Image guided interventions are generally performed using interventional imaging modalities such as fluoroscopy and ultrasound. These modalities allow for instantaneous visualization of the interventional instruments in relation to the patient anatomy, but are limited in their visualization capabilities. X-ray imaging yields 2D projection images, which has bad soft-tissue contrast; for 3D information, biplane imaging systems are required. Ultrasound imaging generally images only one 2D slice.
Diagnostic imaging modalities such as MRI and CT, on the other hand, are more versatile and allow for accurate 3D imaging of the anatomy. However, such imaging modalities are generally not suitable for or available in interventional settings.
Registration of a pre-operative coronary model from CTA to intra-operative X-ray imaging.
Bringing these 3D pre-interventional imaging modalities to the interventional suite has several advantages. It facilitates minimally invasive approaches, by offering additional visualization modalities during the interventions. Moreover, it may improve the accuracy, as it enables introduction of plans that are made on pre-interventional data into the interventional setting.
The primary goal of this research line is to improve image guidance in interventions by introducing (the pre-operative diagnostic) 3D images into the interventional suite. Whereas such navigation approaches are becoming state-of-the art in applications such as brain surgery and orthopedics, application of this type of technology in interventions in soft tissue anatomy has been hampered by issues such as tissue motion and deformation. Our focus is to bring pre-operative information on the target (e.g. location of a tumor or occlusion, or a treatment plan) to the intervention, also in cases where tissue motion and deformation may occur. We are developing and evaluating methods to keep the pre-operative data synchronized with the interventional situation by combining interventional imaging with additional functional data, position tracking information and motion/deformation models.
The main research areas for image guidance in interventions therefore are:
- (3D) image processing, e.g. to enhance or segment the diagnostic images
- image registration: registration of the pre-operative images to the patient, e.g. by registration of the pre-operative modality to the intra-operative modalities
- tracking of motion and deformation of the anatomy, to allow compensation of the pre-operative images to the intra-operative situation
- and, in relation this, modeling of (cyclic) deformation of tissue, such as a beating heart, to be able to account for these deformations in image guidance
- tracking of the instruments, to facilitate combined display of the instrument and the pre-operative data
- Improved image guidance in intravascular interventions
- 3D Statistical shape modeling for improved intra-operative guidance
- Developing tools for hyperthermia treatment planning
- 4D US for improved image guidance in minimally invasive interventions
- EasyCTO – Computer Aided Image Guidance for Percutaneous Treatment of Coronary Chronic Total Occlusions
- Image Guidance in minimally invasive liver interventions
- OCT processing
- IMAGIC – Intelligent image guidance in cardiac interventions
- Improved Image Guidance for TACE
- Automated spinous process segmentation in X-ray images via deep learning methods