qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging

Robust Magnetic Resonance Imaging of Short T2 Tissues

Tissues with short transverse relaxation times are defined as ‘short T2 tissues’, and short T2 tissues often appear dark on images generated by conventional magnetic resonance imaging techniques. Common short T2 tissues include tendons, meniscus, and cortical bone. Ultrashort Echo Time (UTE) pulse sequences can provide morphologic contrasts and quantitative maps for short T2 tissues by reducing time-of-echo to the system minimum (e.g., less than 100 us). Therefore, UTE sequences have become a powerful imaging tool for visualizing and quantifying short T2 tissues in many applications. In this work, we developed a new Flexible Ultra Short time Echo (FUSE) pulse sequence employing a total of thirteen acquisition features with adjustable parameters, including optimized radiofrequency pulses, trajectories, choice of two or three dimensions, and multiple long-T2 suppression techniques. Together with the FUSE sequence, an improved analytical density correction and an auto-deblurring algorithm were incorporated as part of a novel reconstruction pipeline for reducing imaging artifacts. Firstly, we evaluated the FUSE sequence using a phantom containing short T2 components. The results demonstrated that differing UTE acquisition methods, improving the density correction functions and improving the deblurring algorithm could reduce the various artifacts, improve the overall signal, and enhance short T2 contrast. Secondly, we applied the FUSE sequence in bovine stifle joints (similar to the human knee) for morphologic imaging and quantitative assessment. The results showed that it was feasible to use the FUSE sequence to create morphologic images that isolate signals from the various knee joint tissues and carry out comprehensive quantitative assessments, using the meniscus as a model, including the mappings of longitudinal relaxation (T1) times, quantitative magnetization transfer parameters, and effective transverse relaxation (T2*) times. Lastly, we utilized the FUSE sequence to image the human skull for evaluating its feasibility in synthetic computed tomography (CT) generation and radiation treatment planning. The results demonstrated that the radiation treatment plans created using the FUSE-based synthetic CT and traditional CT data were able to present comparable dose calculations with the dose difference of mean less than a percent. In summary, this thesis clearly demonstrated the need for the FUSE sequence and its potential for robustly imaging short T2 tissues in various applications

Gradient echo-based quantitative MRI of human brain at 7T : Mapping of T1, MT saturation and local flip angle

Quantitative MRI (qMRI) refers to the process of deriving maps of MR contrast parameters, such as relaxation times, from conventional images. If the qMRI maps have a high degree of precision and a low degree of bias, they can be compared longitudinally, across subjects, and (ideally) between measurement protocols and research sites. They also provide a more direct biophysical interpretation of the pixel intensities. The increased magnetization of spins at ultra-high field (UHF) strengths of 7T and above could potentially be translated into higher spatial resolution and/or reduced scan time. This thesis tackles UHF-related challenges in qMRI, namely the increased inhomogeneity of the radio frequency (RF) field (B1) and increased specific absorption rate (SAR). The changing relaxation times (i.e. prolonged T1 and shortened T2) also needs to be accounted for.Here, spoiled gradient-recalled echo (GRE) techniques are employed to map (primarily) two structural MR parameters, i.e. the longitudinal relaxation time (T1) and the magnetization transfer (MT) saturation (MTsat). Because of its influence at UHF, emphasis is also put on mapping of the local flip angle. Primarily, qMRI is performed by the inversion of analytical signal equations, as opposed to numerical approaches.The process of implementing and modifying the dual flip angle (DFA) technique in conjunction with an MT-weighted GRE for 7T is described. Implementation is performed within the well-established multi-parameter mapping (MPM) framework and special attention is afforded to the reduction of biases as well as overcoming saftey restrictions imposed by SAR. An approach to obtain high-SNR low-bias flip angle maps at 7T, using the dual refocusing echo acquisition mode (DREAM) technique is also presented. This is important since high fidelity flip angle maps are a prerequisite in DFA-based T1-mapping and recommended for correcting MTsat at UHF. Furthermore, MPRAGE-based techniques are discussed. Firstly, it is demonstrated how to most effectively obtain B1-corrected MPRAGE images of “pure” T1 contrast using a sequential protocol This is followed by a description of T1-mapping using MP2RAGE. Finally, an innovative technique for simultaneous mapping of T1 and the local flip angle is introduced, dubbed “MP3RAGE”

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates and , proton density and magnetisation transfer saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research

Model-based multi-parameter mapping

Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.Comment: 20 pages, 6 figures, accepted at Medical Image Analysi

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

Methodological Development of a Multi-Parametric Quantitative Imaging Biomarker Framework for Assessing Treatment Response with MRI

Quantitative imaging biomarkers (QIBs) are increasingly being incorporated into early phase clinical trials as a means of non-invasively assessing the spatially heterogeneous treatment response to anticancer therapies, particularly as indicators for early response. MR QIBs are derived from the analysis of in vivo imaging data, such as that acquired via dynamic contrast enhanced (DCE), dynamic susceptibility enhanced (DSC), and diffusion tensor imaging (DTI). To date, preclinical and clinical applications of such QIBs have provided strong evidence for potential efficacy, but efforts to create meaningful estimates of localized treatment response using multiple QIBs have been stifled by the need for rigorous characterization of biases and variances inherent in MR equipment and analysis tools and a suitable means of associating QIB changes with treatment response. This research sought to develop such a framework, incorporating multiple MRI QIBs associated with the microvascular environment, e.g., permeability, flow, and volume, and the cellular environment, e.g., water diffusion, into a single classification model to generate maps of predicted locoregional response. To ensure treatment associated changes measured in vivo exceeded equipment related levels of bias and variance, two phantoms were developed. Weekly assessment of the MR imaging data from which the QIBs were derived resulted in coefficients of variation less than 15% for QIBs assessed, well below the expected treatment related changes (approximately 40%). Bias and variance associated with the software tools developed to facilitate longitudinal assessments of treatment response, QUATTRO, was also assessed using synthesized imaging data mimicking clinically relevant acquisitions schema, and found to introduce negligible levels of bias and variance. Finally, to develop an integrated approach to assessing response using multiple QIBs, two experienced radiation oncologists contoured regions of partial response (PR), stable disease (SD), and progressive disease (PD) on rigidly co-registered high grade brain tumor patient data sets, which included DCE, DSC, and DTI acquisitions. Response matched voxel-by-voxel QIBs were trained using an ordinal regression classifier. Using leave-one-out cross-validation, the prediction accuracies of the best model (single DTI QIB) were found to be, mean (standard error), 69.0 (11.1)% for SD, 35.2 (11.7)% for PD, and 52.3(9.7)% overall. In summary, this work resulted in the development of a comprehensive framework for predicting voxelwise radiological treatment response, including the development of phantoms and associated acquisitions for MR equipment quality control and establishment of system-related bias and variance, and a comprehensive software package for performing related image analyses and outcome prediction

Impact of Particle Sizes on MRI Signal Relaxation in Phantoms for Assessment of Hepatic Steatosis and Iron Overload

Magnetic Resonance Imaging (MRI) is emerging as a powerful tool to non-invasively evaluate diffuse liver diseases such as hepatic steatosis and iron overload. To test new MRI techniques, phantom studies are utilized in place of patients but often do not consider the microscopic interactions of particles suspended in the media which may cause a notable difference in the signal. Hence, this study investigates the impact of differing particle sizes on the magnetic resonance signal using phantoms. To accomplish this, steatosis phantoms were created using two different mixing methods to control droplet size and while combination iron-fat phantoms featuring iron particles of differing diameters were used to emulate hepatic iron overload. Signal behavior from both sets of phantoms were resolved using linear calibrations to determine values from two known biomarkers, fat fraction and R2*, for steatosis and iron overload, respectively. Overall, evidence showing that particle size impacts the signal to a significant degree remains inconclusive, but fitting model performance in biomarker quantification varied. This study demonstrates different sequencing and post-processing assessments are critical for the analysis of sensitive biomarkers such as R2* and FF