Publications by authors named "P Vagdargi"

Flexible array transducers can adapt to patient-specific geometries during real-time ultrasound (US) image-guided therapy monitoring. This makes the system radiation-free and less user-dependency. Precise estimation of the flexible transducer's geometry is crucial for the delay-and-sum (DAS) beamforming algorithm to reconstruct B-mode US images.

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Article Synopsis
  • Image-guided neurosurgery relies on accurate localization, but challenges arise from brain deformation during surgery, making it hard to use preoperative images effectively.
  • A 3D deep learning framework, DL-Recon, has been developed to enhance the quality of intraoperative CBCT images by combining physics-based models with deep learning techniques, utilizing uncertainty information for better accuracy.
  • The framework was trained and validated using paired CT and simulated CBCT images, and its performance was evaluated for clinical feasibility through a pilot study involving neurosurgery patients, showing promise in improving the registration of brain tissues during surgery.
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Internal fixation of pelvic fractures is a challenging task requiring the placement of instrumentation within complex three-dimensional bone corridors, typically guided by fluoroscopy. We report a system for two- and three-dimensional guidance using a drill-mounted video camera and fiducial markers with evaluation in first preclinical studies. The system uses a camera affixed to a surgical drill and multimodality (optical and radio-opaque) markers for real-time trajectory visualization in fluoroscopy and/or CT.

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The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid.

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Conventional neuro-navigation can be challenged in targeting deep brain structures via transventricular neuroendoscopy due to unresolved geometric error following soft-tissue deformation. Current robot-assisted endoscopy techniques are fairly limited, primarily serving to planned trajectories and provide a stable scope holder. We report the implementation of a robot-assisted ventriculoscopy (RAV) system for 3D reconstruction, registration, and augmentation of the neuroendoscopic scene with intraoperative imaging, enabling guidance even in the presence of tissue deformation and providing visualization of structures beyond the endoscopic field-of-view.

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