Abstract Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.
Motivation: A non-invasive amyloid beta (Aβ) imaging technique is needed for objective diagnosis and treatment monitoring of Alzheimer’s disease. Goal(s): To develop and validate an MRF-based method quantifying brain Aβ. Approach: A framework with efficient MRF data acquisition, neural network decoding, and atlas-based segmentation was implemented. A prospective analysis was conducted on external dataset to evaluate its generalizability, repeatability, and correlation with Aβ-PET measurements and clinical cognitive function tests. Results: The method showed high repeatability (CV<2%), significant correlation with Aβ-PET measurements and Montreal Cognitive Assessment test (p=0.015 and 0.020, respectively), and discriminated subject-level Aβ positivity with an AUC of 0.84 on external test set. Impact: The proposed framework is compatible with clinical 3T MRI and offers ‘one-stop’ examination in 10 minutes for patients with cognitive decline by providing structural MRI and Aβ-quantification. Its non-invasive nature facilitates longitudinal evaluation and correlates with Aβ-PET and cognitive function.
Motivation: To address the unmet need for a cross-vendor, multiparametric technique to facilitate data pooling across sites. Goal(s): To evaluate a vendor-standardized multiparametric mapping scheme based on 3D-QALAS for whole-brain T1, T2, and proton density (PD) mapping. Approach: Intra-scanner repeatability and inter-vendor reproducibility were evaluated in vivo on five different 3T systems from four vendors (GE, Philips, Siemens, and Canon). Patients with multiple sclerosis were scanned on systems from different vendors to assess the feasibility of the scheme in real-world clinical settings. Results: 3D-QALAS provided T1, T2, and PD with coefficient of variations <4.0% using 3T scanners from different manufacturers. Impact: The four major vendors used in this study constitute a considerable portion of the global installation base, demonstrating the value of cross-vendor quantitative technique 3D-QALAS for imaging in clinical sites with multiple vendors, as well as in multicenter research settings.
Multiparametric techniques compatible with multiple vendors to facilitate the pooling of data among different sites and vendors are desired. Here, we developed a vendor-standardized whole-brain multiparametric mapping scheme based on 3D-QALAS. Intra-scanner repeatability and inter-vendor reproducibility were evaluated on test-retest session data on five different 3T systems from four MRI vendors (GE, Philips, Siemens, and Canon). T1 and T2 relaxation times and proton density values derived from 3D-QALAS showed coefficient of variations of <4.0% across scanners from different vendors. Finally, we performed an inter-vendor validation on multiple sclerosis patients to assess the feasibility of the scheme in real-world clinical settings.
We developed a framework utilizing MR fingerprinting and a complex-valued neural network to detect brain amyloid burden. The tailored neural network was trained on real amyloid-PET imaging data and MR fingerprinting acquisitions to estimate PET-derived amyloid deposition from the MR fingerprinting signal evolutions. This complex-valued neural network architecture, designed to increase sensitivity to amyloid-related signals, showed a subject-level amyloid positivity classification with AUC = 0.87 in patients with cognitive decline. The proposed method enables non-invasive amyloid burden mapping, T1 and T2 mapping in a single scan, and is suitable not only for diagnosis but also for monitoring amyloid-reducing treatments.
To compare the significance of the two-compartment model, considering diffusional anisotropy with conventional diffusion analyzing methods regarding the detection of occult changes in normal-appearing white matter (NAWM) of multiple sclerosis (MS). Diffusion-weighted images (nine b-values with six directions) were acquired from 12 healthy female volunteers (22–52 years old, median 33 years) and 13 female MS patients (24–48 years old, median 37 years). Diffusion parameters based on the two-compartment model of water diffusion considering diffusional anisotropy was calculated by a proposed method. Other parameters including diffusion tensor imaging and conventional apparent diffusion coefficient (ADC) were also obtained. They were compared statistically between the control and MS groups. Diffusion of the slow diffusion compartment in the radial direction of neuron fibers was elevated in MS patients (0.121 × 10−3 mm2/s) in comparison to control (0.100 × 10−3 mm2/s), the difference being significant (P = 0.001). The difference between the groups was not significant in other comparisons, including conventional ADC and fractional anisotropy (FA) of diffusion tensor imaging. The proposed method was applicable to clinically acceptable small data. The parameters obtained by this method improved the detectability of occult changes in NAWM compared to the conventional methods. • Water diffusion was compared between the controls and multiple sclerosis patients. • A two-compartment model, considering diffusional anisotropy was selected for water diffusion analysis. • Axial and radial diffusion of fast and slow diffusion components were evaluated. • A new method was developed to obtain the metrics stably. • The metrics indicated high detectability of slight differences between the groups.
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