Purpose To optimize a steady‐state imaging sequence for maximizing the amide proton transfer effects in pulsed‐CEST (pCEST) imaging. Method The steady‐state pCEST (SS‐pCEST) sequence is a fast CEST imaging scheme that applies repetitive short RF pulses for generating CEST and acquiring MR imaging signal alternately. To maximize the obtainable amide proton transfer effects, the SS‐pCEST scheme is analyzed and optimized with respect to not only the imaging parameters but also the imaging schemes of the signal acquisition part. Three imaging parameters such as the flip angle and RF power for saturation and the flip angle for imaging are selected as factors affecting the obtainable CEST effects; and 2 imaging schemes, namely, SSFP and spoiled gradient echo sequences, are analyzed and compared for numerical simulations and MRI experiments at 3 tesla. Results SS‐pCEST combined with SSFP could provide higher amide proton transfer effects than that with spoiled gradient echo. Furthermore, in the proposed SS‐pCEST imaging with SSFP, 3 imaging parameters can be independently optimized so that the optimization complexities can be reduced. Conclusion We optimized the SS‐pCEST imaging method with SSFP to maximize the amide proton transfer effects. In addition, our analysis showed the SSFP sequence was more efficient than the spoiled gradient echo sequence for SS‐pCEST imaging.

Purpose Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion‐free spin‐ and stimulated‐echo signals and combine the signals with a physics‐driven unsupervised network to estimate T1, T2, and proton density (M0) parameter maps, along with B0 and B1 information from the acquired signals. Theory and Methods An imaging sequence with three 90° RF pulses is utilized to acquire spin‐ and stimulated‐echo signals. We utilize blip‐up/‐down acquisition to eliminate geometric distortion incurred by the effects of B0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo‐shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin‐ and stimulated‐echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. Results The proposed acquisition provided distortion‐free T1, T2, relative proton density (M0), B0, and B1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin‐ and stimulated‐echo signals, analytic fitting and the network‐based method were able to estimate T1, T2, M0, B0, and B1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. Conclusion The proposed acquisition/reconstruction technique enabled whole‐brain acquisition of coregistered, distortion‐free, T1, T2, M0, B0, and B1 maps at 1 × 1 × 5 mm3 resolution in 50 s. The proposed unsupervised neural network provided noise‐robust parameter estimates from this rapid acquisition.

The proposed method can be a useful tool for clinical diagnosis, such as the cartilage segmentation and the fast screening of lesions.

No abstract

Purpose: A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil. Theory and Methods: A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k-space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k-space data that consists of the sampled data and the estimated data at unsampled points. Results: In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals. Conclusion: The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts. K E Y W O R D Saliasing artifacts, locally segmented reconstruction, multichannel receiver coil, parallel imaging