1 mapping sequences, the model improved the image quality of the saturation time images and the T1 maps. The model was able to enhance the T1 maps analytically and visually. Analytically, the model did not considerably modify the T1 values while improving the standard deviation in both myocardium and blood. Visually, the model improved the T1 maps by removing noise and motion artifacts without losing resolution on the edges. In conclusion, the RDN model was validated on three different MR datasets and used to improve the image quality of the T1 maps obtained with 3D SACORA and 3D SASHA. In summary, a 3D single breath-hold MR methodology was introduced, including a ready to-go 3D single breath-hold T1 mapping sequence for 3T (3D SACORA), together with the ideas for a new 3D T1/T2 mapping sequence (3D dual-SACORA); and a deep learning-based post-processing implementation capable of improving the image quality of 3D SACORA T1 maps.This thesis has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N722427.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Carlos Alberola López.- Secretario: María Jesús Ledesma Carbayo.- Vocal: Nathan Mewto