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Purpose: Ultrashort echo time (UTE) sequences play a key role in imaging and quantifying short T2 species. However, almost all of the relevant studies was conducted at relatively high fields. The purpose of this work was to further explore the feasibility of UTE imaging and T2* measurement for short T2 species at low fields.Methods: A 2D UTE sequence with an echo time (TE) of 0.37 ms was developed on a 0.35 T permanent magnet scanner. This sequence acquires multiecho images to fit the monoexponential signal decay model for quantitative T2* calculations. In the phantom experiments, MnCl2 solutions with different T2* values were used to assess the curve fitting model in low fields. In the in vivo experiments, T2* measurements were performed on the Achilles tendon of five normal volunteers.Results: The phantom studies showed a significant linear relationship between the MnCl2 solution concentration and R2* (1/T2*) values, which indicated the stability and accuracy of the T2* quantification model. The in vivo studies demonstrated that mean T2* value of Achilles tendon is 1.83 ± 0.21 ms, and the mean coefficient of determination (R-squared) was 0.996.Conclusions: Both phantom and in vivo experiments showed that UTE imaging and quantification for short T2 components were feasible at low field 0.35 T scanner. This pilot study presents preliminary conclusions for future work. [ABSTRACT FROM AUTHOR]

Compressed sensing magnetic resonance imaging (CS-MRI) greatly accelerates the acquisition process and yield considerable reconstructed images. Deep learning was introduced into CS-MRI to further speed up the reconstruction process and improve the image quality. Recently, generative adversarial network (GAN) using two-stage cascaded U-Net structure as generator has been proven to be effective in MRI reconstruction. However, previous cascaded structure was limited to few feature information propagation channels thus may lead to information missing. In this paper, we proposed a GAN-based model, DBGAN, for MRI reconstruction from undersampled k-space data. The model uses cross-stage skip connection (CSSC) between two end-to-end cascaded U-Net in our generator to widen the channels of feature propagation. To avoid discrepancy between training and inference, we replaced classical batch normalization (BN) with instance normalization (IN). A stage loss is involved in the loss function to boost the training performance. In addition, a bilinear interpolation decoder branch is introduced in the generator to supplement the missing information of the deconvolution decoder. Tested under five variant patterns with four undersampling rates on different modality of MRI data, the quantitative results show that DBGAN model achieves mean improvements of 3.65 dB in peak signal-to-noise ratio (PSNR) and 0.016 in normalized mean square error (NMSE) compared with state-of-the-art GAN-based methods on T1-Weighted brain dataset from MICCAI 2013 grand challenge. The qualitative visual results show that our method can reconstruct considerable images on brain and knee MRI data from different modality. Furthermore, DBGAN is light and fast – the model parameters are fewer than half of state-of-the-art GAN-based methods and each 256 × 256 image is reconstructed in 60 milliseconds, which is suitable for real-time processing. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is widely used to get the information of anatomical structure and physiological function with the advantages of high resolution and non-invasive scanning. But the long acquisition time limits its application. To reduce the time consumption of MRI, compressed sensing (CS) theory has been proposed to reconstruct MRI images from undersampled k-space data. But conventional CS methods mostly use iterative methods that take lots of time. Recently, deep learning methods are proposed to achieve faster reconstruction, but most of them only pay attention to a single domain, such as the image domain or k-space. To take advantage of the feature representation in different domains, we propose a cross-domain method based on deep learning, which first uses convolutional neural networks (CNNs) in the image domain, k-space and wavelet domain simultaneously. The combined order of the three domains is also first studied in this work, which has a significant effect on reconstruction. The proposed IKWI-net achieves the best performance in various combinations, which utilizes CNNs in the image domain, k-space, wavelet domain and image domain sequentially. Compared with several deep learning methods, experiments show it also achieves mean improvements of 0.91 dB in peak signal-to-noise ratio (PSNR) and 0.005 in structural similarity (SSIM). [ABSTRACT FROM AUTHOR]

• This work proposes a DOI rebinning method combining the correlation of LORs with the prior activity distribution to assign coincidence events to the rebinned sinogram. • The weights used for coincidence events assignment includes two parts: geometric weights calculated based on the areas of intersection, and activity weights derived from the prior activity distribution for improving the assignment probability. • The proposed DOI rebinning method achieves relatively optimal image quality while reducing the number of reconstruction iterations. For positron emission tomography (PET) scanners with depth-of-interaction (DOI) measurement, the DOI rebinning method that utilizes DOI information to process the projection data is critical to image quality. Current DOI rebinning methods map coincidence events onto the rebinned sinogram based on the correlation of lines of response (LOR). This study aims to incorporate prior radioactivity distribution of the imaging object into DOI rebinning to obtain better image quality. A DOI rebinning method based on both geometric and activity weights was proposed to assign coincidence events to the rebinned sinogram defined by a virtual ring. The geometric weights, representing the correlation between LORs, were calculated based on the areas of intersection. The activity weights, reflecting the activity distribution of the imaging object, were derived from the previous reconstructed image. Monte Carlo simulation data from four phantoms, including the image quality phantom, Derenzo phantom, and two rat-like ROBY phantoms, was used to evaluate the proposed method. The recovery coefficient (RC), contrast recovery coefficient (CRC), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) were used as image quality metrics. Compared to other DOI rebinning methods, the proposed method achieved the highest RC (maximum improvement of 32%) and CRC at the same noise level and was also optimal in terms of the SSIM and PSNR. Meanwhile, incorporating the prior activity distribution into DOI rebinning also improved the image reconstruction speed. This work developed a new DOI rebinning method combining the correlation of LORs with the prior activity distribution, achieving relatively optimal image quality and reconstruction speed. Furthermore, it still needs to be evaluated on the actual equipment. [ABSTRACT FROM AUTHOR]

Abstract: Joint estimation of coil sensitivities and output image (JSENSE) is a promising approach that improves the reconstruction of parallel magnetic resonance imaging (pMRI). However, when acceleration factor increases, the signal to noise ratio (SNR) of JSENSE reconstruction decreases as quickly as that of the conventional pMRI. Although sparse constraints have been used to improve the JSENSE reconstruction in recent years, these constraints only use the sparsity of the output image, which cannot fully exploit the prior information of pMRI. In this paper, we use the sparsity of coil images, instead of the output image, to exploit more prior information for JSENSE. Numerical simulation, phantom and in vivo experiments demonstrate that the proposed method has better performance than the SparseSENSE method and the constrained JSENSE method using the sparsity of the output image only. [Copyright &y& Elsevier]

Abstract: A recently developed partially separable functions (PSF) model can be used to generate high-resolution dynamic magnetic resonance imaging (MRI). However, this method could not robustly reconstruct high-quality MR images because the estimation of the PSF parameters is often interfered by the noise of the sampled MR data. To improve the robustness of MRI reconstruction using the PSF model, we proposed a new algorithm to estimate the PSF parameters by jointly using robust principal component analysis and modified truncated singular value decomposition regularization methods, instead of using the least square fitting method in the original PSF model. The experiment results of in vivo cardiac MRI demonstrated that the proposed algorithm can robustly reconstruct dynamic MR images with higher signal-to-noise ratio and clearer anatomical structures in comparison with the previous PSF model. [Copyright &y& Elsevier]

Abstract: Purpose: This study aimed to develop a 0.014-in., anti-solenoid loop (ASL) magnetic resonance imaging guidewire (MRIG) for intravascular 3.0-T MR imaging. Materials and Methods: We first designed the ASL MRIG, which was made of a coaxial cable with its extended inner conductor and outer conductor connected to two micro-anti-solenoids. We then evaluated in vitro the functionality of the ASL MRIG by imaging a “vessel” in a phantom and achieving signal-to-noise ratio (SNR) and SNR contour map of the new 0.014-in. ASL MRIG. Subsequently, we validated in vivo the feasibility of using the ASL MRIG to generate intravenous 3.0-T MR images of parallel iliofemoral arteries of near-human-sized living pigs. Results: In vitro evaluation showed that the 0.014-in. ASL MRIG functioned well as a receiver coil with the 3.0-T MR scanner, clearly displaying the vessel wall with even distribution of MR signals and SNR contours from the ASL MRIG. Of the in vivo studies, the new ASL MRIG enabled us to successfully generate intravenous 3.0-T MR imaging of the iliofemoral arteries. Conclusion: This study confirms that it is possible to build such small-looped MRIG at 0.014 in. for intravascular 3.0-T MR imaging. [Copyright &y& Elsevier]