Faculty Bibliography

BACKGROUND:Identifying early neuropathological changes in Alzheimer's disease (AD) is important for improving treatment efficacy. Among quantitative MRI measures, transverse relaxation time (T2) has been shown to reflect tissue microstructure relevant in aging and neurodegeneration; however, findings regarding T2 changes in both normal aging and AD have been inconsistent. The association between T2 and amyloid-beta (Aβ) accumulation, a hallmark of AD pathology, is also unclear, particularly in cognitively normal individuals who may be in preclinical stages of the disease. PURPOSE/OBJECTIVE:To investigate longitudinal hippocampal T2 changes in a cognitively normal cohort of older adults and their association with global Aβ accumulation. STUDY TYPE/METHODS:Retrospective, longitudinal. SUBJECTS/METHODS:56 cognitively normal adults between 55 and 90 years of age (17 males and 39 females). FIELD STRENGTH/SEQUENCE/UNASSIGNED:3 Tesla; multi-echo spin echo sequence for T2 mapping; 18F-florbetaben positron emission tomography for Aβ measurement. ASSESSMENT/RESULTS:Bilateral hippocampal T2 and volume were extracted to relate to Aβ PET measurements. To understand variations in AD risk, participants were separated into Aβ-high and Aβ-low subgroups using a predetermined threshold. STATISTICAL TESTS/METHODS:Linear mixed-effect models and general linear models were used. A p-value < 0.025 was considered significant to account for bilateral comparisons. RESULTS:Older age was associated with increased T2 in the bilateral hippocampus (left: β = 0.30, right: β = 0.25) and smaller hippocampal volume on the left (β = -0.12). In the Aβ-low subgroup, both longitudinal T2 increase rates (β = 0.65) in the left hippocampus and bilateral cross-sectional T2 (left: β = 0.64, right: β = 0.46) were positively correlated with Aβ PET, independent of hippocampal volume. DATA CONCLUSION/CONCLUSIONS:This study provided in vivo evidence linking hippocampal T2 to Aβ accumulation in cognitively normal aging individuals, suggesting that quantitative T2 may be sensitive to microstructural changes accompanying early Aβ pathology, such as neuroinflammation, demyelination, and reduced tissue integrity. EVIDENCE LEVEL/METHODS:3. TECHNICAL EFFICACY/UNASSIGNED:Stage 2.

BACKGROUND:Molecular imaging, particularly positron emission tomography (PET), has significantly advanced the diagnosis and management of disease by visualizing biological processes at a cellular and molecular level. PET imaging of the brain, spine, and head/neck, summarized under the umbrella term Neuro-PET, enables noninvasive diagnosis and monitoring of diseases such as dementia, epilepsy, cancer, movement, or autoimmune disorders. The rising prevalence of these conditions, as well as new treatment options necessitating response assessment, are expected to escalate Neuro-PET imaging volumes, with projections for a significant increase in the need for specialized imaging services. This increasing clinical need highlights existing workforce shortages and underscores the need for neuroradiologists to acquire proficiency in molecular imaging. This expanded role seeks to address the growing demand. To this end, we propose a rigorous, structured, patient-centered, and collaborative framework for expanding neuroradiologists' training and practice to include Neuro-PET interpretation. METHODS:This ASNR consensus statement outlines competency recommendations, training pathways, and implementation strategies to incorporate Neuro-PET into neuroradiology practice. This approach is based on existing guidelines and was informed by survey data from neuroradiologists and molecular imaging subspecialists revealing current practice patterns and training needs. For neuroradiology fellows, structured training encompasses hands-on Neuro-PET imaging experience, understanding the biologic and molecular basis of radiopharmaceuticals used in Neuro-PET, and integrating molecular insights with anatomical data. Neuroradiologists beyond fellowship can undertake practice-based curriculum involving supervised case interpretation, standardized reader training courses, continuing medical education (CME), and peer review. KEY MESSAGE/CONCLUSIONS:Neuroradiologists, with their in-depth expertise of central nervous system structure and function, are well positioned to meld molecular imaging data with traditional anatomical findings. They can achieve competency and should be granted practice privileges in interpreting Neuro-PET studies through a comprehensive combination of structured training, hands-on clinical experience, and documented CME hours. ABBREVIATIONS/BACKGROUND:PET = positron emission tomography; CME = continuing medical education; ACR= American College of Radiology.

The prevalence of Alzheimer's disease and other dementias is increasing as populations live longer lifespans. Imaging is becoming a key component of the workup for patients with cognitive impairment or dementia. Integrated PET-MRI provides a unique opportunity for same-session multimodal characterization with many practical benefits to patients, referring physicians, radiologists, and researchers. The impact of integrated PET-MRI on clinical practice for early adopters of this technology can be profound. Classic imaging findings with integrated PET-MRI are illustrated for common neurodegenerative diseases or clinical-radiological syndromes. This review summarizes recent technical innovations that are being introduced into PET-MRI clinical practice and research for neurodegenerative disease. More recent MRI-based attenuation correction now performs similarly compared to PET-CT (e.g., whole-brain bias < 0.5%) such that early concerns for accurate PET tracer quantification with integrated PET-MRI appear resolved. Head motion is common in this patient population. MRI- and PET data-driven motion correction appear ready for routine use and should substantially improve PET-MRI image quality. PET-MRI by definition eliminates ~50% of the radiation from CT. Multiple hardware and software techniques for improving image quality with lower counts are reviewed (including motion correction). These methods can lower radiation to patients (and staff), increase scanner throughput, and generate better temporal resolution for dynamic PET. Deep learning has been broadly applied to PET-MRI. Deep learning analysis of PET and MRI data may provide accurate classification of different stages of Alzheimer's disease or predict progression to dementia. Over the past 5 years, clinical imaging of neurodegenerative disease has changed due to imaging research and the introduction of anti-amyloid immunotherapy-integrated PET-MRI is best suited for imaging these patients and its use appears poised for rapid growth outside academic medical centers. Evidence level: 5. Technical efficacy: Stage 3.

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.

Fahr disease is a rare neurodegenerative syndrome characterized by abnormal symmetric calcium deposition in the white matter, cerebral cortex, deep gray, and cerebellar nuclei. The characteristic CT pattern is well known, but descriptions of molecular imaging in Fahr disease remain sparse. We present the characteristic imaging patterns of Fahr disease by CT, I-123-Ioflupane SPECT, and integrated FDG PET/MRI in a single patient.

The clinical translation of diffusion magnetic resonance imaging (dMRI)-derived quantitative contrasts hinges on robust reproducibility, minimizing both same-scanner and cross-scanner variability. As multi-site data sets, including multi-shell dMRI, expand in scope, enhancing reproducibility across variable MRI systems and MRI protocols becomes crucial. This study evaluates the reproducibility of diffusion kurtosis imaging (DKI) metrics (beyond conventional diffusion tensor imaging (DTI)), at the voxel and region-of-interest (ROI) levels on magnitude and complex-valued dMRI data, using denoising with and without harmonization. We compared same-scanner, cross-scanner, and cross-protocol variability for a multi-shell dMRI protocol (2-mm isotropic resolution, b = 0, 1000, 2000 s/mm²) in 20 subjects. We first evaluated the effectiveness of Marchenko-Pastur Principal Component Analysis (MPPCA) based denoising strategies for both magnitude and complex data to mitigate noise-induced bias and variance, to improve dMRI parametric maps and reproducibility. Next, we examined the impact of denoising under different population analysis approaches, specifically comparing voxel-wise versus region of interest (ROI)-based methods. We also evaluated the role of denoising when harmonizing dMRI across scanners and protocols. The results indicate that DTI and DKI maps visually improve after MPPCA denoising, with noticeably fewer outliers in kurtosis maps. Denoising, either using magnitude or complex dMRI, enhances voxel-wise reproducibility, with test-retest variability of kurtosis indices reduced from 15%-20% without denoising to 5%-10% after denoising. Complex dMRI denoising reduces the noise floor by up to 60%. Denoising not only reduced variability across scans and protocols, but also increased statistical power for low SNR voxel-wise comparisons when comparing cross sectional groups. In conclusion, MPPCA denoising, either over magnitude or complex dMRI data, enhances the reproducibility and precision of higher-order diffusion metrics across same-scanner, cross-scanner, and cross-protocol assessments. The enhancement in data quality and precision facilitates the broader application and acceptance of these advanced imaging techniques in both clinical practice and large-scale neuroimaging studies.

Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral to both research and clinical workflows. While brain lesions have gained significant attention in MS research, the involvement of spinal cord lesions in MS is relatively understudied. This is largely owing to the variability in spinal cord magnetic resonance imaging (MRI) acquisition protocols, high individual anatomical differences, the complex morphology and size of spinal cord lesions, and lastly, the scarcity of labeled datasets required to develop robust segmentation tools. As a result, automatic segmentation of spinal cord MS lesions remains a significant challenge. Although some segmentation tools exist for spinal cord lesions, most have been developed using sagittal T2-weighted (T2w) sequences primarily focusing on cervical spines. With the growing importance of spinal cord imaging in MS, axial T2w scans are becoming increasingly relevant due to their superior sensitivity in detecting lesions compared to sagittal acquisition protocols. However, most existing segmentation methods struggle to effectively generalize to axial sequences due to differences in image characteristics caused by the highly anisotropic spinal cord scans. To address these challenges, we developed a robust, open-source lesion segmentation tool tailored specifically for axial T2w scans covering the whole spinal cord. We investigated key factors influencing lesion segmentation, including the impact of stitching together individually acquired spinal regions, straightening the spinal cord, and comparing the effectiveness of 2D and 3D convolutional neural networks (CNNs). Drawing on these insights, we trained a multi-center model using an extensive dataset of 582 MS patients, resulting in a dataset comprising an entirety of 2,167 scans. We empirically evaluated the model's segmentation performance across various spinal segments for lesions with varying sizes. Our model significantly outperforms the current state-of-the-art methods, providing consistent segmentation across cervical, thoracic, and lumbar regions. To support the broader research community, we integrate our model into the widely-used Spinal Cord Toolbox (v7.0 and above), making it accessible via the command sct_deepseg lesion_ms_axial_t2 -i <path-to-image.nii.gz>.

PURPOSE/OBJECTIVE:maps from fewer echoes. METHODS:mapping was evaluated in seven experiments. RESULTS:estimation. CONCLUSIONS:estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.

OBJECTIVES/OBJECTIVE:The aim of this study was to determine whether MRI radiomic features of key cerebral structures differ between women and men, and whether detection of such differences depends on the image resolution. MATERIALS AND METHODS/METHODS:Ultrahigh resolution (UHR) 3D MP2RAGE (magnetization-prepared 2 rapid acquisition gradient echo) T1-weighted MR images (voxel size, 0.7 × 0.7 × 0.7 mm3) of the brain of 30 subjects (18 women and 12 men; mean age, 39.0 ± 14.8 years) without abnormal findings on MRI were retrospectively included. MRI was performed on a whole-body 7 T MR system. A convolutional neural network was used to segment the following structures: frontal cortex, frontal white matter, thalamus, putamen, globus pallidus, caudate nucleus, and corpus callosum. Eighty-seven radiomic features were extracted respectively: gray-level histogram (n = 18), co-occurrence matrix (n = 24), run-length matrix (n = 16), size-zone matrix (n = 16), and dependence matrix (n = 13). Feature extraction was performed at UHR and, additionally, also after resampling to 1.4 × 1.4 × 1.4 mm3 voxel size (standard clinical resolution). Principal components (PCs) of radiomic features were calculated, and independent samples t tests with Cohen d as effect size measure were used to assess differences in PCs between women and men for the different cerebral structures. RESULTS:At UHR, at least a single PC differed significantly between women and men in 6/7 cerebral structures: frontal cortex (d = -0.79, P = 0.042 and d = -1.01, P = 0.010), frontal white matter (d = -0.81, P = 0.039), thalamus (d = 1.43, P < 0.001), globus pallidus (d = 0.92, P = 0.020), caudate nucleus (d = -0.83, P = 0.039), and corpus callosum (d = -0.97, P = 0.039). At standard clinical resolution, only a single PC extracted from the corpus callosum differed between sexes (d = 1.05, P = 0.009). CONCLUSIONS:Nonnegligible differences in radiomic features of several key structures of the brain exist between women and men, and need to be accounted for. Very high spatial resolution may be required to uncover and further investigate the sexual dimorphism of brain structures on MRI.