Faculty Bibliography

BACKGROUND AND PURPOSE/OBJECTIVE: MATERIALS AND METHODS/METHODS: RESULTS: CONCLUSIONS:The dynamics of the CVR response can reveal differential and previously occult temporal signatures in the pharmacologic response of white matter to ACZ, suggesting potential interactions between macrovascular and microvascular hemodynamic impairment.

PURPOSE/OBJECTIVE:Ultra-low-field (ULF) MRI offers a promising path to accessible neuroimaging, with potential to address global healthcare disparities and advance population-level brain health research. However, the inherently low signal-to-noise ratio (SNR), reduced spatial resolution, and altered tissue contrasts relative to conventional high-field (HF) scans are significant barriers to ULF analysis and interpretation. While deep learning (DL) approaches have been proposed to enhance ULF image quality, many rely on synthetic training data due to the lack of available subject-matched ULF and HF scans, introducing potential "domain shift" errors when applied to real acquisitions. Here, we present a DL framework trained on real ULF and HF-MRIs to address these limitations and improve ULF-derived brain volume analysis. METHODS:A CycleGAN framework was developed for image-to-image translation across field strengths, while mitigating the need for large subject-matched ULF- and HF-MRIs. This approach enabled pretraining on large open-access MRI datasets followed by fine-tuning on real ULF scans. Model performance was evaluated through downstream brain volumetric analysis, assessing volumetric agreement with HF-derived measurements and test-retest reproducibility. We additionally explored a framework to reduce input acquisition requirements, improving scan protocol efficiency while preserving enhanced performance. RESULTS:The proposed methods significantly improved hippocampal volumetric agreement and brain segmentation accuracy between ULF- and HF-MRI compared with existing strategies. Test-retest reproducibility for DL-enhanced images was superior to that of direct segmentation on ULF scans. CONCLUSION/CONCLUSIONS:The proposed framework substantially improved the accuracy and reliability of ULF-derived brain volume measurements, particularly for subcortical structures such as the hippocampus.

Tractography is a key component of efforts to map brain connectivity. As a rapidly evolving field of neuroscience, current tractography methods are diverse, often varying across research laboratories and different software pipelines. Therefore, it suffers from a lack of standardization, leading to inconsistencies in results, which can limit reproducibility and affect the robustness needed for research and clinical applications of these methods. Variability in data acquisition procedures, inconsistencies in spatial referencing schemes and implementations, and anatomical heterogeneity-at the individual level, across the lifespan, and across species-hinder comparative analyses. Additionally, the lack of consensus on best practices complicates the development of robust automated quality control pipelines and limits the clinical translation of tractography-based procedures. Establishing standardized protocols for acquisition, preprocessing, and tractography reconstruction is critical toward enabling reliable tract-specific analyses, facilitating cross-study harmonization, and supporting replicable large-scale population studies. The present article provides an overview of the current challenges in tractography standardization and identifies the key aspects that require standardization for reliable, reproducible, and robust tractography.

Integrated electronic health record databases provide an unprecedented opportunity to enhance knowledge of disease prediction, prevention, and management in real-world settings. The Precision Medicine (PMED) Registry is a cohort of approximately 2 million patients seen at NYU Langone Health inpatient and outpatient centers, capturing data generated during clinical care from January 1, 2010, to the present, with regular data updates. Data have been used for several research investigations, including international meta-analyses, validation of disease identification algorithms, local evaluation of risk tools, testing analytical pipelines for imaging data, and the investigation of novel correlates of established risk prediction models. Additionally, the assessment of local practice has provided insights into clinical practice patterns and aided quality improvement efforts to assess and promote the uptake of guideline-directed therapies at the system and provider level. This study illustrates how real-world integrated electronic health record data with multi-modal clinical information can be leveraged to support research in prediction, diagnosis, prevention, and treatment optimization across health systems.

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.

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.

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.

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.

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.