Chao Li^{1}, Hang Zhang^{1}, Jinwei Zhang^{1}, Pascal Spincemaille^{1}, Thanh Nguyen^{1}, and Yi Wang^{1}

^{1}Cornell university, New York, NY, United States

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson’s disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed.

$$

\begin{split}

S_m = &F^H\{\sum_iM_i \odot F[m(A_i\cdot r)e^{-\frac{TE}{T2*(A_i\cdot r)}}e^{-id\ast[\chi(A_i\cdot r)]\omega_0TE}]\}\\\approx&F^H\{\sum_iM_i \odot F[m(A_i\cdot r) e^{-\frac{TE}{T2*(A_i\cdot r)}}e^{-ib(A_i\cdot r)\omega_0TE}]\}

\end{split}

$$

Here $$$A_i$$$ is the affine matrix acting on the position at motion state $$$i$$$, $$$F$$$ the Fourier transform, $$$\odot$$$ denotes pointwise multiplication, and $$$M_i$$$ is a binary mask indicating the k-space lines acquired during motion state $$$i$$$. The phase factor in the equation is written as an affine transformation acting on the position of the susceptibility source which is then convolved with the dipole kernel. For purely translational motion, the order of the affine transformation and dipole convolution can be exchanged. While this property does not strictly hold for rotation, the intra-scan head rotation angle is relatively small. We simulate motion-corrupted MGRE data from motion-free data using the equation, and use the corresponding QSM image pairs to train a CNN consisting of a 3D U-net and a 2D PreNet denoiser (18) (Figure.1) for motion artifact removal. Rigid body motion was simulated for a Cartesian trajectory in the range of ±5 mm for translation and ±5° for rotation. The trained network was tested on unseen simulated motion-corrupted images and in vivo datasets obtained from healthy volunteers and PD patients with real motion artifacts. This dataset consisted of 3D MGRE scans with negligible motion artifacts obtained from 110 patients using a 3T GE Signa HDxt scanner and the following typical imaging parameters: flip angle = 15-20°, first TE = 3.5-6.3 ms, TR = 48ms-58ms, #TE = 11, ΔTE = 3.8-5.5ms, matrix size = 512×512×48-60, voxel size = 0.5×0.5×3 $$$mm^3$$$. 10 motion trajectories are simulated on each of the motion-free MGRE data, leading to 1100 images, for training, validating, and testing (split ratio 8:1:1) the CNN. A second MGRE dataset with actual motion artifacts was obtained from 5 PD patients using a 3T Siemens Magnetom Skyra scanner and the following typical imaging parameters: flip angle = 15°, first TE = 6.69 ms, TR = 44 ms, #TE = 10, ΔTE = 3.6 ms, matrix size= 260×320×128, voxel size = 0.8×0.8×0.5 mm3. A third MGRE dataset was collected from 4 healthy subjects using a 3T GE Discovery MR750 with the following imaging parameters: FA = 15°, first TE = 6.3 ms, TR = 49 ms, #TE = 11, ΔTE = 4.1 ms, matrix size= 512×512×60, voxel size = 0.5×0.5×3 $$$mm^3$$$. Each subject was scanned twice, one without head motion and one with head movement lasting 10-20 sec twice or three times during the scan. All images were resampled to a voxel size of 0.75×0.75×3 $$$mm^3$$$ for training, validation and testing.

Structural similarity index (SSIM) , peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) of the motion corrupted and motion corrected images are computed for the validation set including 110 simulated examples. ROI-based susceptibility analysis is performed on 10 simulated examples for ROIs including caudate nucleus, red nucleus, or substantia nigra.

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Network Architecture: (a) the flow of motion correction procedure: The motion corrupted image is passed through a 3D U-Net and then a 2D progressive recurrent neural network (PreNet). The U-Net and the PreNet are trained separately; (b) illustration of the denoiser PreNet with multiple-stage recursion; (c) the structure of the PreNet: It includes a convolution layer with ReLU, a convolutional LSTM, conventional ResBlocks and a final convolution layer.

One representative example of a healthy volunteer. a) scan without motion, b) scan with voluntary motion, and c) motion corrected (starting from b). For display purposes only, the motion corrupted (b) and motion corrected (c) images were registered to motion free images (a).

One representative example from Parkinson’s disease patients with real motion artifacts where the ground truth motion-free reference is not available. The left displays the motion-corrupted image and the right displays the motion-reduced image corrected by our neural network.

(a), (b) and (c) are SSIM, PSNR and RMSE for the motion-corrupted versus motion-corrected image compared with the motion-free reference calculated on the simulated dataset: The top and the bottom of box represent 25th percentile and 75th percentile respectively; the red line represents the median. (d) Comparison of average susceptibilities in caudate nucleus, red nucleus, and substantia nigra in motion-free and motion-corrected images in 10 simulated cases. The slope of the linear fit is 1.059, the Y-interception is 1.35 ppb, and .

DOI: https://doi.org/10.58530/2022/3908