Browsing by Author "Hajnal, Joseph V."

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    Motion estimation applied to reconstruct undersampled dynamic MRI
    (2007) Prieto Vásquez, Claudia; Guarini Hermann, Marcelo Walter; Hajnal, Joseph V.; Irarrázaval Mena, Pablo
    Magnetic Resonance Imaging (MRI) has become an important tool for dynamic clinical studies. Regrettably, the long acquisition time is still a challenge in dynamic MRI. Several undersampled reconstruction techniques have been developed to speed up the acquisition without significantly compromising image quality. Most of these methods are based on modeling the pixel intensity changes. Recently, we introduced a new approach based on the motion estimation of each object element (obel, a piece of tissue). Although the method works well, the outcome is a trade off between the maximum undersampling factor and the motion estimation accuracy. In this work we propose to improve its performance through the use of additional data from multiple coils acquisition. Preliminary results on cardiac MRI show that further undersampling and/or improved reconstruction accuracy is achieved using this technique. Furthermore, an approximation of the vector field of motion is obtained. This method is appropriate for sequences where the obels' intensity through time is nearly constant.
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    Reconstruction of undersampled dynamic images by modeling the motion of object elements
    (2007) Prieto Vásquez, Claudia; Batchelor, Philip G.; Hill, D.L.G.; Hajnal, Joseph V.; Guarini Hermann, Marcelo Walter; Irarrázaval Mena, Pablo
    Dynamic MRI is restricted due to the time required to obtain enough data to reconstruct the image sequence. Several undersampled reconstruction techniques have been proposed to reduce the acquisition time. In most of these techniques the nonacquired data are recovered by modeling the temporal information as varying pixel intensities represented in time or in temporal frequencies. Here we propose a new approach that recovers the missing data through a motion estimation of the object elements ('' obels,'' or pieces of tissue) of the image. This method assumes that an obel displacement through the sequence has lower bandwidth than fluctuations in pixel intensities caused by the motion, and thus it can be modeled with fewer parameters. Preliminary results show that this technique can effectively reconstruct (with root mean square (RMS) errors below 4%) cardiac images and joints with undersampling factors of 8 and 4, respectively. Moreover, in the reconstruction process an approximation of the motion vectors is obtained for each obel, which can be used to quantify dynamic information. In this method the motion need not be confined to a part of the field of view (FOV) or to a portion of the temporal frequency. It is appropriate for dynamic studies in which the obels' motion model has fewer parameters than the number of acquired samples.