Publications by authors named "Ashley Gillman"

Article Synopsis
  • The Brain Imaging Data Structure (BIDS) is a community-created standard for organizing neuroscience data and metadata, helping researchers manage various modalities efficiently.
  • The paper discusses the evolution of BIDS, including the guiding principles, extension mechanisms, and challenges faced during its development.
  • It also highlights key lessons learned from the BIDS project, aiming to inspire and inform researchers in other fields about effective data organization practices.
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Article Synopsis
  • The Brain Imaging Data Structure (BIDS) is a collaborative standard designed to organize various neuroscience data and metadata.
  • The paper details the history, principles, and mechanisms behind the development and expansion of BIDS, alongside the challenges it faces as it evolves.
  • It also shares lessons learned from the project to help researchers in other fields apply similar successful strategies.
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Cerebral malaria is the most serious manifestation of severe falciparum malaria. Sequestration of infected red blood cells and microvascular dysfunction are key contributing processes. Whether these processes occur in early stage disease prior to clinical manifestations is unknown.

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The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets.

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Background: Multicentre clinical trials evaluating the role of F-Fluoroethyl-L-tyrosine (F-FET) PET as a diagnostic biomarker in glioma management have highlighted a need for standardised methods of data analysis. F-FET uptake normalised against background in the contralateral brain is a standard imaging technique to delineate the biological tumour volume (BTV). Quantitative analysis of F-FET PET images requires a consistent and robust background activity.

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Article Synopsis
  • The study aimed to systematically review techniques for diagnosing and predicting COVID-19 using medical imaging, evaluating the performance and clinical applications of these methods, while also looking at the authors behind these studies.
  • After screening 1,002 studies, 71% were excluded due to bias, indicating significant concerns about reliability in this research area.
  • Diagnostic methods showed high performance (AUC of 0.924 and accuracy of 91.7%), mainly using deep learning techniques, while prognostic methods were less effective (AUC of 0.836 and accuracy of 78.4%).
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SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique.

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Background: Plasmodium vivax has been proposed to infect and replicate in the human spleen and bone marrow. Compared to Plasmodium falciparum, which is known to undergo microvascular tissue sequestration, little is known about the behavior of P. vivax outside of the circulating compartment.

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This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms.

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Patient motion is an important consideration in modern PET image reconstruction. Advances in PET technology mean motion has an increasingly important influence on resulting image quality. Motion-induced artifacts can have adverse effects on clinical outcomes, including missed diagnoses and oversized radiotherapy treatment volumes.

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