Comparison of TSENSE, k-t SENSE and PINOT Fast Imaging Methods on Cine MRI
نویسندگان
چکیده
L. H. Hamilton, J. A. Fabregat, D. Moratal, S. Ramamurthy, S. Kozerke, and M. E. Brummer School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States, Universidad Politécnica de Valencia, Valencia, Spain, Children’s Healthcare of Atlanta, Atlanta, GA, United States, University and ETH Zurich, Zurich, Switzerland, School of Medicine, Emory University, Atlanta, GA, United States
منابع مشابه
"PINOT": time-resolved parallel magnetic resonance imaging with a reduced dynamic field of view.
This article introduces a novel method named "Parallel Imaging and Noquist in Tandem" (PINOT) for accelerated image acquisition of cine cardiac magnetic resonance imaging. This method combines two prior information formalisms, the SPACE-RIP implementation of parallel imaging and the Noquist method for reduced-data image reconstruction with prior knowledge of static and dynamic regions in the fi...
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Introduction Breath-hold cine MRI with balanced steady-steady free precession (b-SSFP) may yield non diagnostic image quality in patients with impaired breath-hold capacity and/or arrhythmias. In such patients, it may be necessary to perform real-time cine MRI. Currently, dynamic parallel imaging methods, such as TSENSE [1] and TGRAPPA [2], can be used to achieve only moderate acceleration rate...
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For patients with impaired breath-hold capacity and/or arrhythmias, real-time cine MRI may be more clinically useful than breath-hold cine MRI. However, commercially available real-time cine MRI methods using parallel imaging typically yield relatively poor spatio-temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm...
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TSENSE and TGRAPPA are autocalibrated parallel imaging techniques that can improve the temporal resolution and/or spatial resolution in dynamic magnetic resonance imaging applications. In its original form, TSENSE uses temporal low-pass filtering of the undersampled frames to create the sensitivity map. TGRAPPA uses a sliding-window moving average when finding the autocalibrating signals. Both ...
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