A fast chemical exchange saturation transfer imaging scheme based on single-shot spatiotemporal encoding

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI

One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to accelerate MRI acquisition, while this could introduce artifacts and reduce signal-to-noise ratio (SNR). To accelerate CEST-MRI acquisition while maintaining suitable image quality, we proposed an attention-based multioffset deep learning reconstruction network (AMO-CEST) with a multiple radial k-space sampling strategy for CEST-MRI. The AMO-CEST also contains dilated convolution to enlarge the receptive field and data consistency module to preserve the sampled k-space data. We evaluated the proposed method on a mouse brain dataset containing 5760 CEST images acquired at a pre-clinical 3 T MRI scanner. Quantitative results demonstrated that AMO-CEST showed obvious improvement over zero-filling method with a PSNR enhancement of 11 dB, a SSIM enhancement of 0.15, and a NMSE decrease of [Formula: see text] in three acquisition orientations. Compared with other deep learning-based models, AMO-CEST showed visual and quantitative improvements in images from three different orientations. We also extracted molecular contrast maps, including the amide proton transfer (APT) and the relayed nuclear Overhauser enhancement (rNOE). The results demonstrated that the CEST contrast maps derived from the CEST images of AMO-CEST were comparable to those derived from the original high-resolution CEST images. The proposed AMO-CEST can efficiently reconstruct high-quality CEST images from under-sampled k-space data and thus has the potential to accelerate CEST-MRI acquisition.

Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging

Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.

Accelerating CEST MRI in the mouse brain at 9.4 T by exploiting sparsity in the Z-spectrum domain

PURPOSE

Chemical exchange saturation transfer (CEST) is an MR contrast modality offering an enhanced sensitivity for the detection of dilute metabolites with exchangeable protons. Quantitative analysis requires the acquisition of a number of images (usually between 20 and 50 RF offsets) per Z-spectrum, leading to long acquisition times of the order of 5-40 min in practice. In this work, we explore the possibility of employing sparsity in the Z-spectrum domain (irradiation offset dimension) to provide an accelerated acquisition scheme without compromising the quality of reconstructed CEST spectra.

METHOD AND THEORY

Ex vivo and in vivo data were acquired on an experimental, small animal 9.4 T system. Three different reconstruction methods were tested: k-Z SPARSE, k-Z SLR and k-Z principal component analysis (PCA) using retrospective undersampling with net acceleration factors R = 2, 3, 5. The quality of the reconstructed data was compared with respect to CEST spectra and full magnetization transfer ratio (MTR) asymmetry maps.

RESULTS

In both phantom and in vivo data, CEST spectra and the resulting MTR asymmetry maps were reconstructed without significant deterioration in data quality. For a low acceleration factor (R = 2, 3) all applied methods resulted in similar data quality, while for high acceleration factor (R = 5) only k-Z PCA and k-Z SLR could be used. Loss in spatial resolution was observed in reconstruction with k-Z PCA for all acceleration factors. An example of prospective undersampling with acceleration factor R = 3 and k-Z PCA reconstruction demonstrates improved CEST maps when compared with fully sampled data acquisition with either three times longer scan duration or threefold prolonged acquisition window per frequency offset.

CONCLUSION

The acquisition time of CEST spectra can be significantly accelerated by exploiting the sparsity of the Z-domain. For prospective and retrospective analysis using k-Z PCA, an acceleration factor of up to R = 3 can be used without significant loss in data quality.

Super-resolved water/fat image reconstruction based on single-shot spatiotemporally encoded MRI

Single-shot spatiotemporally encoded (SPEN) MRI has been validated to possess considerable performance in both spatial and temporal resolution. Water/fat separation is essential for MRI applications in which only water signal is needed. In this article, a super-resolved water/fat image reconstruction method (dubbed SWAF) combined prior knowledge was developed based on single-shot SPEN MRI. The point spread function of spatiotemporal encoding under multiple chemical shifts situation was derived and used for constructing an equation for SWAF image reconstruction. By processing the prior chemical shift information with filtering operation, an initial spin density profile of water/fat and a weighting matrix for water/fat residual artifacts suppression were obtained to guide the reconstruction process. A l₁ norm minimization problem with regularization was exploited to reconstruct separated water/fat images with high spatial resolution and less residual/aliasing artifacts. Numeric simulation and experiments on water-oil phantom and rat abdomen/neck imaging demonstrated the effectiveness and robustness of this new method. The SWAF method proposed herein would promote the application of SPEN MRI in the cases where water/fat separation is required.

Ultrafast multi-slice chemical exchange saturation transfer imaging scheme based on segmented spatiotemporal encoding

Chemical exchange saturation transfer (CEST) imaging is an important magnetic resonance molecular imaging technology. However, long acquisition time limits its clinical application, especially when multi-slice CEST imaging is needed. Though single-shot EPI can be used to accelerate CEST imaging, images are often distorted under inhomogeneous magnetic fields. In this work, we propose a new method called CEST-SeSPEN for ultrafast multi-slice CEST imaging based on segmented spatiotemporally encoded (SeSPEN) MRI. Experiments were performed on creatine phantom and hen egg. The results show that CEST-SeSPEN can provide good CEST contrast images. Its acquisition time is much shorter than other multi-slice CEST methods currently available. It may be used in challenging situation where high temporal resolution and robustness to field inhomogeneity are vital.