Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction

Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which no other imaging modality allows. This innovative technology could revolutionise the ability to perform cardiac clinical diagnosis, risk stratification, prognosis and therapy follow-up. However, DT-CMR is currently inefficient with over six minutes needed to acquire a single 2D static image. Therefore, DT-CMR is currently confined to research but not used clinically. We propose to reduce the number of repetitions needed to produce DT-CMR datasets and subsequently de-noise them, decreasing the acquisition time by a linear factor while maintaining acceptable image quality. Our proposed approach, based on Generative Adversarial Networks, Vision Transformers, and Ensemble Learning, performs significantly and considerably better than previous proposed approaches, bringing single breath-hold DT-CMR closer to reality.

[1]  Syed Waqas Zamir,et al.  Restormer: Efficient Transformer for High-Resolution Image Restoration , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Luc Van Gool,et al.  SwinIR: Image Restoration Using Swin Transformer , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

[3]  Jianmin Bao,et al.  Uformer: A General U-Shaped Transformer for Image Restoration , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  T. Reese,et al.  Accelerated in Vivo Cardiac Diffusion-Tensor MRI Using Residual Deep Learning-based Denoising in Participants with Obesity. , 2021, Radiology. Cardiothoracic imaging.

[5]  Wen Gao,et al.  Pre-Trained Image Processing Transformer , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Timo Ropinski,et al.  Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[7]  Jin Keun Seo,et al.  Unpaired Image Denoising Using a Generative Adversarial Network in X-Ray CT , 2019, IEEE Access.

[8]  Loïc Royer,et al.  Noise2Self: Blind Denoising by Self-Supervision , 2019, ICML.

[9]  Pierrick Coupé,et al.  MRI Denoising Using Deep Learning , 2018, Patch-MI@MICCAI.

[10]  Gözde B. Ünal,et al.  Patch-Based Image Inpainting with Generative Adversarial Networks , 2018, ArXiv.

[11]  Tao Tan,et al.  Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network , 2017, Japanese Journal of Radiology.

[12]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

[13]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[14]  Ke Li,et al.  Low-dose CT denoising with convolutional neural network , 2016, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[15]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[16]  Jan Sijbers,et al.  Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters , 2015, Magnetic resonance in medicine.

[17]  Enhong Chen,et al.  Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.

[18]  Stefan Skare,et al.  The presence of two local myocardial sheet populations confirmed by diffusion tensor MRI and histological validation , 2011, Journal of magnetic resonance imaging : JMRI.

[19]  Manuel Guizar-Sicairos,et al.  Efficient subpixel image registration algorithms. , 2008, Optics letters.

[20]  S. Mori,et al.  Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research , 2006, Neuron.

[21]  P. Kingsley,et al.  Introduction to diffusion tensor imaging mathematics: Part III. Tensor calculation, noise, simulations, and optimization , 2006 .

[22]  P. Basser.  Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images , 1995, NMR in biomedicine.

[23]  D. West,et al.  Gadolinium-associated nephrogenic systemic fibrosis. , 2008, Community oncology.