Publications by authors named "Pamela Schaefer"

Article Synopsis
  • Portable low-field magnetic resonance imaging (LF-MRI) offers a promising way to assess Alzheimer's disease (AD) patients in places where traditional MRI isn't available, despite some limitations in image quality.
  • * Researchers optimized LF-MRI techniques and created a free machine learning tool for analyzing brain structure and white matter changes in patients with cognitive impairments.
  • * The study found that LF-MRI accurately measures hippocampal volumes and white matter hyperintensities, suggesting that this technology can improve access to neuroimaging for dementia patients at a lower cost.
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  • Cognitive motor dissociation (CMD) involves patients who can follow commands in brain scans like fMRI, despite showing no behavioral signs of language function, highlighting the importance of accurate diagnosis in severe brain injuries.
  • Recent findings outline a structured approach for assessing CMD at clinical institutions, underlining the need for ethical considerations, standardized protocols, and effective communication of results.
  • The proposed method for CMD assessment aims to be adaptable, allowing for updates and improvements as more evidence becomes available in the field.
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In this episode of the Podcast Series on Training and Education, Pamela Schaefer, MD, joins host Monica Cheng, MD, to discuss incorporating education into radiology careers. Dr. Schaefer shares her journey, the role of leadership, and advice for aspiring educators.

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Objective: For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke.

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Acute stroke imaging plays a vital and time-sensitive role in therapeutic decision-making. Current clinical workflows widely use computed tomography (CT) and magnetic resonance (MR) techniques including CT and MR perfusion to estimate the volume of ischemic penumbra at risk for infarction without acute intervention. The use of imaging techniques aimed toward evaluating the metabolic derangements underlying a developing infarct may provide additional information for differentiating the penumbra from benign oligemia and infarct core.

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Background And Purpose: The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings.

Materials And Methods: Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study.

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  • Deep learning (DL) techniques have been tested for speeding up lumbar MRI exams, but their overall impact on scan time and image quality for routine use is still unclear.
  • A study evaluated 36 patients using both DL-accelerated and conventional MRI protocols, focusing on various diagnostic factors like stenosis and nerve compression.
  • Results showed that the DL method did not significantly differ in diagnostic quality but had lower signal-to-noise ratio and more artifacts, suggesting it can reduce scan times while providing comparable results.
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Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery.

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The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images.

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Flow-related artifacts have been observed in highly accelerated T1-weighted contrast-enhanced wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid gradient-echo (MPRAGE) imaging and can lead to diagnostic uncertainty. We developed an optimized flow-mitigated Wave-CAIPI MPRAGE acquisition protocol to reduce these artifacts through testing in a custom-built flow phantom. In the phantom experiment, maximal flow artifact reduction was achieved with the combination of flow compensation gradients and radial reordered k-space acquisition and was included in the optimized sequence.

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Ischemic strokes in young adults are increasing in incidence and have emerged as a public health issue. The radiological features are not only diagnostic in identifying ischemic infarctions but also provide important clues in the investigation of the underlying causes or in the identification of risk factors. According to the different imaging patterns associated with ischemic stroke in young adults, the causes can be classified into 5 categories: cardioembolism, large vessel vasculopathy, small vessel vasculopathy, toxic-metabolic, and hypercoagulable disorders.

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Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. However, imaging findings may be indeterminate, and algorithmic inferences may have substantial uncertainty.

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Article Synopsis
  • The study evaluated a new high-speed imaging technique called Wave-T1-MPRAGE, which aims to improve brain MRI scans by reducing the time needed for imaging while maintaining diagnostic quality.
  • Researchers compared this technique to the standard T1-MPRAGE sequence in 80 patients, focusing on how well each method visualizes enhancing lesions and the impact of motion artifacts and noise.
  • The results showed that Wave-T1-MPRAGE performed equally well in detecting brain lesions and had significant advantages, including halving the scan time while minimizing noise and motion issues.
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Background Portable, low-field-strength (0.064-T) MRI has the potential to transform neuroimaging but is limited by low spatial resolution and low signal-to-noise ratio. Purpose To implement a machine learning super-resolution algorithm that synthesizes higher spatial resolution images (1-mm isotropic) from lower resolution T1-weighted and T2-weighted portable brain MRI scans, making them amenable to automated quantitative morphometry.

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Objectives: Wave-CAIPI (Controlled Aliasing in Parallel Imaging) enables dramatic reduction in acquisition time of 3D MRI sequences such as 3D susceptibility-weighted imaging (SWI) but has not been clinically evaluated at 1.5 T. We sought to compare highly accelerated Wave-CAIPI SWI (Wave-SWI) with two alternative standard sequences, conventional three-dimensional SWI and two-dimensional T2*-weighted Gradient-Echo (T2*w-GRE), in patients undergoing routine brain MRI at 1.

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Background: Studies of neurologic outcomes have found conflicting results regarding differences between patients with substance-related cardiac arrests (SRCA) and non-SRCA. We investigate the effects of SRCA on severe cerebral edema development, a neuroimaging intermediate endpoint for neurologic injury.

Methods: 327 out-of-hospital comatose cardiac arrest patients were retrospectively analyzed.

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Purpose: To evaluate the impact of magnetization transfer (MT) on brain tissue contrast in turbo-spin-echo (TSE) and EPI fluid-attenuated inversion recovery (FLAIR) images, and to optimize an MT-prepared EPI FLAIR pulse sequence to match the tissue contrast of a clinical reference TSE FLAIR protocol.

Methods: Five healthy volunteers underwent 3T brain MRI, including single slice TSE FLAIR, multi-slice TSE FLAIR, EPI FLAIR without MT-preparation, and MT-prepared EPI FLAIR with variations of the MT-preparation parameters, including number of preparation pulses, pulse amplitude, and resonance offset. Automated co-registration and gray matter (GM) versus white matter (WM) segmentation was performed using a T1-MPRAGE acquisition, and the GM versus WM signal intensity ratio (contrast ratio) was calculated for each FLAIR acquisition.

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Background And Objectives: In patients with severe coronavirus disease 2019 (COVID-19), disorders of consciousness (DoC) have emerged as a serious complication. The prognosis and pathophysiology of COVID-DoC remain unclear, complicating decisions about continuing life-sustaining treatment. We describe the natural history of COVID-DoC and investigate its associated brain connectivity profile.

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Purpose: To assess the utility of ASL in evaluating patients presenting to the ED with stroke-like symptoms.

Methods: ASL and DWI images from 526 consecutive patients presenting to the ED with acute stroke symptoms were retrospectively reviewed. DWI images were evaluated for volume of restricted diffusion using ABC/2.

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