Deep Learning based segmentation of carotid vessel wall and plaque in multi-sequence MRI
Deep Learning based segmentation of carotid vessel wall and plaque in multi-sequence MRI
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Dr. med. univ. Malik Galijasevic, PhD, Dr. med. univ. Valentin K. Ladenhauf, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA
Cooperation: Dipl.-Ing. Christina Schwaiger, BSc BSc MSc, VascAge GmbH
In this project a semi-supervised deep learning model for the segmentation of carotid artery vessel wall and plaque in multi-sequence magnetic resonance imaging is developed. Different approaches for the integration of complementary information from multiple MRI sequences and improving segmentation performance in settings of limited annotated imaging data are evaluated. The project aims to enhance the assessment of carotid atherosclerosis and stroke risk using advanced image analysis methods.
Multi-sequence MRI radiomics for the assessment of vulnerable carotid plaques
Multi-sequence MRI radiomics for the assessment of vulnerable carotid plaques
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Dr. med. univ. Malik Galijasevic, PhD, Dr. med. univ. Valentin K. Ladenhauf, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA
Cooperation: Dipl.-Ing. Christina Schwaiger, BSc BSc MSc, VascAge GmbH
In this project, multi-sequence magnetic resonance imaging of carotid artery plaque is analyzed using artificial intelligence (AI)-based image analysis methods. Radiomic features are extracted and compared with histopathological findings to better characterize plaque vulnerability. The aim is to improve the quantitative and reproducible assessment of stroke-relevant carotid atherosclerosis.
Quantitative contrast clearance analysis for brain tumor imaging
Quantitative contrast clearance analysis for brain tumor imaging
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Cand. med. Hannah Rittmayer
Cooperation: Department of Mechatronics, University of Innsbruck
This project focuses on the development and validation of quantitative methods for contrast clearance analysis in MRI of brain tumors. By combining imaging data with clinical and histopathological findings, quantitative imaging biomarkers are investigated for the differentiation of tumor progression from treatment-related effects following radiotherapy. The long-term goal is to improve imaging-based treatment assessment in neuro-oncology.
Radiomics-based correlation of MRI features and cerebrospinal fluid biomarkers in neuroinflammatory disease
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Dipl.-Ing. Markus Tiefenthaler, BSc PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Dr. med. univ. Anna Katharina Stock, PhD
Cooperation: Department of Neurology, Priv.-Doz. Dr. Lukas Neumann, Department of Technical Mathematics (University of Innsbruck)
In this study, radiomic features are extracted from multimodal MRI and analyzed in relation to cerebrospinal fluid biomarkers of inflammation and blood-brain barrier integrity. The aim is to investigate potential links between imaging-derived markers and immunological processes in neuroinflammatory diseases.
Photon-counting CT angiography for improved follow-up imaging of surgically clipped intracranial aneurysms
Photon-counting CT angiography for improved follow-up imaging of surgically clipped intracranial aneurysms
Team: Dr. med. univ. Lukas Lenhart, PhD, Dipl.-Ing. Marie-Christine Pali, BSc PhD, Dr. med. univ. Philipp Deisl, Dr. med. univ. Fabian Lamprecht, Cand. med. Hannah Rittmayer, Dr. med. univ. Malik Galijasevic, PhD, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Univ.-Prof. Dr. med. univ. Astrid Grams, MBA
Cooperation: Department of Neurosurgery
In this project, photon-counting CT angiography is evaluated for post-surgical imaging of clipped intracranial aneurysms. The study focuses on image quality, vessel delineation, and reduction of metal-related artifacts compared to conventional CT angiography. The aim is to improve diagnostic reliability in the follow-up of surgically treated aneurysms.
Photon-counting CT for early infarct assessment after mechanical thrombectomy
Photon-counting CT for early infarct assessment after mechanical thrombectomy
Team: Univ.-Prof. Dr. med. univ. Astrid Grams, MBA, Dipl.-Ing. Marie-Christine Pali, BSc PhD, Dr. med. univ. Constantin Eisenschink, Dr. med. univ. Malik Galijasevic, PhD, Assoz.-Prof. Priv.-Doz. Dr. med. univ. Bernhard Glodny, Dr. med. univ. Lukas Lenhart, PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD
Cooperation: Department of Neurology, Priv.-Doz. Dr. Lukas Neumann, Department of Technical Mathematics (University of Innsbruck)
In this project, photon-counting CT-derived virtual non-contrast imaging is evaluated for early infarct detection following mechanical thrombectomy in acute ischemic stroke. The diagnostic performance of photon-counting CT is compared with conventional CT imaging regarding infarct visualization and prediction of final infarct extent.
Quantitative visibility thresholds of iodine staining using Photon-counting CT
Quantitative visibility thresholds of iodine staining using Photon-counting CT
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Dr. med. univ. Constantin Eisenschink, Dr. med. univ. Anna Katharina Stock, PhD, Assoz.-Prof. Priv.-Doz. Dr. med. univ. Bernhard Glodny, Dr. med. univ. Lukas Lenhart, PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Univ.-Prof. Dr. med. univ. Astrid Grams, MBA
Cooperation: Department of Neurology, Priv.-Doz. Dr. Lukas Neumann, Department of Technical Mathematics (University of Innsbruck)
In this project, photon-counting CT-derived iodine maps are investigated for the assessment of postinterventional parenchymal changes following mechanical thrombectomy in acute ischemic stroke. Quantitative thresholds for the visibility of iodine staining on conventional CT images are evaluated to improve standardized image interpretation after endovascular stroke treatment.
Radiomics in dual-energy computed tomography of acute ischemic stroke
Radiomics in dual-energy computed tomography of acute ischemic stroke
Team: Dr. med. univ. Constantin Eisenschink, Dipl.-Ing. Marie-Christine Pali, BSc PhD, Univ.-Prof. Dr. med. univ. Astrid Grams, MBA, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Assoz.-Prof. Priv.-Doz. Dr. med. univ. Bernhard Glodny
Cooperation: Department of Neurology, Priv.-Doz. Dr. Lukas Neumann, Department of Technical Mathematics (University of Innsbruck)
In this study, radiomic features derived from dual-energy computed tomography are analyzed in patients with acute ischemic stroke. The aim is to investigate whether subtle imaging characteristics in brain regions without visible infarct demarcation are associated with the later development of infarction.
Radiomics and imaging biomarkers in neoadjuvant immunotherapy for resectable NSCLC
Radiomics and imaging biomarkers in neoadjuvant immunotherapy for resectable NSCLC (INNWOP phase II trial (EudraCT 2020-004707-13))
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Priv.-Doz. Mag. Dr. med. univ. Gerlig Widmann, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA
Cooperation: Department of Internal Medicine V, Hematology & Oncology, Comprehensive Cancer Center Innsbruck and Tyrolean Cancer Research Institute, Innpath Institute of Pathology, Department of Pathology, Department of Internal Medicine (St. Vinzenz Hospital Zams, Zams), Department of Visceral, Transplant and Thoracic Surgery, Center of Operative Medicine, Deartment of Pneumology (LKH Hochzirl-Natters, Tirol Kliniken, Natters), Department of Nuclear Medicine, Biocenter, Institut of Bioinformatics
In this study, radiomic features derived from longitudinal CT imaging are investigated in patients with resectable early-stage non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunotherapy. The study evaluates whether changes in radiomic parameters during treatment can serve as non-invasive biomarkers for pathological response and treatment efficacy, and how they relate to metabolic PET imaging findings.
Radiomics in photon-counting CT of acute ischemic stroke
Radiomics in photon-counting CT of acute ischemic stroke
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Dr. med. univ. Constantin Eisenschink, Univ.-Prof. Dr. med. univ. Astrid Grams, MBA, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Assoz.-Prof. Priv.-Doz. Dr. med. univ. Bernhard Glodny
Cooperation: Department of Neurology, Priv.-Doz. Dr. Lukas Neumann, Department of Technical Mathematics (University of Innsbruck)
In this project, photon-counting CT-based radiomic analysis is investigated in acute ischemic stroke. Different radiomic features are evaluated regarding their potential to detect early tissue alterations in regions without visible infarct demarcation on conventional imaging.
k-space radiomics for analyzing longitudinal brain changes in patients with multiple sclerosis
k-space radiomics for analyzing longitudinal brain changes in patients with multiple sclerosis
Team: Dipl.-Ing. Marie-Christine Pali, BSc PhD, Univ.-Prof. Dr. med. univ. Elke R. Gizewski, MHBA, Ass.-Prof. Priv.-Doz. Dr. med. univ. Stephanie Mangesius, PhD, Ass.-Prof. Dipl.-Ing. Dr. tech Christoph Birkl
Cooperation: Department of Neurology, Universitätsklinikum Essen
In this project, k-radiomics is investigated for the quantitative analysis of MRI data in patients with multiple sclerosis (MS). Quantitative features are derived directly from k-space data and evaluated with regard to disease-related changes and disease progression. Longitudinal MRI data are analyzed to assess whether k-space-derived features provide additional or complementary information compared to conventional image-based radiomics approaches.