S
Simon R. Arridge
Researcher at University College London
Publications - 602
Citations - 33776
Simon R. Arridge is an academic researcher from University College London. The author has contributed to research in topics: Iterative reconstruction & Optical tomography. The author has an hindex of 83, co-authored 582 publications receiving 30962 citations. Previous affiliations of Simon R. Arridge include University of Cambridge & University College London Hospitals NHS Foundation Trust.
Papers
More filters
Book ChapterDOI
Physiological system identification with the kalman filter in diffuse optical tomography
Solomon G. Diamond,Theodore J. Huppert,Ville Kolehmainen,Maria Angela Franceschini,Jari P. Kaipio,Simon R. Arridge,David A. Boas +6 more
TL;DR: This paper uses auxiliary physiological measurements such as blood pressure and heart rate within a Kalman filter framework to model physiological components in DOT and significantly improved estimates of the local hemodynamics in this test case.
Journal ArticleDOI
Acoustic Wave Field Reconstruction From Compressed Measurements With Application in Photoacoustic Tomography
TL;DR: A modification of the Curvelet frame is proposed to account for the smoothing effects of data acquisition and motivated by a frequency domain model for photoacoustic tomography.
Journal ArticleDOI
Multi-Scale Learned Iterative Reconstruction
TL;DR: In this paper, a multi-scale learned iterative reconstruction scheme was proposed for 3D cone beam computed tomography from real measurement data of an organic phantom, which is scalable to large scale inverse problems with non-trivial forward operators.
Journal ArticleDOI
Inverse Born series for the Calderon problem
TL;DR: In this paper, a direct reconstruction method for the Calderon problem based on inversion of the Born series is proposed, and the convergence, stability and approximation error of the method are characterized.
Journal ArticleDOI
Compensation of modeling errors due to unknown domain boundary in diffuse optical tomography.
TL;DR: The results show that the Bayesian approximation error method can be used to reduce artifacts in reconstructed images due to unknown domain shape.