no code implementations • 2 Jan 2024 • Lorenzo Venturini, Samuel Budd, Alfonso Farruggia, Robert Wright, Jacqueline Matthew, Thomas G. Day, Bernhard Kainz, Reza Razavi, Jo V. Hajnal
We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers.
1 code implementation • 2 May 2018 • Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew C. H. Lee, Amir Alansary, Steven McDonagh, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
In this paper, we propose a general Riemannian formulation of the pose estimation problem.
no code implementations • 19 Sep 2017 • Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.
no code implementations • 28 Feb 2017 • Steven McDonagh, Benjamin Hou, Konstantinos Kamnitsas, Ozan Oktay, Amir Alansary, Mary Rutherford, Jo V. Hajnal, Bernhard Kainz
Fast imaging is required for targets that move to avoid motion artefacts.
1 code implementation • 28 Feb 2017 • Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.