Publications by authors named "Michael E Moseley"

Article Synopsis
  • The study aims to improve prediction of 90-day functional outcomes in ischemic stroke patients using a deep learning model that combines non-contrast CT images and clinical data, potentially aiding healthcare planning and clinical trials.
  • The dataset included 1,335 patients from multiple trials and registries, and the model demonstrated superior accuracy in predicting outcomes compared to models using only imaging or clinical data alone.
  • The fused model achieved a mean absolute error (MAE) of 0.94 for mRS score prediction and an AUC of 0.91 for identifying unfavorable outcomes, indicating it significantly outperforms existing methods.
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Article Synopsis
  • Artificial intelligence in medicine usually faces challenges related to small, non-diverse patient data due to privacy concerns, but federated learning (FL) offers a solution by allowing training across different hospitals without sharing sensitive data.
  • The newly developed FL-PedBrain platform is specifically designed for pediatric brain tumors, enabling collaborative training for tumor classification and segmentation across 19 international centers, addressing the lack of diverse datasets in this area.
  • FL-PedBrain shows impressive performance metrics, maintaining almost equivalent accuracy to centralized data training while significantly improving segmentation performance by 20 to 30% at external sites, and allows for the examination of data variability in real-world situations.
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Objective: Hydrocephalus is a challenging neurosurgical condition due to nonspecific symptoms and complex brain-fluid pressure dynamics. Typically, the assessment of hydrocephalus in children requires radiographic or invasive pressure monitoring. There is usually a qualitative focus on the ventricular spaces even though stress and shear forces extend across the brain.

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Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations.

Methods: The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial.

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Objective: Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects.

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Cerebral blood flow (CBF) is an important hemodynamic parameter to evaluate brain health. It can be obtained quantitatively using medical imaging modalities such as magnetic resonance imaging and positron emission tomography (PET). Although CBF in adults has been widely studied and linked with cerebrovascular and neurodegenerative diseases, CBF data in healthy children are sparse due to the challenges in pediatric neuroimaging.

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Objectives: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion.

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Metastatic osteosarcoma has a poor prognosis with a 2-y, event-free survival rate of ∼15 to 20%, highlighting the need for the advancement of efficacious therapeutics. Chimeric antigen receptor (CAR) T-cell therapy is a potent strategy for eliminating tumors by harnessing the immune system. However, clinical trials with CAR T cells in solid tumors have encountered significant challenges and have not yet demonstrated convincing evidence of efficacy for a large number of patients.

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Automatic segmentation of lung nodules on computed tomography (CT) images is challenging owing to the variability of morphology, location, and intensity. In addition, few segmentation methods can capture intra-nodular heterogeneity to assist lung nodule diagnosis. In this study, we propose an end-to-end architecture to perform fully automated segmentation of multiple types of lung nodules and generate intra-nodular heterogeneity images for clinical use.

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Cerebrovascular reactivity (CVR), the capacity of the brain to increase cerebral blood flow (CBF) to meet changes in physiological demand, is an important biomarker to evaluate brain health. Typically, this brain "stress test" is performed by using a medical imaging modality to measure the CBF change between two states: at baseline and after vasodilation. However, since there are many imaging modalities and many ways to augment CBF, a wide range of CVR values have been reported.

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The prognosis for high-grade glioma (HGG) remains dismal and the extent of resection correlates with overall survival and progression free disease. Epidermal growth factor receptor (EGFR) is a biomarker heterogeneously expressed in HGG. We assessed the feasibility of detecting HGG using near-infrared fluorescent antibody targeting EGFR.

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Purpose: 3D multi-echo gradient-recalled echo (ME-GRE) can simultaneously generate time-of-flight magnetic resonance angiography (pTOF) in addition to T2*-based susceptibility-weighted images (SWI). We assessed the clinical performance of pTOF generated from a 3D ME-GRE acquisition compared with conventional TOF-MRA (cTOF).

Methods: Eighty consecutive children were retrospectively identified who obtained 3D ME-GRE alongside cTOF.

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Background Iron oxide nanoparticles are an alternative contrast agent for MRI. Gadolinium deposition has raised safety concerns, but it is unknown whether ferumoxytol administration also deposits in the brain. Purpose To investigate whether there are signal intensity changes in the brain at multiecho gradient imaging following ferumoxytol exposure in children and young adults.

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Background: To maintain cerebral blood flow (CBF), cerebral blood vessels dilate and contract in response to blood supply through cerebrovascular reactivity (CR).

Purpose: Cardiovascular (CV) disease is associated with increased stroke risk, but which risk factors specifically impact CR is unknown.

Study Type: Prospective longitudinal.

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In resting-state functional MRI (rs-fMRI), functional networks are assessed utilizing the temporal correlation between spontaneous blood oxygen level-dependent signal fluctuations of spatially remote brain regions. Recently, several groups have shown that temporal shifts are present in rs-fMRI maps in patients with cerebrovascular disease due to spatial differences in arterial arrival times, and that this can be exploited to map arrival times in the brain. This suggests that rs-fMRI connectivity mapping may be similarly sensitive to such temporal shifts, and that standard rs-fMRI analysis methods may fail to identify functional connectivity networks.

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Purpose: Amplified magnetic resonance imaging (aMRI) was recently introduced as a new brain motion detection and visualization method. The original aMRI approach used a video-processing algorithm, Eulerian video magnification (EVM), to amplify cardio-ballistic motion in retrospectively cardiac-gated MRI data. Here, we strive to improve aMRI by incorporating a phase-based motion amplification algorithm.

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Measurement of the ability of blood vessels to dilate and constrict, known as vascular reactivity, is often performed with breath-holding tasks that transiently raise arterial blood carbon dioxide (PCO) levels. However, following the proper commands for a breath-holding experiment may be difficult or impossible for many patients. In this study, we evaluated two approaches for obtaining vascular reactivity information using blood oxygenation level-dependent signal fluctuations obtained from resting-state functional magnetic resonance imaging data: physiological fluctuation regression and coefficient of variation of the resting-state functional magnetic resonance imaging signal.

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This study aimed to determine whether measurements of cerebrovascular reserve and oxygenation, assessed with spin relaxation rate R2', yield similar information about pathology in pre-operative Moyamoya disease patients, and to assess whether R2' is a better measure of oxygenation than other proposed markers, such as R2* and R2. Twenty-five pre-operative Moyamoya disease patients were scanned at 3.0T with acetazolamide challenge.

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While brain connectivity analyses have been demonstrated to identify ill patients for a number of diseases, their ability to predict cognitive impairment after brain injury is not well established. Traditional post brain injury models, such as stroke, are limited for this evaluation because pre-injury brain connectivity patterns are infrequently available. Patients with severe carotid stenosis, in contrast, often undergo non-emergent revascularization surgery, allowing the collection of pre and post-operative imaging, may experience brain insult due to perioperative thrombotic/embolic infarcts or hypoperfusion, and can suffer post-operative cognitive decline.

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Purpose: This work describes a new method called amplified MRI (aMRI), which uses Eulerian video magnification to amplify the subtle spatial variations in cardiac-gated brain MRI scans and enables better visualization of brain motion.

Methods: The aMRI method takes retrospective cardiac-gated cine MRI data as input, applies a spatial decomposition, followed by temporal filtering and frequency-selective amplification of the MRI cardiac-gated frames before synthesizing a motion-amplified cine data set.

Results: This approach reveals deformations of the brain parenchyma and displacements of arteries due to cardiac pulsatility, especially in the brainstem, cerebellum, and spinal cord.

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Here we investigate the utility of a dual-echo Echo-Planar Imaging (DE-EPI) Diffusion Weighted Imaging (DWI) approach to improve lesion conspicuity in pediatric imaging. This method delivers two 'echo images' for one diffusion-preparation period. We also demonstrate how the echoes can be utilized to remove transmit/receive coil-induced and static magnetic field intensity modulations on both echo images, which often mimic pathology and thereby pose diagnostic challenges.

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Background: Susceptibility-weighted imaging (SWI) in neuroimaging can be challenging due to long scan times of three-dimensional (3D) gradient recalled echo (GRE), while faster techniques such as 3D interleaved echo-planar imaging (iEPI) are prone to motion artifacts. Here we outline and implement a 3D short-axis propeller echo-planar imaging (SAP-EPI) trajectory as a faster, motion-correctable approach for SWI.

Methods: Experiments were conducted on a 3T MRI system.

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Spontaneous fluctuations in blood oxygenation level-dependent (BOLD) images are the basis of resting-state fMRI and frequently used for functional connectivity studies. However, there may be intrinsic information in the amplitudes of these fluctuations. We investigated the possibility of using the amplitude of spontaneous BOLD signal fluctuations as a biomarker for cerebral vasomotor reactivity.

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Purpose: To investigate if delays in resting-state spontaneous fluctuations of the BOLD (sfBOLD) signal can be used to create maps similar to time-to-maximum of the residue function (Tmax) in Moyamoya patients and to determine whether sfBOLD delays affect the results of brain connectivity mapping.

Materials And Methods: Ten patients were scanned at 3 Tesla using a gradient-echo echo planar imaging sequence for sfBOLD imaging. Cross correlation analysis was performed between each brain voxel signal and a reference signal comprised of either the superior sagittal sinus (SSS) or whole brain (WB) average time course.

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Despite the global impact and advances in understanding the pathophysiology of cerebrovascular diseases, the term "stroke" is not consistently defined in clinical practice, in clinical research, or in assessments of the public health. The classic definition is mainly clinical and does not account for advances in science and technology. The Stroke Council of the American Heart Association/American Stroke Association convened a writing group to develop an expert consensus document for an updated definition of stroke for the 21st century.

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