Parallel imaging in time‐of‐flight magnetic resonance angiography using deep multistream convolutional neural networks

Abstract

Purpose To develop and evaluate a method of parallel imaging time‐of‐flight (TOF) MRA using deep multistream convolutional neural networks (CNNs). Methods A deep parallel imaging network (“DPI‐net”) was developed to reconstruct 3D multichannel MRA from undersampled data. It comprises 2 deep‐learning networks: a network of multistream CNNs for extracting feature maps of multichannel images and a network of reconstruction CNNs for reconstructing images from the multistream network output feature maps. The images were evaluated using normalized root mean square error (NRMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity (SSIM) values, and the visibility of blood vessels was assessed by measuring the vessel sharpness of middle and posterior cerebral arteries on axial maximum intensity projection (MIP) images. Vessel sharpness was compared using paired t tests, between DPI‐net, 2 conventional parallel imaging methods (SAKE and ESPIRiT), and a deep‐learning method (U‐net). Results DPI‐net showed superior performance in reconstructing vessel signals in both axial slices and MIP images for all reduction factors. This was supported by the quantitative metrics, with DPI‐net showing the lowest NRMSE, the highest PSNR and SSIM (except R = 3.8 on sagittal MIP images, and R = 5.7 on axial slices and sagittal MIP images), and significantly higher vessel sharpness values than the other methods. Conclusion DPI‐net was effective in reconstructing 3D TOF MRA from highly undersampled multichannel MR data, achieving superior performance, both quantitatively and qualitatively, over conventional parallel imaging and other deep‐learning methods.

Deep‐learned short tau inversion recovery imaging using multi‐contrast MR images

“…There are some studies trying to reduce scan time of single contrast MRI. [47][48] In this study, we show that an image generation method with a different contrast can be obtained from several other contrast images without the need for additional scanning, which could reduce costs. This approach potentially shortens the overall acquisition time required to obtain enough contrast images.…”

“…However, multiple acquisitions of various contrast images require substantial scan time, which may burden both patients and clinics. There are some studies trying to reduce scan time of single contrast MRI 47‐48 . In this study, we show that an image generation method with a different contrast can be obtained from several other contrast images without the need for additional scanning, which could reduce costs.…”

Deep learning for brain disorders: from data processing to disease treatment

“…The majority of the DL methods operate at the image-level [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] with the networks learning a mapping from aliased images, reconstructed from under-sampled k-spaces using the inverse Fourier transform, to de-aliased images. The de-aliased image can be the final product [23,25,27,24,31,32,35], be used as input of classic approaches [21] or be combined with the initial under-sampled k-space before a refinement step [26,28,34]. The network architecture can also be designed to mimic a classic iterative reconstruction where steps of reconstruction and DL-based regularization alternate [22,29,30,33].…”

Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI

“…[3][4][5][6][7][8][9][10][11][12][13] However, most have been trained using brain images acquired on a 3T machine and applied to brain images also on a 3T machine. [3][4][5][6][7][8][9][10][11][12][13] However, clinically, the contrast of MR images can vary because of differences in the scan protocol, field strength, and anatomical location, and it is unclear whether this variability affects the performance of these denoising methods. 14 To ensure robustness against these variations, deep learning-based reconstruction (DLR) methods have been proposed to perform denoising only for high-frequency components that contain detailed information about structures and most of the noise, while leaving low-frequency components containing the image contrast information.…”