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Abstract #0126

Automated Selection of 2D-CAIPIRINHA Kernels and Application to 3D CE-MRA

Paul T. Weavers1, Eric A. Borisch1, Stephen J. Riederer1

1MR Research Laboratory, Mayo Clinic, Rochester, MN, United States

2D-CAIPIRINHA has been shown to reduce noise amplification when compared to traditional 2D-SENSE. However at high acceleration (R≥8) it is not clear which kernel will best accomplish this. An automated method to select the optimal kernel in a receiver coil and patient-specific manner in less than ten seconds has been developed. It has been validated with a retrospective study of nine sets of 3D foot exams, as well as in a prospective 3D contrast-enhanced MRA study.

Keywords

abdominal able accelerated acceleration accelerations accommodates acquisition additionally adjusting aliasing allowing allows already amplification anatomical angiography another application arrow arrows automated available axes axial become best built calibration choosing chosen clinic coil comprehensive computation conducted considered consistently contours contrast cost custom days degradation denoted designated determined develop developed directed discussed done encode encoding enhanced ensure entire equivalent evaluated exam examination example exams feet foot frequency function green hardware heel identify illustrates immediate implementation impractical improved improvement indicate indicated intensity kernel kernels laboratory latter made magma mayo median methodology metric moving necessary nine noise note noted optimal optimization optimized optimum part particularly patient pattern patterns percentile practical previously principal process projecting projection promise prospective prospectively qualitatively quality reduced reducing reduction related relatively representative resolved retrospective sampling scalar scanner selection sense sensitivities sensitivity several space statistics studies subject subjected tolerant traditional tuning twice type types vary vessel vessels visibility visible volume volunteer weavers