The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might look ideal providing exact 3D information, however this is at the cost of a higher radiation dose, longer imaging time, and more space requirements in the operating room. Introducing some depth information with relatively low dose, and requiring less space, digital tomosynthesis (DTS) is a potential candidate for guiding interventions. However, due to the few number of projections and to the limited angle acquisition, DTS has poor depth resolution. Since high quality patient-specific prior CT scans are usually performed prior to the intervention for diagnosis or to plan the intervention, and given that such images share a fair amount of information with the intraoperative DTS images, we propose in this work a prior-based iterative reconstruction framework to improve the intraoperative DTS image quality. The framework is based on registering the prior CT image to an intermediate low-quality intraoperative DTS image, then iteratively re-reconstructing the intraoperative DTS image using the co-registered prior CT as the starting image. We acquired prior CT and intraoperative CBCT data of a liver phantom and simulated some intraoperative DTS projection images using a spherical ellipse scan geometry. Our results show a great improvement in the DTS image quality with the proposed method and prove the importance of choosing a good starting point for the iterative DTS reconstruction.
The perfusion imaging using C-arm CT could be used intraoperatively for liver cancer treatment planning and evaluation. To deal with undersampled data due to slow C-arm CT rotation and pause between the rotations, we applied model-based reconstruction methods. Recent works using the time separation technique with an analytical basis function set have led to a significant improvement in the quality of C-arm CT perfusion maps. In this work we apply the time separation technique with a prior knowledge basis function set extracted using singular value decomposition from CT perfusion reconstructions. On C-arm CT liver perfusion scan simulated based on the real CT liver perfusion scan we show that the bases extracted from only two CT perfusion scans are capable of modeling the C-arm CT data correctly.
The use of C-Arm-based cone-beam computed tomography (CBCT) plays an increasing role in interventions, especially for guidance and therapy control. The slow speed and the high dose limit the use of CBCT to research and prevent its widespread application in clinical routine. Acquiring less data using greater angular step or limited angular range is an obvious way to overcome these issues. However, images reconstructed from such datasets using standard reconstruction algorithms are deteriorated with severe artifacts. In this work, we investigate the use of a new nullspace-constrained modification scheme for sparse-view and limited-angle intra-operative CT image reconstruction. This scheme allows to perform fast unconstrained ART reconstruction, and, based on prior knowledge regarding the object to be reconstructed, some modifications restricted to the nullspace of the system can be easily applied as a post-processing step. Within this scheme, we enforce sparsity by integrating geometric prior information regarding the interventional tool itself, besides a high-quality pre-operative CT image. The presented method was compared to the compressed sensing-based algorithms NIHT and PrIDICT. Performance was evaluated qualitatively and quantitatively. This new scheme is shown to be promising for low-dose intra-operative image reconstruction. Compared to PrIDICT and NIHT, it shows higher reconstruction accuracy and demonstrates the ability to precisely visualize the instrument’s position even when only 15 projection views are acquired over a full angular range. It demonstrates an accurate reconstruction with a high degree of robustness against data incompleteness and sparsity level over-estimation.
The dynamics of large-scale neural circuits is known to play an important role in both aberrant and normal cognitive functioning. Describing these phenomena is extremely important when we want to get an understand- ing of the aging processes and for neurodegenerative disease evolution. Modern systems and control theory offers a wealth of methods and concepts that can be easily applied to facilitate an insight into the dynamic processes governing disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. Past research has shown that two types of controllability - the modal and average controllability - are key components when it comes to the mechanistic explanation of how the brain operates in different cognitive states. The average controllability describes the role of a brain network’s node in driving the system to many easily reachable states. On the other hand, the modal controllability is employed to identify the states that are difficult to control. The first controllability type favors highly connected areas while the latter weakly connected areas of the brain. Certain areas of the brain or nodes in the connectivity graph (structural or functional) can act as drivers and move the system (brain) into specific states of action. To determine these areas we apply the novel concept of exact controllability and determine the minimum set and the location of driver nodes for dementia networks. Our results applied on structural brain networks in dementia suggest that this novel technique can accurately describe the different node roles in controlling trajectories of brain networks, and show the transition of some driver nodes and the conservation of others in the course of this disease.
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