Faculty Bibliography

Diffusion MRI fiber tractography is sensitive to noise and artifacts in diffusion-weighted images, and these challenges can propagate into fiber-orientation estimation and the tractography process. In this “Did You Know” communication, we synthesize evidence that state-of-the-art preprocessing improves tractography anatomical fidelity and test-retest reproducibility compared to minimally processed data. We summarize best-practice preprocessing – including denoising, motion and eddy current correction, EPI distortion correction, and Gibbs ringing removal – along with additional and emerging steps, and highlight integrated, publicly available pipelines that implement these methods in standardized, containerized workflows. We also outline practical acquisition and data-handling considerations that maximize the benefits of modern processing, providing a foundation for reliable tractography-based studies of the brain.

PURPOSE/OBJECTIVE:Rotational invariants (RIs) are at the root of many dMRI applications. Among others, they are presented as a sensible way of reducing the dimensionality of biophysical models. While thermal noise impact on diffusion metrics has been well studied, little is known on its effect on spherical harmonics-based RI (RISH) features and derived markers. In this work, we evaluate the effect of noise on RISH features and downstream Standard Model Imaging (SMI) estimates. THEORY AND METHODS/METHODS:Using simulated and test/retest multishell MRI data, we assess the accuracy and precision of RISH features and SMI parameters in the presence of thermal noise, as well as its robustness to variations in protocol design. We further propose and evaluate correction strategies that bypass the need of rotational invariant features as an intermediate step. RESULTS:Both RISH features and SMI estimates are impacted by SNR-dependent Rician biases. However, higher-order RISH features are susceptible to a secondary noise-related source of bias, which not only depends on SNR, but also protocol and underlying microstructure. Rician bias-correcting techniques are insufficient to maximize the accuracy of RISH and SMI features, or to ensure consistency across protocols. SMI estimators that avoid RISH features by fitting the model to the directional diffusion MRI data outperform RISH-based approaches in accuracy, repeatability, and reproducibility across acquisition protocols. CONCLUSIONS:RISH features are increasingly used in dMRI analysis, yet they are prone to various sources of noise that lower their accuracy and reproducibility. Understanding the impact of noise and mitigating such biases is critical to maximize the validity, repeatability, and reproducibility of dMRI studies.

Diffusion-weighted Magnetic Resonance Imaging (dMRI) has emerged as an imaging modality of interest to measure axon diameters noninvasively. The previously observed b ^-½ power law scaling suggests that high b-value dMRI signals originate from water confined within "stick" geometries, representing impermeable cellular processes. A key assumption is that any deviation from this power law at high b-values-modeled as a non-zero perpendicular intracellular diffusivity-must be specifically axonal in origin. Recent developments in axon diameter mapping build upon such assumptions, thereby neglecting the possibility that other cellular structures, such as glial processes, may also exhibit similar "stick"-like characteristics. This explorative study investigates the validity of axon diameter mapping by evaluating its robustness to experimental variation. In particular, it compares the mapping of the axon diameter using the zeroth- (spherical mean) and second-order (spherical variance) rotationally invariant spherical harmonic (RISH) features. As a condition for validity, axon diameter should be robust to such variations in RISH order. A novel log-linear estimator with a closed-form solution for computationally efficient axon diameter mapping is introduced, which can be applied with a minimum of two high b-value measurements. Using this estimator, it was observed that axon diameter measurements vary with RISH order, suggesting that high b-value signals from non-axonal cellular sources may confound axon diameter mapping. Monte Carlo simulations show that such dependence on RISH order could be explained by the presence of glial processes. Overall, these results highlight the need for caution in the interpretation of dMRI-derived "axon" diameter.

Optimal treatment of glioma has been a subject of debate over the last few decades, since maximal resection can improve survival, whereas preservation of functional peritumoral brain tissue minimizes the risk of postoperative neurological deficits. Our preliminary study uses tractography and neural tissue microstructure modeling based on diffusion MRI to quantify progressive axonal degeneration in proximity to frontotemporal glioma. For this, we sample major white matter pathways that traverse peritumoral penumbra at two time points. The results show a pattern of decreased intra-axonal water fraction beyond anatomical MRI abnormalities, which may indicate a tumor invasion of normal-appearing white matter that potentially advocates supratotal resection.

The development of noninvasive MRI biomarkers as surrogates of histopathological features in kidney tissue requires detailed explorations of contrast. Therefore, we studied kidney diffusion kurtosis imaging (DKI) with a wide array of encodings, including flow compensation, variable directional sampling, and cardiac gating regimes. Twelve healthy volunteers underwent DKI at 5-10 diffusion weightings (b-values) ranging from 0 to 1200 smm^-2 with 12 or 30 directional samplings, bipolar or flow-compensated diffusion gradient waveforms, and at systolic or diastolic cardiac phases. DKI biomarkers, mean diffusivity (MD) and kurtosis (MK), were interrogated using a directionally robust fitting algorithm compared to conventional fits. The combination of flow compensation and cardiac triggering at the diastolic phase in the kidneys reduced flow effects on DKI. In systole, flow-compensated waveforms significantly reduced MD and MK for both cortex and medulla: cortex MD: 3.00 versus 2.55 μm² ms^-1, medulla MD: 2.80 versus 2.39 μm² ms^-1, cortex MK: 0.58 versus 0.45, and medulla MK: 0.60 versus 0.47 (all p < 0.05). Flow suppression alleviated requirements for processing the DKI at higher minimum b-values, as neither MD nor MK significantly differed at the diastolic phase for minimum b-values of 0 versus 200 smm^-2: cortex MD: 2.30 versus 2.28 μm² ms^-1, p = 0.278; medulla MD: 2.29 versus 2.28 μm² ms^-1, p = 0.437; cortex MK: 0.37 versus 0.36, p = 0.308; and medulla MK: 0.40 versus 0.40, p = 0.904. Flow-compensated waveforms mitigate cardiac and respiratory motion-related artifacts at higher diffusion encodings in addition to microcirculation effects. The robust fitting initially developed for brain DKI is highly applicable to the kidneys because it disentangles tissue-specific directional diffusion information from artifacts.

Ultra low-field (ULF) MRI is an accessible neuroimaging modality that can bridge healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio of ULF-MRI often necessitates reductions in spatial resolution and, combined with the field-dependency of MRI contrast, challenges the accurate extraction of clinically relevant brain morphology. We evaluate the current state of ULF-MRI brain volumetry utilizing techniques for enhancing spatial resolution and leveraging recent advancements in brain segmentation. This is based on the agreement between ULF and corresponding high-field (HF) MRI brain volumes, and test-retest repeatability for multiple ULF scans. In this study, we find that accurate brain volumes can be measured from ULF-MRIs when combining orthogonal imaging directions for T₂-weighted images to form a higher resolution image volume. We also demonstrate that not all orthogonal imaging directions contribute equally to volumetric accuracy and provide a recommended scan protocol given the constraints of the current technology.

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Tractography is a unique modality for the in vivo measurement of structural connectivity, crucial for understanding brain networks and neurological conditions. With increasing b-value, the diffusion-weighting signal becomes primarily sensitive to the intra-axonal signal. However, it remains unclear how tractography is affected by this observation. Here, using open-source datasets, we showed that at high b-values, DWI reduces the uncertainty in estimating fiber orientations. Specifically, we found the ratio of biologically-meaningful longer-range connections increases, accompanied with downstream impact of redistribution of connectome and network metrics. However, when going beyond b = 6000 s/mm², the loss of SNR imposed a penalty. Lastly, we showed that the data reaches satisfactory reproducibility with b-values above 1200 s/mm². Overall, the results suggest that using b-values above 2500 s/mm² is essential for more accurate connectome reconstruction by reducing uncertainty in fiber orientation estimation, supporting the use of higher b-value protocols in standard diffusion MRI scans and pipelines.