Our AI research group aims to develop novel methods to improve brain imaging and establish new approaches for non-invasive tumor characterization. Recent advances in processing of imaging data with use of Artificial Intelligence and introduction of quantitative MR imaging sequences provide numerous opportunities for clinicians to improve patient management and therapy planning.
One of our main interests is to automatize brain tumor detection and consecutive segmentation using Deep Learning based techniques to free up radiologists’ resources and enhance reproducibility of imaging data. In combination with newly developed imaging sequences such as MR Fingerprinting, we want to determine tumor biology, non-invasively. Finally, we use Artificial Intelligence for studies regarding non-invasive multiparametric diagnostics and studies evaluating multimodal tumor therapy.
One of our current projects aims to develop methods for distinguishing true tumor progression from therapy-related changes in glioma patients undergoing treatment, with one approach utilizing quantitative MRI imaging and another involving deep learning algorithms, making it more accessible compared to the alternative FET-PET imaging. Additionally, advanced language analysis will be employed to effectively summarize the complex disease courses of glioma patients, thereby enhancing their treatment and resource allocation.
Another focus of our research group is the automated detection of neurovascular pathologies with special interest in intracranial aneurysms. Further, we want to use automatically generated segmentations to stratify patient risk and facilitate endovascular or surgical treatment.
- Else Kröner-Fresenius-Stiftung (Kai Laukamp & Michael Schönfeld)
- Institut für Diagnostische und Interventionelle Radiologie, University Hospital Cologne (Kai Laukamp & Michael Schönfeld)
- Faculty of Medicine and University Hospital Cologne (Kai Laukamp)
- Cologne Clinician Scientist Programm (David Zopfs)
- Else Kröner-Fresenius-Stiftung (Simon Lennartz)
- Institute for Diagnostic Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling Klinikum Minden, University Hospitals of the Ruhr-University Bochum, Germany
- Department of Neurosurgery and Stereotaxy, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
- Philips GmbH Innovative Technologies, Aachen, Germany
- Lung Cancer Group, Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- 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