Fast single image super-resolution using estimated low-frequency k-space data in MRI

Application and interpretation of vessel wall magnetic resonance imaging for intracranial atherosclerosis: a narrative review

Background and Objective

Atherosclerosis is a systemic disease that occurs in the arteries, and it is the most important causative factor of ischemic stroke. Vessel wall magnetic resonance imaging (VWMRI) is one of the best non-invasive methods for displaying the vascular features of intracranial atherosclerosis. The main clinical applications of this technique include the exploration of the pathogenesis of intracranial atherosclerotic lesions, follow-up monitoring, and treatment prognosis judgment. As the demand for intracranial VWMRI increases in clinical practice, radiologists should be aware of the selection of imaging parameters and how they affect image quality, clinical indications, evaluation methods, and limitations in interpreting these images. Therefore, this review focused on describing how to perform and interpret VWMRI of intracranial atherosclerotic lesions.

Methods

We searched the studies on the application of VWMRI in the PubMed database from January 1, 2000 to March 31, 2022, and focused on the analysis of related studies on VWMRI in atherosclerotic lesions, including technical application, expert consensus, imaging characteristics, and the clinical significance of intracranial atherosclerotic lesions.

Key Content and Findings

We reviewed and summarized recent advances in the clinical application of VWMRI in atherosclerotic diseases. Currently accepted principles and expert consensus recommendations for intracranial VWMRI include high spatial resolution, multiplanar two and three-dimensional imaging, multiple tissue-weighted sequences, and blood and cerebrospinal fluid suppression. Understanding the characteristics of VWMRI of normal intracranial arteries is the basis for interpreting VWMRI of atherosclerotic lesions. Evaluating VWMRI imaging features of intracranial atherosclerotic lesions includes plaque morphological and enhancement characteristics. The evaluation of atherosclerotic plaque stability is the highlight of VWMRI.

Conclusions

VWMRI has a wide range of clinical applications and can address important clinical questions and provide critical information for treatment decisions. VWMRI plays a key role in the comprehensive evaluation and prevention of intracranial atherosclerosis. However, intracranial VWMRI is still unable to obtain in vivo plaque pathological specimens for imaging—pathological comparison is the most significant limitation of this technique. Further technical improvements are expected to reduce acquisition time and may ultimately contribute to a better understanding of the underlying pathology of lesions on VWMRI.

MRI super-resolution via realistic downsampling with adversarial learning

Many deep learning (DL) frameworks have demonstrated state-of-the-art performance in the super-resolution (SR) task of magnetic resonance imaging, but most performances have been achieved with simulated low-resolution (LR) images rather than LR images from real acquisition. Due to the limited generalizability of the SR network, enhancement is not guaranteed for real LR images because of the unreality of the training LR images. In this study, we proposed a DL-based SR framework with an emphasis on data construction to achieve better performance on real LR MR images. The framework comprised two steps: (a) downsampling training using a generative adversarial network (GAN) to construct more realistic and perfectly matched LR/high-resolution (HR) pairs. The downsampling GAN input was real LR and HR images. The generator translated the HR images to LR images and the discriminator distinguished the patch-level difference between the synthetic and real LR images. (b) SR training was performed using an enhance4d deep super-resolution network (EDSR). In the controlled experiments, three EDSRs were trained using our proposed method, Gaussian blur, and k-space zero-filling. As for the data, liver MR images were obtained from 24 patients using breath-hold serial LR and HR scans (only HR images were used in the conventional methods). The k-space zero-filling group delivered almost zero enhancement on the real LR images and the Gaussian group produced a considerable number of artifacts. The proposed method exhibited significantly better resolution enhancement and fewer artifacts compared with the other two networks. Our method outperformed the Gaussian method by an improvement of 0.111 ± 0.016 in the structural similarity index and 2.76 ± 0.98 dB in the peak signal-to-noise ratio. The blind/reference-less image spatial quality evaluator metric of the conventional Gaussian method and proposed method were 46.6 ± 4.2 and 34.1 ± 2.4, respectively.

FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution

High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super- resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images. In the framework of the FA-GAN, the local fusion feature block, consisting of different three-pass networks by using different convolution kernels, is proposed to extract image features at different scales. And the global feature fusion module, including the channel attention module, the self-attention module, and the fusion operation, is designed to enhance the important features of the MR image. Moreover, the spectral normalization process is introduced to make the discriminator network stable. 40 sets of 3D magnetic resonance images (each set of images contains 256 slices) are used to train the network, and 10 sets of images are used to test the proposed method. The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed FA-GAN method are higher than the state-of-the-art reconstruction methods.

Evaluation of super-resolution on 50 pancreatic cancer patients with real-time cine MRI from 0.35T MRgRT

MR-guided radiotherapy (MRgRT) systems provide excellent soft tissue imaging immediately prior to and in real time during radiation delivery for cancer treatment. However, 2D cine MRI often has limited spatial resolution due to high temporal resolution. This work applies a super resolution machine learning framework to 3.5 mm pixel edge length, low resolution (LR), sagittal 2D cine MRI images acquired on a MRgRT system to generate 0.9 mm pixel edge length, super resolution (SR), images originally acquired at 4 frames per second (FPS). LR images were collected from 50 pancreatic cancer patients treated on a ViewRay MR-LINAC. SR images were evaluated using three methods. 1) The first method utilized intrinsic image quality metrics for evaluation. 2) The second used relative metrics including edge detection and structural similarity index (SSIM). 3) Finally, automatically generated tumor contours were created on both low resolution and super resolution images to evaluate target delineation and compared with DICE and SSIM. Intrinsic image quality metrics all had statistically significant improvements for SR images versus LR images, with mean (±1 SD) BRISQUE scores of 29.65 ± 2.98 and 42.48 ± 0.98 for SR and LR, respectively. SR images showed good agreement with LR images in SSIM evaluation, indicating there was not significant distortion of the images. Comparison of LR and SR images with paired high resolution (HR) 3D images showed that SR images had a mean (±1 SD) SSIM value of 0.633 ± 0.063 and LR a value of 0.587 ± 0.067 (p ≪ 0.05). Contours generated on SR images were also more robust to noise addition than those generated on LR images. This study shows that super resolution with a machine learning framework can generate high spatial resolution images from 4fps low spatial resolution cine MRI acquired on the ViewRay MR-LINAC while maintaining tumor contour quality and without significant acquisition or post processing delay.

Structure Prior Constrained Estimation of Human Cardiac Diffusion Tensors

OBJECTIVE

The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions.

METHODS

A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator. The two regularizers penalize the dissimilarity between neighboring DTs and the difference between estimated and prior fiber orientations, respectively. A novel numerical solution is presented to ensure the positive definite estimation.

RESULTS

Experiments on ex vivo human cardiac data show that the SPC method is able to well estimate DTs at most voxels, and is superior to state-of-the-art methods in terms of the mean errors of principal eigenvector, second eigenvector, helix angle, transverse angle, fractional anisotropy, and mean diffusivity.

CONCLUSION

The SPC method is a practical and reliable alternative to current denoising- or regularization-based methods for the estimation of human cardiac DT.

SIGNIFICANCE

The SPC method is able to accurately estimate human cardiac DTs in dMRI with a few diffusion gradient directions.