Prior image based temporally constrained reconstruction algorithm for magnetic resonance guided high intensity
Jaya Prakash1, Nick Todd2, Phaneendra K Yalavarthy3
1Institute of Biological and Medical Imaging, Helmholtz Zentrum Munich, Ingolstaedter Landstraße 1, Munich D-85764, Germany and Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India.
Purpose:
A prior image based temporally constrained reconstruction (PITCR) algorithm was developed for obtaining accurate temperature maps having better volume coverage, and spatial, and temporal resolution than other algorithms for highly undersampled data in magnetic resonance (MR) thermometry.
Methods:
The proposed PITCR approach is an algorithm that gives weight to the prior image and performs accurate reconstruction in a dynamic imaging environment. The PITCR method is compared with the temporally constrained reconstruction (TCR) algorithm using pork muscle data.
Results:
The PITCR method provides superior performance compared to the TCR approach with highly undersampled data. The proposed approach is computationally expensive compared to the TCR approach, but this could be overcome by the advantage of reconstructing with fewer measurements. In the case of reconstruction of temperature maps from 16% of fully sampled data, the PITCR approach was 1.57× slower compared to the TCR approach, while the root mean square error using PITCR is 0.784 compared to 2.815 with the TCR scheme.
Conclusions:
The PITCR approach is able to perform more accurate reconstructions of temperature maps compared to the TCR approach with highly undersampled data in MR guided high intensity focused ultrasound.
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