Faculty Bibliography
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128
Mapping regional brain total sodium concentration - using anatomically- guided reconstruction of dual echo Sodium-23 MRI: moving toward improved accuracy and precision
BACKGROUND AND PURPOSE/OBJECTIVE:Na) MRI provides unique information about ionic homeostasis in the brain. However, in vivo quantification of regional brain sodium is highly challenging due to low SNR and limited spatial resolution. Here, we employ our novel anatomically guided reconstruction (AGR) method to overcome these challenges and enable precise quantification of regional brain total sodium concentration (TSC). MATERIALS AND METHODS/METHODS:< 0.05. RESULTS:. CONCLUSIONS:The AGR helps sodium quantification in healthy human brains by reducing the partial volume effect and variance of TSC in non-cortical brain regions. Our normative values of TSC in the brain regions set the stage to better understand derangements of sodium metabolism and homeostasis in neurological disease. ABBREVIATIONS/BACKGROUND:= sodium-potassium pump; PVC= partial volume correction; PVE= partial volume effect; TSC= total sodium concentration; VH= vitreous humor.
PMID: 40854686
ISSN: 1936-959x
CID: 5910012
Single-quantum sodium MRI at 3 T for separation of mono- and bi-T2 sodium signals
Sodium magnetic resonance imaging (MRI) is highly sensitive to cellular ionic balance due to tenfold difference in sodium concentration across membranes, actively maintained by the sodium-potassium (Na+-K+) pump. Disruptions in this pump or membrane integrity, as seen in neurological disorders like epilepsy, multiple sclerosis, bipolar disease, and mild traumatic brain injury, lead to increased intracellular sodium. However, this cellular-level alteration is often masked by the dominant extracellular sodium signal, making it challenging to distinguish sodium populations with mono- vs. bi-exponential transverse (T2) decays-especially given the low signal-to-noise ratio (SNR) even at an advanced clinical field of 3 Tesla. Here, we propose a novel technique that leverages intrinsic difference in T2 decays by acquiring single-quantum images at multiple echo times (TEs) and applying voxel-wise matrix inversion for accurate signal separation. Using numerical models, agar phantoms, and human subjects, we achieved high separation accuracy in phantoms (95.8% for mono-T2 and 72.5-80.4% for bi-T2) and demonstrated clinical feasibility in humans. This approach may enable early detection of neurological disorders and early assessment of treatment responses at the cellular level using sodium MRI at 3 T.
PMCID:12304196
PMID: 40721716
ISSN: 2045-2322
CID: 5903142
Direct Localization of the VIM/DRTT Using Quantitative Susceptibility Mapping in Essential Tremor: A Pilot MRI Study
BACKGROUND AND PURPOSE/OBJECTIVE:Accurate localization of the ventral intermediate nucleus (VIM) within the dentatorubrothalamic tract (DRTT) is critical for effective neurosurgical treatment of essential tremor (ET). This study evaluated the feasibility and anatomical specificity of quantitative susceptibility mapping (QSM) for direct VIM/DRTT visualization, comparing it with conventional diffusion tractography-based reconstructions. MATERIALS AND METHODS/METHODS:Twenty-seven participants (10 healthy controls, 17 ET patients) were enrolled across two institutions and imaged on 3T MRI systems. QSM-defined VIM/DRTT regions were manually segmented based on characteristic hypointense susceptibility contrast. Whole-brain diffusion tractography was performed to reconstruct the DRTT, pyramidal tract (PT), and medial lemniscus (ML) tracts. Spatial overlap between QSM-and tractography-defined VIM/DRTT regions was calculated, as well as overlap with neighboring PT and ML tracts to assess specificity. RESULTS:Two participants were excluded due to insufficient VIM/DRTT streamlines in tractography reconstruction. In healthy controls, QSM-and tractography-defined VIM/DRTT showed high spatial correspondence (left: 87.6 ± 5.1%; right: 85.3 ± 6.5%). ET patients exhibited slightly lower overlap (mean range: 71.5 - 85.1%). Overlap with neighboring PT and ML tracts was minimal (<3.3%), confirming high anatomical specificity of QSM-derived VIM/DRTT regions. CONCLUSIONS:QSM enables direct visualization of the VIM/DRTT with high spatial agreement to conventional tractography-based approaches while demonstrating minimal overlap with adjacent tracts. These findings support QSM as a complementary or standalone imaging modality for improved, patient-specific neurosurgical targeting in ET. ABBREVIATIONS/BACKGROUND:DBS = deep brain stimulation; DRTT = dentatorubrothalamic tract; ET = essential tremor; ML = medial lemniscus; MRgFUS = MR-guided focused ultrasound; VIM = ventral intermediate nucleus; PT = pyramidal tract; QSM = quantitative susceptibility mapping; WM = white matter.
PMID: 40681310
ISSN: 1936-959x
CID: 5897652
Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
PMCID:12238412
PMID: 40628696
ISSN: 2041-1723
CID: 5890702
Silent Trauma: Neuroimaging Highlights Subtle Changes from Military Blast Exposure [Comment]
PMID: 40298604
ISSN: 1527-1315
CID: 5833502
FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI
The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI with case-level labels, and its public availability aims to advance fast and quantitative machine learning research.
PMCID:11791504
PMID: 39772976
ISSN: 2638-6100
CID: 5805022
Microstructurally informed subject-specific parcellation of the corpus callosum using axonal water fraction
The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as Hofer and Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas known to consist of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC known to consist of a greater number of large diameter fibers. Based on the degree of change in ƒ along the callosum, seven callosal subregions were consistently delineated for each individual. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.
PMID: 39671086
ISSN: 1863-2661
CID: 5761922
Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition
BACKGROUND AND PURPOSE/OBJECTIVE:Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS/METHODS:In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve. RESULTS:Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth. CONCLUSIONS:To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.
PMCID:11392353
PMID: 38663992
ISSN: 1936-959x
CID: 5689582
Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor
BACKGROUND AND PURPOSE/OBJECTIVE:Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION/UNASSIGNED:In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).
PMID: 38963424
ISSN: 1432-1920
CID: 5726432
Author Correction: Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure
PMID: 39103445
ISSN: 2045-2322
CID: 5696752
