Search

Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial ${\rm T}_{2}$ mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial ${\rm T}_{2}$ values were in good agreement with the reference values obtained from multiple breath-holds (62.5 $\pm$ 11.1 ms against 62.2 $\pm$ 11.2 ms which was not significant with ${\rm p}=0.49$ ).

Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).