Neuroradiology research
The Research Group for Artificial Intelligence (AI) in Neuroradiology works closely with various institutes at the University Hospital of Cologne and other radiology institutes in Germany to develop and evaluate new, innovative AI-supported methods in neuroradiology imaging. A particular focus is on the automated evaluation and interpretation of brain tumors and the detection and segmentation of vascular pathologies.
Main research areas
Artificial intelligence in neuro-oncology
The focus in the field of neuro-oncology is on the development of new methods to improve neurological image analysis and non-invasive tumor characterization. The latest developments in the processing of image data using artificial intelligence or deep learning and the introduction of quantitative MRI imaging sequences offer promising opportunities to improve patient management and therapy planning.
Artificial intelligence in neurovascular imaging
Another research focus is the automated detection of neurovascular pathologies with a special focus on intracranial aneurysms. We also aim to use automatically generated segmentations to reduce patient risk and support endovascular and surgical treatments.
- Else Kröner-Fresenius Foundation (Kai Laukamp & Michael Schönfeld)
- Institute for Diagnostic and Interventional Radiology, University Hospital Cologne (Kai Laukamp & Michael Schönfeld)
- Faculty of Medicine and University Hospital Cologne (Kai Laukamp)
- Cologne Clinician Scientist Program (David Zopfs)
- Else Kröner-Fresenius Foundation (Simon Lennartz)
- Institute for Diagnostic Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling Clinic Minden, University Hospital of the Ruhr University Bochum
- Clinic for Neurosurgery and Stereotaxy, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, Essen University Hospital
- Clinic for Diagnostic and Interventional Radiology, University Hospital Würzburg
- Clinic for Diagnostic and Interventional Neuroradiology and Pediatric Neuroradiology, University Hospital Bonn
- Institute for Diagnostic and Interventional Radiology, University Hospital Bonn
- Philips GmbH Innovative Technologies, Aachen
- Lung Cancer Group, Clinic I for Internal Medicine, Faculty of Medicine and University Hospital Cologne
Monitoring Patients with Glioblastoma by Using a Large Language Model: Accurate Summarization of Radiology Reports with GPT-4. Radiology. 2024
Highly compressed SENSE accelerated relaxation-enhanced angiography without contrast and triggering (REACT) for fast non-contrast enhanced magnetic resonance angiography of the neck: Clinical evaluation in patients with acute ischemic stroke at 3 tesla. Magn Reson Imaging. 2024
Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy. Radiology. 2024
GPT-4 for Automated Determination of Radiological Study and Protocol based on Radiology Request Forms: A Feasibility Study. Radiology 2023
Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases. AJNR Am J Neuroradiol. 2022
Virtual non-contrast reconstructions improve differentiation between vascular enhancement and calcifications in stereotactic planning CT scans of cystic intracranial tumors. Eur J Radiol. 2022
Two-dimensional CT measurements enable assessment of body composition on head and neck CT. Zopfs D, Pinto Dos Santos D, Kottlors J, Reimer RP, Lennartz S, Kloeckner R, Schlaak M, Theurich S, Kabbasch C, Schlamann M, Große Hokamp N. Eur Radiol. 2022
Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography. Diagnostics. 2022
Contrast-Enhanced Black Blood MRI Sequence Is Superior to Conventional T1 Sequence in Automated Detection of Brain Metastases by Convolutional Neural Networks. Diagnostics 2021
Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning. J Magn Reson Imaging. 2021
Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage. Neuroradiology 2021
Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model. Am J Neuroradiol. 2021
Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning. Sci Rep. 2020
Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning. J Magn Reson Imaging. 2020
Automated Meningioma Segmentation in Multiparametric MRI: Comparable Effectiveness of a Deep Learning Model and Manual Segmentation. Clin Neuroradiol. 2020
MRI Follow-up of Astrocytoma: Automated Coregistration and Color-Coding of FLAIR Sequences Improves Diagnostic Accuracy With Comparable Reading Time. J Magn Reson Imaging. 2020
Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading. World Neurosurg. 2019
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur Radiol. 2019
Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time. Eur Radiol. 2019
Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine. Invest Radiol. 2018
