Faculty Bibliography
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132
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
Impact of dataset size on fine-tuning foundation models for neuroanatomic segmentation: Testing the foundation model hypothesis
BACKGROUND:Foundation models have shown remarkable potential in medical imaging by leveraging extensive pretraining on general datasets to enable fine-tuning for specific tasks. This is thought to be particularly beneficial for tasks where annotated data is scarce. A key underlying assumption, however, is that these models can learn from small amounts of training data more efficiently than existing state-of-the-art models. PURPOSE/OBJECTIVE:This study aims to characterize the performance of two major foundation segmentation models (SAM and MedSAM) when fine-tuned to segment neuroanatomic structures across a spectrum of dataset sizes, compared to a standard fully-supervised UNet model. METHODS:This study used 1,113 T1-weighted 3D MRIs from the Human Connectome Project's Young Adult cohort with corresponding Freesurfer-generated, manually-refined segmentations of 93 gray and white matter regions. The dataset was divided into 891 (80%) training MRIs, 111 (10%) validation MRIs, and 111 (10%) testing MRIs. SAM and MedSAM models were first fine-tuned and compared against a standard UNet model using Dice score to establish the baseline performance using all training 3D volumes. Subsequently, MedSAM and UNet models were fine-tuned across a varying number of training volumes to assess performance with diminishing dataset size, down to a single MRI, as well as no MRIs (zero-shot) for the MedSAM and SAM models. RESULTS:Using the entire training set, UNet outperformed MedSAM and SAM across most regions, with median Dice scores of 0.88 versus 0.82 and 0.84, respectively (p < 0.001). With diminishing dataset size, UNet continued to perform as well as or better than MedSAM in the three studied regions, down to even a single 3D volume. In the zero-shot setting, SAM and MedSAM showed some ability to segment with overall median Dice scores of 0.66 and 0.59, respectively. CONCLUSIONS:SAM and MedSAM did not outperform a standard UNet model in segmentation tasks, even in extremely limited training data settings, contrary to the foundation model hypothesis, suggesting that foundation models do not necessarily yield superior fine-tuned performance compared to standard segmentation models in the low data setting. Instead, the potential benefit of foundation models will depend on the characteristics of the task at hand and the behavior and capacity of the specific foundation model in question. Thus, it will be essential to benchmark against standard supervised deep learning methods for each distinct application to demonstrate the added value of using a foundation model.
PMID: 41699958
ISSN: 2473-4209
CID: 6004472
The Role of MRI in Debunking the Fallacy of "Mild" Traumatic Brain Injury
Mild traumatic brain injury (mTBI) is a prevalent yet often overlooked public health concern due to the absence of detectable abnormalities on CT or conventional MRI scans. Approximately 18.3%-31.3% of mTBI patients experience persistent symptoms 3-6 months post-injury, despite normal imaging results, making diagnosis and treatment challenging. In recent years, advanced neuroimaging modalities have emerged with the potential to reveal subtle physiological and structural brain changes that are invisible to traditional imaging. Diffusion MRI (dMRI), for instance, is particularly valuable for detecting white matter injury; perfusion MRI assesses alterations in cerebral blood flow; sodium MRI (23Na MRI) provides insights into ionic homeostasis; and functional MRI (fMRI) detects disruptions in functional brain network connectivity. In this review, we first explore the underlying mechanisms of mTBI and then summarize current evidence supporting the use of advanced MRI techniques to detect injury signatures associated with these mechanisms. Finally, we highlight populations at heightened risk for repeated injuries-underscoring the urgent need for more sensitive diagnostic tools that can identify injury early, guide return-to-activity decisions, and prevent cumulative brain damage. EVIDENCE LEVEL: N/A. TECHNICAL EFFICACY: Stage 3.
PMID: 40911393
ISSN: 1522-2586
CID: 5985712
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
Linking Symptom Phenotypes to Patterns of White Matter Injury in Mild Traumatic Brain Injury: A Latent Class Analysis
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.
PMID: 41203427
ISSN: 1936-959x
CID: 5960522
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
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
