USING PRIOR KNOWLEDGE OF HUMAN ANATOMY TO CONSTRAIN MR IMAGE ACQUISITION AND RECONSTRUCTION - HALF K-SPACE AND FULL K-SPACE TECHNIQUES

In this note, we demonstrate how to utilize prior knowledge of human c ranial anatomy to constrain full k-space and half k-space acquisition and reconstruction of 128 times 128 images, We used a database of magn etic resonance head images to derive new basis functions which represe nt the most important features of the head, The ''training'' images we re also used to derive formulas for reconstructing head images from a subset of the usual 128 phase-encoded signals and to determine the opt imal k-space locations of those signal measurements, We used this algo rithm, called Feature-Recognizing MRI, to reconstruct 128 times 128 he ad images from 50-60% of the signals filling the full k-space, Further more, we combined the algorithm with a conventional half k-space techn ique to create 128 times 128 images from only 60% of the 80 signals re quired by the usual unconstrained half k-space imaging, Thus, the prio r knowledge represented by the image database, together with a half k- space technique, made it possible to construct accurate magnetic reson ance images from only 30-40% of the complete set of 128 signals, In ot her words, a database of head images was used to devise a 1/3k-space m ethod for imaging the head. (C) 1997 Elsevier Science Inc.