An Online Repository for Pre-Clinical Imaging Protocols (PIPs).
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2023-03
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Abstract
Providing method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.
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Gammon, Seth T, Allison S Cohen, Adrienne L Lehnert, Daniel C Sullivan, Dariya Malyarenko, Henry Charles Manning, David A Hormuth, Heike E Daldrup-Link, et al. (2023). An Online Repository for Pre-Clinical Imaging Protocols (PIPs). Tomography (Ann Arbor, Mich.), 9(2). pp. 750–758. 10.3390/tomography9020060 Retrieved from https://hdl.handle.net/10161/27248.
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Scholars@Duke
Daniel Carl Sullivan
Research interests are in oncologic imaging, especially the clinical evaluation and validation of imaging biomarkers for therapeutic response assessment.
Stephanie Blocker
I am a cancer biologist whose research laboratory focuses on solid tumor imaging. We utilize multi-modal, multi-scale imaging, combined with nuanced statistical and machine learning approaches, to measure important features of cancer. My goal os to develop and translate imaging approaches which improve clinical diagnostics and personalize care for cancer patients.
Alexandra Badea
I have a joint appointment in Radiology and Neurology and my research focuses on neurological conditions like Alzheimer’s disease. I work on imaging and analysis to provide a comprehensive characterization of the brain. MRI is particularly suitable for brain imaging, and diffusion tensor imaging is an important tool for studying brain microstructure, and the connectivity amongst gray matter regions.
I am interested in image segmentation, morphometry and shape analysis, as well as in integrating information from MRI with genetics, and behavior. Our approaches target: 1) phenotyping the neuroanatomy using imaging; 2) uncovering the link between structural and functional changes, the genetic bases, and environmental factors. I am interested in generating methods and tools for comprehensive phenotyping.
We use high-performance cluster computing to accelerate our image analysis. We use compressed sensing image reconstruction, and process large image arrays using deformable registration, perform segmentation based on multiple image contrasts including diffusion tensor imaging, as well as voxel, and graph analysis for connectomics.
At BIAC my efforts focus on developing multivariate biomarkers and identifying vulnerable networks based on genetic risk for Alzheimer's disease.
My enthusiasm comes from the possibility to extend from single to integrative multivariate and network based analyses to obtain a comprehensive picture of normal development and aging, stages of disease, and the effects of treatments. I am working on multivariate image analysis and predictive modeling approaches to help better understand early biomarkers for human disease indirectly through mouse models, as well as directly in human studies.
I am dedicated to supporting an increase in female presence in STEM fields, and love working with students. The Bass Connections teams involve undergraduate students in research, providing them the opportunity to do independent research studies and get involved with the community. These students have for example takes classes such as:
BME 394: Projects in Biomedical Engineering (GE)
BME 493: Projects in Biomedical Engineering (GE)
ECE 899: Special Readings in Electrical Engineering
NEUROSCI 493: Research Independent Study 1
Cristian Tudorel Badea
- Our QIAL lab advances quantitative imaging by designing novel CT systems, reconstruction algorithms, image analysis and applications, with a core strength in preclinical CT.
- Current efforts center on spectral CT (dual-energy and photon-counting) with nanoparticle contrast agents for theranostics, multidimensional CT for challenging applications such as intracranial aneurysm, cardiac, and perfusion imaging, and modern reconstruction and image processing ( including deep learning).
- In parallel, we lead co-clinical cancer imaging work; I served as PI of the U24 Duke Preclinical Research Resources for Quantitative Imaging Biomarkers within the NCI Co-Clinical Imaging Research Program (CIRP).
- We are also building a virtual preclinical photon-counting CT platform for cancer studies to accelerate method development and translation.
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