Faculty Bibliography

PURPOSE/OBJECTIVE: METHODS: RESULTS:Applied to in vivo human MRI with linear tensor encoding on a high-performance gradient system, PIPE evaluates fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3 min. CONCLUSIONS:

PURPOSE/OBJECTIVE:We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature quantification that enhance the biological specificity of imaging towards cancer. METHODS: RESULTS: CONCLUSIONS:

Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the microscopic complexity of human tissues enable the development of innovative biomarkers with unprecedented fidelity to histology. Simulation-informed dMRI has traditionally focussed on brain imaging, and it has neglected other applications, as for example body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging: the Histo-μSim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the data-driven estimation of properties such as the intrinsic extra-cellular diffusivity, cell size or cell membrane permeability. We compare Histo-μSim to metrics from well-established analytical multi-compartment models in silico, on fixed mouse tissues scanned ex vivo (kidneys, spleens, and breast tumours) and in cancer patients in vivo. Results suggest that Histo-μSim is feasible in clinical settings, and that it delivers metrics that more accurately reflect histology as compared to analytical models. In conclusion, Histo-μSim offers histologically-meaningful tissue descriptors that may increase the specificity of dMRI towards cancer, and thus play a crucial role in precision oncology.

BACKGROUND AND PURPOSE/OBJECTIVE:Mild traumatic brain injury (MTBI) is a common public health concern with potential long-term consequences, yet its underlying pathophysiology remains poorly understood. Clinical heterogeneity of individuals having diverse extent and array of symptoms has impeded the identification of reliable imaging biomarkers. Traditional group-level analyses may obscure biologically meaningful subtypes. This study uses latent class analysis (LCA) to classify MTBI subjects into symptom-defined subgroups and examines corresponding WM microstructural alterations using advanced diffusion MRI. MATERIALS AND METHODS/METHODS:Sixty-one MTBI patients within one month of injury completed the Rivermead Post-Concussion Symptoms Questionnaire (RPQ). LCA was used to identify symptom-based subgroups. Of these, 54 MTBI patients underwent multi-shell diffusion MRI and were compared with 31 controls. WM changes were assessed across subgroups using ROI-based diffusion analyses. RESULTS:LCA identified three distinct MTBI subgroups: those with minimal to no symptoms (31.5%), the cognitively symptomatic (38.9%), and the more globally symptomatic (29.6%). The three groups were associated with different patterns of diffusion MRI differences compared with controls. The cognitively symptomatic subgroup showed predominantly central WM differences, the globally symptomatic subgroup exhibited more peripheral differences with right-hemisphere predominance and sparing the corpus callosum, marked by reduced fractional anisotropy and kurtosis and elevated diffusivities, the less symptomatic subgroup demonstrated focal differences in the callosal genu, with increased fractional anisotropy and kurtosis and decreased diffusivity measures. CONCLUSIONS:MTBI comprises biologically distinct phenotypes with subgroup-specific WM signatures on diffusion MRI. Even individuals with minimal to no symptoms show WM differences compared with controls, underscoring the limitations of symptom reporting alone. Integrating symptom-based classification with advanced diffusion MRI may improve diagnostic precision to help risk stratification and provide insight into mechanisms of injury. ABBREVIATIONS/BACKGROUND:LCA = latent class analysis; MTBI = mild traumatic brain injury; RPQ = Rivermead post-concussion symptoms questionnaire.

Early diagnosis and noninvasive monitoring of neurological disorders require sensitivity to elusive cellular-level alterations that occur much earlier than volumetric changes observable with the millimeter-resolution of medical imaging modalities. Morphological changes in axons, such as axonal varicosities or beadings, are observed in neurological disorders, as well as in development and aging. Here, we reveal the sensitivity of time-dependent diffusion MRI (dMRI) to the structurally disordered axonal morphology at the micrometer scale. Scattering theory uncovers the two parameters that determine the diffusive dynamics of water along axons: the average reciprocal cross-section and the variance of long-range cross-sectional fluctuations. This theoretical development allows us to predict dMRI metrics sensitive to axonal alterations over tens of thousands of axons in seconds rather than months of simulations in a male rat model of traumatic brain injury, and is corroborated with ex vivo dMRI. Our approach bridges the gap between micrometers and millimeters in resolution, offering quantitative and objective biomarkers applicable to a broad spectrum of neurological disorders.

Oscillating gradient spin echo (OGSE) diffusion MRI (dMRI) can probe the diffusive dynamics on short time scales ≲10 ms, which translates into the sensitivity to tissue microstructure at the short length scales

Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes. This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm³ resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm (Braitenberg and Schüz, 2013), leveraging automated analysis methods. Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. A total of 128,285 myelinated axons were segmented, of which 70,321 and 2,102 were longer than 10 and 100 μm, respectively. Marked local variations in diameter (i.e., beading) and direction (i.e., undulation) were observed along the length of individual axons. Myelinated axons longer than 10 μm had inner diameters around 0.5 µm, outer diameters around 1 µm, and g-ratios around 0.5. This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.

Diffusion Magnetic Resonance Imaging (dMRI) sensitises the MRI signal to spin motion. This includes Brownian diffusion, but also flow across intricate networks of capillaries. This effect, the intra-voxel incoherent motion (IVIM), enables microvasculature characterisation with dMRI, through metrics such as the vascular signal fraction f_V or the vascular Apparent Diffusion Coefficient (ADC) D^∗. The IVIM metrics, while sensitive to perfusion, are protocol-dependent, and their interpretation can change depending on the flow regime spins experience during the dMRI measurements (e.g., diffusive vs ballistic), which is in general not known for a given voxel. These facts hamper their practical clinical utility, and innovative vascular dMRI models are needed to enable the in vivo calculation of biologically meaningful markers of capillary flow. These could have relevant applications in cancer, as in the assessment of the response to anti-angiogenic therapies targeting tumour vessels. This paper tackles this need by introducing SpinFlowSim, an open-source simulator of dMRI signals arising from blood flow within pipe networks. SpinFlowSim, tailored for the laminar flow patterns within capillaries, enables the synthesis of highly-realistic microvascular dMRI signals, given networks reconstructed from histology. We showcase the simulator by generating synthetic signals for 15 networks, reconstructed from liver biopsies, and containing cancerous and non-cancerous tissue. Signals exhibit complex, non-mono-exponential behaviours, consistent with in vivo signal patterns, and pointing towards the co-existence of different flow regimes within the same network, as well as diffusion time dependence. We also demonstrate the potential utility of SpinFlowSim by devising a strategy for microvascular property mapping informed by the synthetic signals, and focussing on the quantification of blood velocity distribution moments and of an apparent network branching index. These were estimated in silico and in vivo, in healthy volunteers scanned at 1.5T and 3T and in 13 cancer patients, scanned at 1.5T. In conclusion, realistic flow simulations, as those enabled by SpinFlowSim, may play a key role in the development of the next-generation of dMRI methods for microvascular mapping, with immediate applications in oncology.

PURPOSE/OBJECTIVE: METHODS: RESULTS: CONCLUSION/CONCLUSIONS:Numerical simulations and theory provide an interpretation of a specific feature of kurtosis time-dependence, offering a potential biomarker for in vivo evaluation of pathology by disentangling the functional (permeability) and structural (cell size) integrity in tissues. This is relevant as the time-dependent diffusion cumulants are sensitive to pathological changes in membrane integrity and cellular structure in diseases, such as ischemic stroke, tumors, and Alzheimer's disease.