Modern graphics processing units work efficiently in the RF Pulse Design for MR image reconstruction, which could make the reconstruction of images quicker with higher signal to noise ratio (SNR) and higher resolution. This paper introduces using GPU techniques, such as multithreads, sharedMem, coalesce, and constMem to optimize the performance of the conjugate gradient least square algorithm for reconstructing MR images. Compared to a full CPU implementation, a version utilizing the conjugate gradient least square algorithm on the NVIDIA Tesla C 1060 is significantly faster. The total image reconstruction time, including all CPU to GPU setup and overhead, also improves dramatically.

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Since their inceptions, the multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. CT and MRI images are synchronously acquired and registered from a hybrid CT-MRI platform. Because image data are highly undersampled, analytic methods are unable to generate decent image quality. To overcome this drawback, we resort to the compressed sensing (CS) techniques, which employ sparse priors that result from an application of a wavelet transform. To utilize multimodal information, projection distance is introduced and is tuned to tailor the texture and pattern of the final images. Specifically, CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. The method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The good performance of the proposed approach is demonstrated on a pair of undersampled CT and MRI body images. Clinical CT and MRI images are tested with the joint reconstruction, the analytic reconstruction, and the independent reconstruction which does not uses multimodal imaging information. Results show that the proposed method improves about 5dB in signal to noise ratio (SNR) and nearly 10% in structural similarity measure comparing to independent reconstruction. It offers similar quality with fully sampled analytic reconstruction with only 20% sampling rate for CT and 40% for MRI. Structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction.

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