Filter design for phased-array mr image reconstruction using super algorithm

Receiver coils in magnetic resonance (MR) systems are used to acquire the signals emitted by the nuclei. Surface coils provide a high signal-to-noise ratio due to their small sensitive region but the usable field of view is also limited to the size of the sensitive region. The use of array coils permits to increase the sensitive region. The outputs from the receiver channels are combined to construct a single composite image from the data of many coils. The image reconstruction is usually performed by using root sum-of-squares (RSS) method, which combines data without the knowledge of the coils sensitivity but it is known to introduce bias in low-signal regions. SUPER reconstruction permits to yield a composite image with higher contrast between high- and low-signal regions than RSS algorithm, by preliminary estimating coil sensitivities using low-pass filtering of original images. The purpose of this work is to introduce a theory for obtaining estimated coil sensitivity maps from the individual coil images and successively combining data from array elements using SUPER algorithm. Performance evaluation and comparison with RSS reconstruction were carried out with MR acquisitions performed using a 32-elements cardiac phased-array coil in a 3 T scanner. © 2012 Wiley Periodicals, Inc.