PET/SPECT/CT preclinical imaging CUBES
Automated microscopy and Spatial Proteomics
Real-time, label free cell analysis
Nano and micro particle analysis
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Webinar recording: Deep dive into Spatial Biology with MERSCOPE Ultra Platform
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We invite you to learn more about MERSCOPE Ultra Platform. Webinar featuring Rob Mathis (Global Instrument Product...
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In this webinar, Dr. Mohammad Ekhlak of Banaras Hindu University will share data and insights from the Center for...
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Webinar recording : BioFlux, the gold standard for Platelet Biology and Thrombosis research
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Thrombosis, the underlying cause of major fatal conditions like ischemic stroke and myocardial infarction, poses a...
Scaling Whole-Genome Sequencing to >50,000 single cells using cellenONE
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cellenONE technology enabled the creation of tens of thousands of high-quality single-cell genomes, paving the way for...
Automatic, Real Time Acquisition of Bioluminescent Kinetic Curves
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Watch this pre-recorded webinar with Dr. Andrew Van Praagh to learn how our new Aura software feature —Kinetics—...
Theranostics: From Mice to Men and Back
Jun 25, 2024
Recorded webinar
Presenters: Prof. Dr. Ken Herrmann and Prof. Dr. Katharina Lückerath – Moderator: Hannah Notebaert
Orion 2024 AACR poster: 17-plex single-step stain and imaging of cell Lung Carcinoma
Jun 21, 2024
RareCyte Orion is a benchtop, high resolution, whole slide multimodal imaging instrument. A combination of quantitative...
Webinar: Dose Reduction and Image Enhancement in Preclinical Mouse Imaging using Deep Learning
Jan 31, 2023
In preclinical imaging, it is important to aim for dose reduction as longitudinal studies with many scanning time points can result in accumulated doses that impact your study results. However, lowering the dose in PET and CT imaging inherently introduces noise, and reduced image quality negatively impacts diagnostic performance. Various denoising techniques already exist, but deep learning (DL) methods have become increasingly popular for image quality enhancement.
Florence explains how she developed and evaluated an image-to-image CNN framework to predict higher quality images from noisier images acquired at lower radiation doses for both modalities.
Related technologies: PET, SPECT, CT
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