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3T MRI Human imaging data acquisition (clinical)
Centre Hospitalier Universitaire De Rennes (CHU RENNES)
MRI 3 Tesla (Philips Ingenia Edition X), imaging, data acquisition, clinical
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3T MRI Imaging data acquisition (Clinical)
Karolinska Institutet (KI)
Centre for Imaging Research (CIR) is a world-leading imaging centre at Karolinska Institutet in Sweden in collaboration with SciLIfeLab and RISE. It offers an exceptional and unique collection of core facilities for cutting-edge structural, functional, and metabolic in vivo multimodal imaging and endovascular/minimally invasive surgical techniques.
The core facility, MRI Centre, aims to catalyse novel and translational research using MRI. We offer a state-of-the-art 3 Tesla GE SIGNA Premier system-wide bore gantry and high-performance XT gradients. We support all types of MRI studies in humans, ranging from small single-centre projects to large-scale international multi-centre studies. A special focus is on offering MRI suitable for multi-modal integration such as CT, PET, EEG and MEG. The KI MRI Centre also offers a full-service lab for drawing blood and investigating psychophysiology (EDA, PPG, Eye-tracking) along with standardised image post-processing and data storage facilities with cluster computing for data analysis. In this service, we offer full support in project consultation, ethical permits and module technical support.
For more information, visit the website: https://imagingresearch.se/facilities/corefacilities/mrc
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3T MRI Imaging data acquisition (Pre-clinical, Large animal)
Karolinska Institutet (KI)
Centre for Imaging Research (CIR) is a world-leading imaging centre at Karolinska Institutet in Sweden in collaboration with SciLIfeLab and RISE. It offers an exceptional and unique collection of core facilities for cutting-edge structural, functional, and metabolic in vivo multimodal imaging and endovascular/minimally invasive surgical techniques.
The core facilities, Karolinska Experimental Research and Imaging Centre (KERIC) and the MRI Centre, aim to catalyse novel and translatory research using MRI. At KERIC, we offer a 9.4T MRI for animals such as rabbits and small piglets (see service for small animal 9.4T MRI). A state-of-the-art 3 Tesla GE SIGNA Premier system-wide bore gantry and high-performance XT gradients are available for large animal preclinical studies. We offer specialised services for large animal models as well as ex vivo imaging and support all types of MRI studies in humans, ranging from small single-centre projects to large-scale international multi-centre studies. A special focus is on offering MRI suitable for multi-modal integration such as CT, PET, EEG and MEG. The KI MRI Centre also offers a full-service lab for drawing blood and investigating psychophysiology (EDA, PPG, Eye-tracking) along with standardised image post-processing and data storage facilities with cluster computing for data analysis. In this service, we offer full support in project consultation, ethical permits, module technical support and animal technicians.
For more information, visit the website: https://imagingresearch.se/facilities/corefacilities/mrc
Or https://imagingresearch.se/facilities/corefacilities/keric
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7T MRI Imaging data acquisition (Clinical)
Karolinska Institutet (KI)
Centre for Imaging Research (CIR) is a world-leading imaging centre at Karolinska Institutet in Sweden in collaboration with SciLIfeLab and RISE. It offers an exceptional and unique collection of core facilities for cutting-edge structural, functional, and metabolic in vivo multimodal imaging and endovascular/minimally invasive surgical techniques.
The core facility, MRI Centre, aims to catalyse novel and translational research using MRI. A unique next-generation clinical 7 Tesla Siemens MAGNETOM Terra.X. equipped with an ultra-high field scanner also offers cutting-edge gradient performance (130 mT/m at 250 T/m/s), parallel transmission, as well as deep learning-based acceleration and denoising for improved image quality. Micrometre resolution in vivo for microstructural imaging and layered fMRI. We support all types of MRI studies in humans, ranging from small single-centre projects to large-scale international multi-centre studies. A special focus is on offering MRI suitable for multi-modal integration such as CT, PET, EEG and MEG. The KI MRI Centre also offers a full-service lab for drawing blood and investigating psychophysiology (EDA, PPG, Eye-tracking) along with standardised image post-processing and data storage facilities with cluster computing for data analysis. In this service, we offer full support in project consultation, ethical permits and module technical support.
For more information, visit the website: https://imagingresearch.se/facilities/corefacilities/mrc
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AI Imaging Lab: Development & Validation of Segmentation and Detection Models
Karolinska Institutet (KI)
## Overview
This service supports SMEs and researchers in developing, training, and validating AI models for medical imaging applications. Hosted at SMAILE, Karolinska Institutet, it covers segmentation, detection, and classification tasks using CT, MRI, nuclear images, multimodal images, ultrasound, histology, and microscopic images. The pipeline includes data curation, evaluation of labeling strategy, model selection and training, evaluation metric selection, and model performance benchmarking.
We provide expertise and support in:
- AI model training and optimization for medical imaging
- Validation using clinical datasets and standard metrics (using publicly available datasets, datasets available through data agreements, and internal datasets at KI, depending on the case)
- Clinical Relevance and Comparison with the state of the art in research and clinical practice
- Imaging biomarkers studies for diagnosis, prognosis, and prediction applications
### How can the service help you?
The service ensures your imaging AI model performs reliably and is aligned with clinical expectations. Whether you’re entering the pre-clinical testing phase or seeking validation to secure investment or regulatory approval, this service equips you with a rigorous evaluation and feedback report.
### How the service will be delivered?
Available both virtually and physically. Imaging data can be reviewed remotely through a secure data transfer process. On-site collaboration is also possible for sensitive datasets or model development, evaluation, and validation. A typical project takes 4–6 weeks.
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## Additional information
### Provider description
SMAILE is the digital health core facility at Karolinska Institute. It offers interdisciplinary support in AI for medical imaging, data analytics, and system validation, partnering with leading institutions under the Swedish TEF-Health node.
### Technical description
The imaging model pipeline is extensively validated by using a curated benchmark set and standard evaluation frameworks such as well-established quantification metrics for object detection, image segmentation, and classification tasks.. Annotation quality is reviewed, and model performance is benchmarked against open or reference models. Standard medical image processing tools such as PyDicom, ANTs, and ITK, as well as community-driven open-sourced frameworks such as MONAI, are the core components of our designed pipelines.
### Service customization
The service can focus on either model development, evaluation, or both. Datasets can be anonymized and securely shared, or analysis can be conducted in a local sandboxed environment.
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AI Model Testing and Validation
Centre D'Excellence En Technologies De L'Information Et De La Communication (CETIC)
Helping to rigorous testing and validation of the developed Multimodal AI models in real hospital environments based on real-world medical images and with collaboration with AI experts and medical staff. In collaboration with the medical staff and advanced deep learning algorithms, test the AI models on datasets not yet seen by the AI to confirm the correct behaviour of the AI. Discussion with medical experts and validation with new data sets (continuous learning) to ensure that the AI is still correct even with new data). Verification that there are no biases.
Keywords: Medical Image Analysis; Deep Learning Algorithms; Binary Classification; Anomaly Localization; AI Model Development; Real-World Images; AI Testing and Validation; Collaboration with Medical Staff; AI Expertise; Custom Pretrained Pipelines; Clinical Imaging; Image Segmentation; Image Feature Extraction; Trustworthy AI
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