Authors: Gengsheng L. Zeng, Xiaodong Ma, Chun Yuan.
Motivation: In cine MRI, the measurements within each timeframe alone are too noisy for image reconstruction. Some information must be ‘borrowed’ from other time frames and the reconstruction algorithm is a slow iterative procedure.
Goals: We set up a constrained objective function, which uses the measurements at other time frames to regularize the image reconstruction. We derive a non-iterative algorithm to minimize this objective function.
Approach: The derivation of the algorithm is based on the calculus of variations. The resultant algorithm is in the form of filtered backprojection.
Results: The feasibility of the proposed algorithm is demonstrated with a clinical patient brain study.
Impact: Non-iterative reconstruction that minimizes a constrained objective function significantly increases the throughput in healthcare institutions. This may translate to reduced healthcare costs. The new reconstruction formula has a closed form that gives an explicit expression of how to incorporate the reference image in dynamic reconstruction.
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