Christoph Forman1, Davide Piccini, Robert Grimm, Jana Hutter, Joachim Hornegger, Michael O Zenge. 1. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany; Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.
Abstract
PURPOSE: To combine weighted iterative reconstruction with self-navigated free-breathing coronary magnetic resonance angiography for retrospective reduction of respiratory motion artifacts. METHODS: One-dimensional self-navigation was improved for robust respiratory motion detection and the consistency of the acquired data was estimated on the detected motion. Based on the data consistency, the data fidelity term of iterative reconstruction was weighted to reduce the effects of respiratory motion. In vivo experiments were performed in 14 healthy volunteers and the resulting image quality of the proposed method was compared to a navigator-gated reference in terms of acquisition time, vessel length, and sharpness. RESULT: Although the sampling pattern of the proposed method contained 60% more samples with respect to the reference, the scan efficiency was improved from 39.5 ± 10.1% to 55.1 ± 9.1%. The improved self-navigation showed a high correlation to the standard navigator signal and the described weighting efficiently reduced respiratory motion artifacts. Overall, the average image quality of the proposed method was comparable to the navigator-gated reference. CONCLUSION: Self-navigated coronary magnetic resonance angiography was successfully combined with weighted iterative reconstruction to reduce the total acquisition time and efficiently suppress respiratory motion artifacts. The simplicity of the experimental setup and the promising image quality are encouraging toward future clinical evaluation.
PURPOSE: To combine weighted iterative reconstruction with self-navigated free-breathing coronary magnetic resonance angiography for retrospective reduction of respiratory motion artifacts. METHODS: One-dimensional self-navigation was improved for robust respiratory motion detection and the consistency of the acquired data was estimated on the detected motion. Based on the data consistency, the data fidelity term of iterative reconstruction was weighted to reduce the effects of respiratory motion. In vivo experiments were performed in 14 healthy volunteers and the resulting image quality of the proposed method was compared to a navigator-gated reference in terms of acquisition time, vessel length, and sharpness. RESULT: Although the sampling pattern of the proposed method contained 60% more samples with respect to the reference, the scan efficiency was improved from 39.5 ± 10.1% to 55.1 ± 9.1%. The improved self-navigation showed a high correlation to the standard navigator signal and the described weighting efficiently reduced respiratory motion artifacts. Overall, the average image quality of the proposed method was comparable to the navigator-gated reference. CONCLUSION: Self-navigated coronary magnetic resonance angiography was successfully combined with weighted iterative reconstruction to reduce the total acquisition time and efficiently suppress respiratory motion artifacts. The simplicity of the experimental setup and the promising image quality are encouraging toward future clinical evaluation.
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