PURPOSE: Despite numerous publications describing the ability of prospective motion correction to improve image quality in magnetic resonance imaging of the brain, a reliable approach to assess this improvement is still missing. A method that accurately reproduces motion artifacts correctable with prospective motion correction is developed, and enables the quantification of the improvements achieved. METHODS: A software interface was developed to simulate rigid body motion by changing the scanning coordinate system relative to the object. Thus, tracking data recorded during a patient scan can be used to reproduce the prevented motion artifacts on a volunteer or a phantom. The influence of physiological motion on image quality was investigated by filtering these data. Finally, the method was used to reproduce and quantify the motion artifacts prevented in a patient scan. RESULTS: The accuracy of the method was tested in phantom experiments and in vivo. The calculated quality factor, as well as a visual inspection of the reproduced artifacts shows a good correspondence to the original. CONCLUSION: Precise reproduction of motion artifacts assists qualification of prospective motion correction strategies. The presented method provides an important tool to investigate the effects of rigid body motion on a wide range of sequences, and to quantify the improvement in image quality through prospective motion correction.
PURPOSE: Despite numerous publications describing the ability of prospective motion correction to improve image quality in magnetic resonance imaging of the brain, a reliable approach to assess this improvement is still missing. A method that accurately reproduces motion artifacts correctable with prospective motion correction is developed, and enables the quantification of the improvements achieved. METHODS: A software interface was developed to simulate rigid body motion by changing the scanning coordinate system relative to the object. Thus, tracking data recorded during a patient scan can be used to reproduce the prevented motion artifacts on a volunteer or a phantom. The influence of physiological motion on image quality was investigated by filtering these data. Finally, the method was used to reproduce and quantify the motion artifacts prevented in a patient scan. RESULTS: The accuracy of the method was tested in phantom experiments and in vivo. The calculated quality factor, as well as a visual inspection of the reproduced artifacts shows a good correspondence to the original. CONCLUSION: Precise reproduction of motion artifacts assists qualification of prospective motion correction strategies. The presented method provides an important tool to investigate the effects of rigid body motion on a wide range of sequences, and to quantify the improvement in image quality through prospective motion correction.
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