TY - GEN
T1 - Finite Radial Reconstruction for Magnetic Resonance Imaging
T2 - 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
AU - Chandra, Shekhar S.
AU - Archchige, Ramitha
AU - Ruben, Gary
AU - Jin, Jin
AU - Li, Mingyan
AU - Kingston, Andrew M.
AU - Svalbe, Imants
AU - Crozier, Stuart
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/22
Y1 - 2016/12/22
N2 - Magnetic resonance (MR) imaging is an important imaging modality for diagnostic medicine due to its ability to visualize the soft tissue of the human body in three dimensions (3D) without ionizing radiation. MR imaging however, typically relies on measurement techniques that do not exploit the geometry of discrete Fourier space, so called k-space, where the measurements are made. In this work, we firstly present a novel k-space tiling scheme that utilizes the finite geometry of discrete Fourier space via the discrete Fourier slice theorem. This produces a sampling of k-space that is pseudo-radial, to be more noise and patient-movement tolerant, and pseudo-random, further improving robustness to noise, whilst sampling with sufficient density near the central k-space region, where the majority of power of anatomical images lies. Secondly, we introduce a stable and iterative discrete reconstruction scheme for recovering images from their limited k-space measurements (in the form discrete slices) based on the maximum likelihood expectation maximization approach. We study the performance of the proposed approach using simulated MR measurements in k-space.
AB - Magnetic resonance (MR) imaging is an important imaging modality for diagnostic medicine due to its ability to visualize the soft tissue of the human body in three dimensions (3D) without ionizing radiation. MR imaging however, typically relies on measurement techniques that do not exploit the geometry of discrete Fourier space, so called k-space, where the measurements are made. In this work, we firstly present a novel k-space tiling scheme that utilizes the finite geometry of discrete Fourier space via the discrete Fourier slice theorem. This produces a sampling of k-space that is pseudo-radial, to be more noise and patient-movement tolerant, and pseudo-random, further improving robustness to noise, whilst sampling with sufficient density near the central k-space region, where the majority of power of anatomical images lies. Secondly, we introduce a stable and iterative discrete reconstruction scheme for recovering images from their limited k-space measurements (in the form discrete slices) based on the maximum likelihood expectation maximization approach. We study the performance of the proposed approach using simulated MR measurements in k-space.
KW - Fourier slice theorem
KW - MLEM
KW - MRI
KW - finite Radon transform
KW - image reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85011105192&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2016.7797043
DO - 10.1109/DICTA.2016.7797043
M3 - Conference contribution
T3 - 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
BT - 2016 International Conference on Digital Image Computing
A2 - Liew, Alan Wee-Chung
A2 - Zhou, Jun
A2 - Gao, Yongsheng
A2 - Wang, Zhiyong
A2 - Fookes, Clinton
A2 - Lovell, Brian
A2 - Blumenstein, Michael
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 November 2016 through 2 December 2016
ER -