Dr. Sisniega’s research background builds on the design of multimodality high-resolution imaging systems for small animal imaging (PET-CT, SPECT-CT, and FDOT-CT), for which Dr. Sisniega played a significant role in the design, development, and optimization of the high-resolution Cone-Beam CT (CBCT) component of the imaging system. His expertise includes system design and characterization through analytical modeling and Monte Carlo simulation techniques using GPU-based parallelization, and algorithm design for CBCT image processing, artifact compensation and 3D model-based image reconstruction.
His current work is focused on improvement of image quality in cone-beam CT through algorithmic approaches for: i) high-fidelity, high-speed, artifact correction, ii) volumetric image reconstruction methods for mitigation of noise and sparse sampling effects, and iii) acceleration of optimization methods to bring advanced model-based iterative reconstruction to runtimes acceptable for clinical practice. This work comprises comprehensive artifact correction methods in soft-tissue CBCT (with application to brain imaging in traumatic brain injury scenarios), including compensation of detector non-idealities, model-based beam-hardening correction, and high-fidelity, high-speed Monte Carlo scatter correction, leveraging variance reduction and GPU parallelization to achieve competitive runtime in clinical scenarios. His last research investigated approaches for purely image-based patient motion compensation in dedicated CBCT of the extremities (undergoing rigid motion) and in more challenging scenarios involving automatic compensation of deformable motion in soft-tissue CBCT.
Affiliated Centers & Institutes
- PhD, Biomedical Engineering, Universidad Carlos III de Madrid, 2013
- BSc, Electrical Engineering, Universidad Politecnica de Madrid, 2006