Quantitative susceptibility mapping (QSM) is a physics-driven computational technique that has a high sensitivity in quantifying iron deposition based on MRI phase images. Furthermore, it has a unique ability to distinguish paramagnetic and diamagnetic contributions such as haemorrhage and calcification based on image contrast. These properties have contributed to a growing interest to use QSM not only in research but also in clinical applications. However, it is challenging to obtain high quality susceptibility map because of its ill-posed nature, especially for researchers who have less experience with QSM and the optimisation of its pipeline. In this paper, we present an open-source processing pipeline tool called SuscEptibility mapping PIpeline tool for phAse images (SEPIA) dedicated to the post-processing of MRI phase images and QSM. SEPIA connects various QSM toolboxes freely available in the field to offer greater flexibility in QSM processing. It also provides an interactive graphical user interface to construct and execute a QSM processing pipeline, simplifying the workflow in QSM research. The extendable design of SEPIA also allows developers to deploy their methods in the framework, providing a platform for developers and researchers to share and utilise the state-of-the-art methods in QSM.

Purpose: To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM.Methods: We created a digital whole‐head tissue property phantom by segmenting and postprocessing high‐resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady‐state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier‐based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion‐weighted imaging phantom data are provided, allowing future investigations of tissue‐microstructure effects in phase and QSM algorithms.Results: The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom’s properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial‐volume effects or complex distributions of frequency shifts within the voxel.Conclusion: The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

Myelin water fraction (MWF) mapping based on data fitting of a 3-pool exponential model of multi-echo gradient echo (mGRE) data using MRI shows great promises for in vivo myelin quantification. However, this multi-exponential fitting is ill-conditioned because of the similar relaxation times and frequency shifts of the various compartments. Additionally, the bound water residing in the myelin sheath of white matter is expected to have a faster longitudinal magnetisation recovery than that of the free water in the axonal and extracellular space. When the Ernst angle is used to achieve maximum SNR and improve fitting, this will introduce a T1-weighting effect to the derived MWF. In this study, we first demonstrate that diffusion-weighted imaging can be used to infer the compartmental signal properties using an analytical fibre model to achieve a robust MWF estimation. Second, we show that by incorporating a variable flip angle scheme to the mGRE acquisition with a multi-compartment relaxometry model, not only the MWF is corrected from the T1 dependency but also the fitting procedure becomes less ill-conditioned and more SNR efficient. Finally, we demonstrate these two approaches can be combined allowing higher spatial resolution MWF maps than what has been reported to date with robust MWF estimation on a small cohort.

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength = 500 mT/m, maximum slew rate = 600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13–30 ms) and b-values up to 17.5 ms/μm2. The anisotropic Kärger model was applied to estimate the apparent exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median ± interquartile range) 13 ± 8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the apparent exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex, and visual cortex exhibit longer apparent exchange times than other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared with the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in diffusion MRI (dMRI) data, which can have a substantial impact on the estimated apparent exchange time and require extra attention when comparing the results between various hardware setups.

Purpose: Ex vivo imaging is a commonly used approach to investigate the biophysical mechanism of orientation‐dependent signal phase evolution in white matter. Yet, how phase measurements are influenced by the structural alteration in the tissue after formalin fixation is not fully understood. Here, we study the effects on magnetic susceptibility, microstructural compartmentalization, and chemical exchange measurement with a postmortem formalin‐fixed whole‐brain human tissue.Methods: A formalin‐fixed, postmortem human brain specimen was scanned with multiple orientations to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. White matter samples were subsequently excised from the whole‐brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure‐related contributions in the signal phase and to validate the findings of the whole‐brain data.Results: The bulk isotropic magnetic susceptibility of ex vivo whole‐brain imaging is comparable to in vivo imaging, with noticeable enhanced nonsusceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalization phase effect were considerably reduced in the formalin‐fixed white matter specimens.Conclusions: Formalin‐fixed postmortem white matter exhibits comparable isotropic magnetic susceptibility to previous in vivo imaging findings. However, the measured phase and magnitude data of the fixed white matter tissue shows a significantly weaker orientation dependency and compartmentalization effect. Alternatives to formalin fixation are needed to better reproduce the in vivo microstructural effects in postmortem samples.