Motion robust MR fingerprinting scan to image neonates with prenatal opioid exposure

Background: A noninvasive and sensitive imaging tool is needed to assess the fast-evolving baby brain. However, using MRI to study non-sedated babies faces roadblocks, including high scan failure rates due to subjects motion and the lack of quantitative measures for assessing potential developmental delays. This feasibility study explores whether MR Fingerprinting scans can provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure, presenting a viable alternative to clinical MR scans. Assessment: MRF image quality was compared to pediatric MRI scans using a fully crossed, multiple reader multiple case study. The quantitative T1 and T2 values were used to assess brain tissue changes between babies younger than one month and babies between one and two months. Statistical Tests: Generalized estimating equations (GEE) model was performed to test the significant difference of the T1 and T2 values from eight white matter regions of babies under one month and those are older. MRI and MRF image quality were assessed using Gwets second order auto-correlation coefficient (AC2) with its confidence levels. We used the Cochran-Mantel-Haenszel test to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. Results: In infants under one month of age, the T1 and T2 values are significantly higher (p<0.005) compared to those between one and two months. A multiple-reader and multiple-case study showed superior image quality ratings in anatomical features from the MRF images than the MRI images. Conclusions: This study suggested that the MR Fingerprinting scans offer a motion-robust and efficient method for non-sedated infants, delivering superior image quality than clinical MRI scans and additionally providing quantitative measures to assess brain development

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

MRI of acquired Brown syndrome: a report of two cases

Brown syndrome is characterized by upward gaze impairment while the eye is in adduction. It is caused by abnormalities involving the superior oblique tendon-trochlea complex. Imaging can help confirm the diagnosis, shed light on its etiology, and determine the best course of treatment. However, reports of magnetic resonance imaging findings of acquired Brown syndrome are scarce in the literature. Here, we describe magnetic resonance imaging features of 2 cases of acquired Brown syndrome

Simultaneous T1 and T2 Brain Relaxometry in Asymptomatic Volunteers Using Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is an imaging tool that produces multiple magnetic resonance imaging parametric maps from a single scan. Herein we describe the normal range and progression of MRF-derived relaxometry values with age in healthy individuals. In total, 56 normal volunteers (24 men and 32 women) aged 11–71 years were scanned. Regions of interest were drawn on T1 and T2 maps in 38 areas, including lobar and deep white matter (WM), deep gray nuclei, thalami, and posterior fossa structures. Relaxometry differences were assessed using a forward stepwise selection of a baseline model that included either sex, age, or both, where variables were included if they contributed significantly (p &lt; 0.05). In addition, differences in regional anatomy, including comparisons between hemispheres and between anatomical subcomponents, were assessed by paired t tests. MRF-derived T1 and T2 in frontal WM regions increased with age, whereas occipital and temporal regions remained relatively stable. Deep gray nuclei such as substantia nigra, were found to have agerelated decreases in relaxometry. Differences in sex were observed in T1 and T2 of temporal regions, the cerebellum, and pons. Men were found to have more rapid age-related changes in frontal and parietal WM. Regional differences were identified between hemispheres, between the genu and splenium of the corpus callosum, and between posteromedial and anterolateral thalami. In conclusion, MRF quantification measures relaxometry trends in healthy individuals that are in agreement with the current understanding of neurobiology and has the ability to uncover additional patterns that have not yet been explored

Simultaneous T 1 and T 2 Brain Relaxometry in Asymptomatic Volunteers Using Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is an imaging tool that produces multiple magnetic resonance imaging parametric maps from a single scan. Herein we describe the normal range and progression of MRF-derived relaxometry values with age in healthy individuals. In total, 56 normal volunteers (24 men and 32 women) aged 11-71 years were scanned. Regions of interest were drawn on T 1 and T 2 maps in 38 areas, including lobar and deep white matter (WM), deep gray nuclei, thalami, and posterior fossa structures. Relaxometry differences were assessed using a forward stepwise selection of a baseline model that included either sex, age, or both, where variables were included if they contributed significantly (P Ͻ .05). In addition, differences in regional anatomy, including comparisons between hemispheres and between anatomical subcomponents, were assessed by paired t tests. MRF-derived T 1 and T 2 in frontal WM regions increased with age, whereas occipital and temporal regions remained relatively stable. Deep gray nuclei such as substantia nigra, were found to have agerelated decreases in relaxometry. Differences in sex were observed in T 1 and T 2 of temporal regions, the cerebellum, and pons. Men were found to have more rapid age-related changes in frontal and parietal WM. Regional differences were identified between hemispheres, between the genu and splenium of the corpus callosum, and between posteromedial and anterolateral thalami. In conclusion, MRF quantification measures relaxometry trends in healthy individuals that are in agreement with the current understanding of neurobiology and has the ability to uncover additional patterns that have not yet been explored. INTRODUCTION Physiological aging changes in cerebral gray and white matter (WM) have been well documented in the neurobiology literature. Normal aging is associated with dendritic pruning, axonal loss, demyelination, and synaptic and neuronal loss (1-4). Several magnetic resonance imaging (MRI)-based metrics such as diffusion tensor imaging (DTI), diffusion, volumetry, and magnetization transfer ratio have been utilized to quantify age-related changes (5-13). MRI relaxometry techniques have also been used to quantify age-related changes in T 1 , T 2 , and T 2 * relaxation properties in healthy individuals (14-23). To our knowledge, all relaxometry studies thus far have utilized separate sequences for quantifying 1 relaxation property at a time by measuring the signal recovery after spin inversion (T 1 ) or the decay of the measured MRI signal (T 2 or T 2 *). Such experiments typicall