SCMR/ISMRM Co-Provided Workshop
SCMR 22nd Annual Scientific Sessions
Niccolo Fuin, PhD
RESEARCH ASSOCIATE
King's College London
Aurelien Bustin, PhD
Research Associate
King's College London
René Botnar, PhD, FSCMR
Chair of Cardiovascular Imaging
King's College London
Claudia Prieto, PhD
Reader
School of Biomedical Engineering and Imaging Sciences, King's College London
Background:
3D whole-heart coronary MR angiography (CMRA) has shown significant potential to noninvasively visualize coronary arteries. However, acquisition remains lengthy as a large amount of data need to be acquired to obtain high-resolution images. Undersampled compressed sensing (CS) reconstruction approaches have been applied to accelerate CMRA1-2. For high acceleration factors, however, CS-based techniques suffer from residual aliasing artifacts and long reconstruction times.
We propose to use a variational neural network (VNN)3 as a probabilistic prior embedded in an iterative direction method of multipliers (ADMM) optimization framework. We utilize the ADMM+VNN approach for the reconstruction of 3D high-resolution images from prospectively undersampled translational motion-corrected CMRA data; and we compare its results with CS for an undersampling factor of 9x.
Methods:
Acquisition: Four healthy subjects underwent free-breathing 3D bSSFP acquisition with spiral-like Cartesian sampling (VD-CASPR4) in a 1.5T scanner (Siemens Magnetom Aera) with 1.2mm3 isotropic resolution, TR/TE/FA= 3.35ms/1.47ms/90 °, 9-fold undersampling and acquisition time of ~3 mins. A 2D image-navigator enabled beat-to-beat 2D translational respiratory motion correction and thus 100% respiratory scan efficiency5.
VNN Training: The proposed VNN (Fig.1) consists of 3 network layers. Each layer presents 24 real-valued 11x11 filters and their corresponding activation functions. The network was trained on 2D magnitude images obtained from retrospectively undersampled (9-fold) 3D CMRA data of 20 healthy subjects. During the training procedure, the output of the VNN is compared to the corresponding fully-sampled reference images. A combination of the normalized root mean-squared error (RMSE) and of the structural similarity index (SSIM) was used as loss function.
Image Reconstruction: ADMM iterates between a data-consistency step, which performs MR-reconstruction using a conjugate gradient descent method with the output of the second step as prior knowledge; and a “denoising/de-aliasing” step, where the VNN provides a high-resolution 3D volume. The two sub-problems are solved iteratively in an Augmented Lagrangian scheme (Fig.1).
Experiments: Image quality of the proposed approach was compared to zero-filled and Wavelet-based CS6 reconstructions in terms of coronary artery length and sharpness.
Results: The proposed reconstruction presents higher image quality than zero-filled and CS (Fig. 2) with coronary artery length and sharpness comparable to the fully-sampled reference (Fig.3). The average total reconstruction time was ~1.5 min.
Conclusion:
We demonstrated that the VNN prior can be successfully trained on retrospectively undersampled data. The whole-heart 3D coronary images obtained with ADMM+VNN have shown improved image quality with respect to zero-filled and CS reconstructions. The proposed method proved to be fast and efficient offering easy integration into clinical workflow.