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Attention-Gated Convolutional Neural Networks for Off-Resonance Correction of Spiral Real-Time Magnetic Resonance Imaging

Yongwan Lim¹, Shrikanth S Narayanan¹, and Krishna S Nayak¹
¹University of Southern California, Los Angeles, CA, United States

Synopsis

Spiral acquisitions are preferred in real-time MRI because of their efficiency, which has made it possible to capture vocal tract dynamics during natural speech. A fundamental limitation of spirals is blurring and signal loss due to off-resonance, which degrades image quality at air-tissue boundaries. Here, we present a new CNN-based off-resonance correction method that incorporates an attention-gate mechanism. This leverages spatial and channel relationships of filtered outputs and improves the expressiveness of the networks. We demonstrate improved performance with the attention-gate, on 1.5T spiral speech RT-MRI, compared to existing off-resonance correction methods.

Introduction

Blurring and signal loss due to off-resonance are the primary limitations of spiral MRI^1–3. In the context of speech real-time MRI (RT-MRI), off-resonance degrades image quality most significantly at air-tissue boundaries^4–6, which are the exact locations of interest. Blurring and signal loss is the result of a complex-valued spatially varying convolution. In order to resolve the artifact, conventional methods^7–12 reconstruct basis images at demodulation frequencies and apply spatially-varying masks to the basis images to form a desired sharp image.
Recently, convolutional neural network (CNN) approaches have shown promise in solving this spiral deblurring task^13,14. The conventional methods require field maps^7,8 or focus metrics^9,11,12 to estimate the spatially-varying mask. One of the advantages of CNN is that once trained, ReLU nonlinearity provides the mask to convolution filters, enabling spatially-varying convolution¹⁵. Since ReLU masks out the activation in an element-wise manner, it cannot exploit local spatial or channel (filter) dependency, unlike the conventional methods.
In this work, we present a CNN-based deblurring method that adapts the attention-gate (AG) mechanism (AG-CNN) to exploit spatial and channel relationships of filtered outputs to improve the expressiveness of the networks^16–18. We demonstrate improved deblurring performance for 1.5T spiral speech RT-MRI, compared to a recent CNN study¹⁴, and several conventional methods.

Methods

Network Architecture
We use a simple 3-layer residual CNN architecture¹⁴ and incorporate a proposed AG module at each convolutional layer, as illustrated in Figure 1. The AG takes the output feature maps (F) from a convolution unit as an input and performs two cascaded depth-wise separable convolutions to generate attention maps (M) in the range from 0 to 1. Depth-wise separable convolution¹⁹ is used to improve the AG module in both performance and overhead. The attention maps M learn to identify salient image regions and channels adaptively for given feature maps F, and they preserve only the activation relevant to the deblurring task in the following convolution layers. The AG multiplies the attention map by the convolution output (i.e., F’ = M(F)$$$\otimes$$$F) to emphasize important elements in space and across channels.

Training Data
2D RT-MRI data from 33 subjects were acquired at our institution on a 1.5T scanner (Signa Excite, GE Healthcare, Waukesha, WI) using a vocal-tract imaging protocol²⁰. It uses a short readout (2.52ms) spiral spoiled-gradient-echo sequence. Ground truth images were obtained after off-resonance correction⁶. We augmented field maps estimated in the correction step by scaling $$$\bf{f'} = \alpha\bf{f}+\beta$$$ with $$$\alpha$$$ ranging from 0 to 3.15 and $$$\beta$$$ ranging from -200 to 200 Hz. Distorted images were then synthesized by using the discrete object approximation and simulating off-resonance using the field map $$$\bf{f'}$$$ and spiral trajectories with readout lengths of 2.520, 4.016, 5.320, and 7.936ms. We split data into 23, 5, and 5 subjects for training, validation, and testing.

Network Training
Our model was trained in a combination of L1 loss ($$$\mathcal{L}_1$$$) and gradient difference loss ($$$\mathcal{L}_{gdl}$$$)²² between the prediction and ground truth as $$$\mathcal{L} = \mathcal{L}_1+\mathcal{L}_{gdl}$$$. In addition to $$$\mathcal{L}_1$$$, $$$\mathcal{L}_{gdl}$$$ is known to provide a sharp image prediction. We used Adam optimizer²³ with a learning rate of 1e-3, a mini-batch size of 64, and 200 epochs. We implemented our network with Keras using Tensorflow backend.

Experiments
We investigate the effectiveness of the AG module by varying depth-wise separable convolution filter sizes, f₁ and f₂ of the first and second AG modules. Two cascaded convolutions in an AG module uses the same filter size of either f₁ or f₂. For comparison, we also deblur images with various existing methods: the previous CNN architecture¹⁵, multi-frequency interpolation (MFI)⁷, and iterative reconstruction (IR)²⁴. Note that field maps are necessary for deblurring in the latter two methods, so we assume ground truth field maps are known for those two, although those would not be available in practice. For all those methods, dynamic images were deblurred frame-by-frame. We report quantitative quality comparison using peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and high-frequency error norm (HFEN).

Results and Discussion

Figure 2 shows the intermediate feature maps. We observe that the attention map M₁ from the first AG module tends to focus on low-level structures such as tissue, air, or air-tissue boundaries with a different focus across channels, while M₂ from the second AG focuses on a high-level channel dependency.
Table 1 shows that adding an AG module on top of CNN layers improves deblurring performance with a slight overhead and less sensitivity to the kernel size. An extensive comparison with existing attention approaches^17,18,25 applicable to this task remains as future work.
Figure 3 shows that AG-CNN outperforms the previous CNN and MFI using a reference field map in multiple readout duration lengths. Figure 4 contains representative image frames. Blurring of the lips and soft palate are not perfectly resolved with the previous CNN method. AG-CNN provides substantially improved depiction of these and other air-tissue boundaries.

Conclusion

We demonstrate AG-CNN deblurring for 1.5T spiral speech RT-MRI. Adding an AG module on top of CNN layer improves deblurring performance by >1dB PSNR, >0.014 SSIM, and >0.029 HFEN compared to the previous CNN architecture and provides results visually comparable to reference IR method with ~10 times faster computation, and without the need for a field map.

Acknowledgements

This work was supported by NIH Grant R01DC007124 and NSF Grant 1514544.

References

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Figures

Figure 1. Network architecture. The attention gate (AG) module is integrated with a previous CNN architecture¹⁴. The AG consists of two cascaded depth-wise separable convolutions¹⁹ (each consisting of a channel-wise convolution followed by a 1x1 convolution), one with ReLU and one with sigmoid activations to generate attention maps (M) in the range from 0 to 1. The attention map is then multiplied back by the convolution output (F) (i.e., F’ = M(F)$$$\otimes$$$F) element-by-element to emphasize important elements in space and across channels.

Figure 2. (Animated GIF) Intermediate feature maps. We observe that the two AG modules build hierarchical attention. M₁ from the first AG module tends to focus on low-level structures such as air, tissue, or air-tissue boundaries with a different focus across channels while M₂ focuses on a high-level channel dependency. We only visualize 4 and 3 channels out of 64 and 32 channels for the first and second AG modules, respectively, due to space and file size constraints.

Table 1. Deblurring performance is improved by adding the proposed AG module on top of the CNN layer. We obtain performance gains of > 1 dB PSNR, > 0.014 SSIM, and > 0.029 HFEN on the test dataset (5 subjects, > 8K frames) with less sensitivity to the size of depth-wise separable convolution kernel in the AG module. Those evaluation metrics were averaged across all the test image frames. The number of parameters is slightly increased due to depth-wise separable convolutions in the AG module. We chose f₁ = f₂ = 3 for the rest of this study.

Figure 3. Quantitative comparison of deblurring performance on multiple spiral trajectories. Four trajectories are considered with varying readout lengths of 2.52, 4.016, 5.320, and 7.936 ms. Overall, AG-CNN outperforms the previous CNN¹⁴ as well as MFI⁷ for PSNR, SSIM, HFEN. It should be noted that we assume a reference field map is known for both MFI and IR²³ methods although it would not be available in practice. IR should be considered as an upper bound of the maximum deblurring performance achievable for a given ground truth field map.

Figure 4. Qualitative comparison of deblurred images. From top to bottom: images after deblurring with various methods, difference images with respect to ground truth, and an intensity vs time plot. The proposed AG-CNN successfully resolves the blurring artifact especially at the lips and soft palate, which is difficult to resolve with the previous CNN¹⁴. The AG-CNN is also visually comparable to IR method²³, which uses the ground truth field map and is computationally expensive (e.g., comp. time: ~1.6 s/frame) compared to the AG-CNN (~0.15 s/frame on a single CPU in inference time).

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)

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