27th ISMRM Annual Meeting • 11-16 May 2019 • Montréal, QC, Canada

Digital Poster Session
Acquisition, Reconstruction & Analysis

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Thursday, 16 May 2019
Digital Poster

Segmentation 1
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45

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Image Reconstruction I
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45

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Machine Learning for Image Reconstruction: Optimised
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45

 Computer # 4769. 51 SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for efficient and robust MR image reconstruction Fang Liu, Lihua Chen, Richard Kijowski, Li Feng The purpose of this work was to develop and evaluate a new deep-learning based image reconstruction framework, termed as Sampling-Augmented Neural neTwork with Incoherent Structure (SANTIS) for MR image reconstruction. Our approach combines efficient end-to-end CNN mapping with k-space consistency using the concept of cyclic loss to enforce data fidelity. Adversarial training is implemented for maintaining high quality perceptional image structure and incoherent k-space sampling is used to improve reconstruction accuracy and robustness. The performance of SANTIS was demonstrated for reconstructing vast undersampled Cartesian knee images and golden-angle radial liver images. Our study demonstrated that the proposed SANTIS framework represents a promising approach for efficient and robust MR image reconstruction at vast acceleration rate. 4770. 52 Crowdsourced Quality Metrics for Image Reconstruction using Machine Learned Ranking Kevin Johnson, Laura Eisenmenger, Patrick Turski, Leonardo Rivera-Rivera In this work, we investigate a scheme for crowd sourcing image quality using machine learned metrics from user rankings of corrupted images. Using an HTML application, experienced observers ranked pairs of corrupted images with respect to image quality. A convolution neural network (CNN) was then trained to produce a quality score that was higher in the preferred images. The trained CNN was found to be more sensitive to artifacts from image blurring and wavelet compression than mean square error. Finally, preliminary use in training a machine learned image reconstruction is demonstrated. 4771. 53 Virtual Imaging Using Generative Adversarial Networks for Image Translation (VIGANIT): Deep Learning based Prediction of Diffusion-Weighted Images from T2-Weighted Brain MR ImagesPresentation Not Submitted Vidur Mahajan, Aravind Upadhyaya, Vasantha Kumar Venugopal, Abhishek Venkataram, Mukundhan Srinivasan, Murali Murugavel, Harsh Mahajan 100 whole brain MRI scans of patients with no abnormality and 30 with acute infarcts, comprising of 25 T2-weighted and Diffusion-Weighted (b=1000) images each, were fed into a Deep Learning model with a 75-25 training-validation split. The T2W image was assigned as the input to predict DW images. Binary Cross entropy of 0.15 for normal and 0.11 for infarct cases was obtained and the predicted images were able to successfully delineate acute and chronic infarcts in all test cases. 4772 54 A Deep Learning Accelerated MRI Reconstruction Model's Dependence on Training Data DistributionVideo Permission Withheld Dimitrios Karkalousos, Kai Lønning, Serge Dumoulin, Jan-Jakob Sonke, Matthan Caan Recurrent Inference Machines (RIM) are deep learning inverse problem solvers that have been shown to generalize well to anatomical structures and contrast settings it was not exposed to during training. This makes RIMs ideal for accelerated MRI reconstruction, where the variation in acquisition settings is high. Using T1- and T2*-weighted brain scans and T2-weighted knee scans, we compare the RIM's performance when trained on only a single type of data against the case where all three data types are present in the training set. We present results that show an overall model robustness, but also indicate a slight preference for training on all three types of data. 4773. 55 Exploring the Hallucination Risk of Deep Generative Models in MR Image Recovery Vineet Edupuganti, Morteza Mardani, Joseph Cheng, Shreyas Vasanawala, John Pauly The hallucination of realistic-looking artifacts is a serious concern when reconstructing highly undersampled MR images. In this study, we train a variational autoencoder-based generative adversarial network (VAE-GAN) on a dataset of knee images and conduct a detailed exploration of the model latent space by generating extensive admissible reconstructions. Our preliminary results indicate that factors such as sampling rate and trajectory as well as loss function affect the risk of hallucinations, but with a reasonable choice of parameters deep learning schemes appear robust in recovering medical images. 4774. 56 DCTV-Net: Model based Convolutional Neural Network for dynamic MRI Shanshan Wang, Yanxia Chen, Leslie Ying, Cheng Li, Ziwen Ke, Taohui Xiao, Xin Liu, Dong Liang, Hairong Zheng Compressive sensing MRI (CS-MRI) is a popular technique to accelerate MR dynamic imaging. Nevertheless, the reconstruction is normally time-consuming and its parameters have to be hand-tuned To address this challenge, we solve a CS-based dynamic MR imaging problem by adopting the Alternating Direction Method of Multipliers (ADMM) iteration method with the most popular deep learning technique. Specifically, we introduce a deep network structure, dubbed as DCTV-NET, for dynamic magnetic resonance image reconstruction from highly under-sampled k-t space data. Experimental results demonstrate that our method is superior to the state-of-the-art dynamic MRI methods. 4775. 57 Learning Primal Dual Network for Fast MR Imaging Jing Cheng, Haifeng Wang, Leslie Ying, Dong Liang We introduce a novel deep learning network which combines elements of model and data driven approaches for fast MR imaging, termed modified Learned PD. The network is inspired by the first-order primal dual algorithm, where the convolutional neural network blocks are used to learn the proximal operators. Learned PD network works directly from undersampled k-space data and reconstructs MR images by updating in k-space and image domain alternatively. This approach has been evaluated by in vivo MR datasets and achieves accurate MR reconstructions, outperforming other comparing methods across various quantitative metrics. 4776. 58 Fidelity Imposing Network Edit (FINE) for Solving Ill-Posed Image Reconstruction Jinwei Zhang, Zhe Liu, Shun Zhang, Pascal Spincemaille, Thanh Nguyen, Mert Sabuncu, Yi Wang A Fidelity Imposing Network Edit (FINE) method is proposed for solving inverse problem that edits a pre-trained network's weights with the physical forward model for the test data to overcome the breakdown of deep learning (DL) based image reconstructions when the test data significantly deviates from the training data. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and undersampled multi-contrast reconstruction in MRI. 4777. 59 Probabilistic Optimization of Cartesian k-Space Undersampling Patterns for Learning-Based  Reconstruction Valery Vishnevskiy, Jonas Walheim, Sebastian Kozerke Learning-based methods offer improved reconstruction accuracy for compressed Sensing MRI. However, most modern methods assume the sampling trajectory to be predefined. In order to further increase reconstruction quality, we present a method for adaptive design of Cartesian undersampling masks. The proposed method delivers sampling trajectories that allow to improve reconstruction accuracy by 26% and 6% compared to the random and state-of-the-art interleaved variable density patterns, respectively. 4778. 60 Deep transform networks for scalable learning of MR reconstruction Anatole Moreau, Florent Gbelidji, Boris Mailhe, Simon Arberet, Xiao Chen, Marcel Dominik Nickel, Berthold Kiefer, Mariappan Nadar In this work we introduce RadixNet, a fast, scalable, transform network architecture based on the Cooley-Tukey FFT, and use it in a fully-learnt iterative reconstruction with a residual dense U-Net image regularization. Results show that fast transform networks can be trained at 256x256 dimensions and outperform the FFT. 4779. 61 Automating fetal brain reconstruction using distance regression learning Lucilio Cordero-Grande, Anthony Price, Emer Hughes, Robert Wright, Mary Rutherford, Joseph Hajnal We describe a method for automated fetal brain reconstruction from stacks of 2D single-shot slices. Brain localization is performed by a deep distance regression network. Slice alignment is accomplished by a global search in the rigid transform space followed by registration using a fractional derivative metric. An outlier robust hybrid 1,2$1,2$-norm and linear high order regularization are used for reconstruction. Brain localization has achieved competitive results without requiring annotated segmentations. The method has produced acceptable reconstructions in 129 out of 133 3T fetal examinations tested so far. 4780. 62 AUTOMAP Image Reconstruction of Ultra-Low Field Human Brain MR Data Koonjoo Neha, Bo Zhu, Matthew Christensen, John Kirsch, Matthew Rosen Due to very low Boltzmann polarization, MR images acquired at ultra-low field (ULF), MR images require significant signal averaging to overcome low signal-to-noise, which results in longer scan times. Here, we apply the deep neural network image reconstruction technique, AUTOMAP (Automated Transform by Manifold Approximation), to 50% under-sampled low SNR in vivo datasets acquired at 6.5 mT. The performance of AUTOMAP on this data was compared to the conventional 3D Inverse Fast Fourier Transform (IFFT). The results for AUTOMAP reconstruction show a significant improvement in image quality and SNR. 4781. 63 Synthetic Banding for bSSFP Data Augmentation Michael Mendoza, Nicholas McKibben, Grayson Tarbox, Neal Bangerter Balanced Steady State Free Precession (bSSFP) MRI is a highly-efficient MRI pulse sequence but suffers from banding artifacts caused by its high sensitivity to magnetic field inhomogeneity. Many algorithms exist that can effectively remove these banding artifacts, typically by requiring multiple phase-cycled acquisitions, which increase scan time. While some of the algorithms can suppress banding to some degree with two sets of phase-cycled acquisitions, much more accurate band suppression is typically achieved with at least four phase-cycled acquisitions. In this work, we present a deep learning method for synthesizing additional phase-cycled images from a set of at least two phase-cycled images that can then be used with existing band reduction techniques in order to reduce scan time. 4782. 64 Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural NetworkPresentation Not Submitted Pingfan Song, Yonina Eldar, Gal Mazor, Migue Rodrigues Dictionary matching based MR Fingerprinting (MRF) reconstruction approaches suffer from inherent quantization errors, as well as time-consuming parameter mapping operations that map temporal MRF signals to quantitative tissue parameters. To alleviate these issues, we design a residual convolutional neural network to capture the mappings from temporal MRF signals to tissue parameters. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. After training, our network is able to take a temporal MRF signal as input and directly output corresponding tissue parameters, playing the role of a dictionary and look-up table used in conventional approaches. However, the designed network outperforms conventional approaches in terms of both inference speed and reconstruction accuracy, which has been validated on both synthetic data and phantom data generated from healthy subjects. 4783. 65 A Deep Learning Algorithm for Non-Cartesian Coil Sensitivity Map Estimation Zihao Chen, Yuhua Chen, Debiao Li, Anthony Christodoulou The use of parallel imaging (PI) to exploit the encoding power of multiple coil sensitivity patterns is essential for any modern method for accelerating MRI. In practice, the need to estimate sensitivity maps when using an image-space PI formulation delays the image reconstruction process, particularly for non-Cartesian acquisitions. This paper presents a deep learning method to estimate sensitivity maps from non-Cartesian dynamic imaging data. Results show that this algorithm provide a significant reduction in the time (from 42s to 2.5s for 12 coils) for generating high-quality coil sensitivity maps from non-Cartesian MR data compared to the conventional algorithms. 4784. 66 Real-time MR image reconstruction using Convolutional Neural Networks Bryson Dietz, Gino Fallone, Keith Wachowicz There has been an increasing interest for systems that combine a linear accelerator with a MRI. The goal of such systems is to allow for real-time adaptive radiotherapy; to have the ability to track a region of interest for the purpose of accurate radiation delivery. This requires the ability to image in real-time. We investigated the use of convolution neural networks (CNNs) for the purpose of real-time imaging. The reconstruction time of our preliminary data was 150 ms using a NVIDIA 1080Ti GTX GPU. Further optimization of the CNN parameters may decrease the reconstruction time below 100 ms. 4785. 67 ShiftNets: Deep Convolutional Neural Networks for MR Image Reconstruction & the Importance of Receptive Field of View Philip Lee, Makai Mann, Brian Hargreaves Deep learning has been applied to the Parallel Imaging problem of resolving coherent aliasing in image domain. Convolutional neural networks have finite receptive FOV, where each output pixel is a function of a limited number of input pixels. For uniformly undersampled data, a simple hypothesis is that including the aliased peak in the receptive FOV would improve suppression of aliasing. We show that a simple channel augmentation scheme allows us to resolve aliasing using 50x fewer parameters than a large U-Net with millions of parameters and a global receptive FOV. This method was tested on retrospectively undersampled knee volumes. 4786. 68 POCS Augmented CycleGAN for MR Image Reconstruction Hanlu Yang, Yiran Li, Danfeng Xie, Wang Ze Traditional MRI reconstruction depends heavily on solving nonlinear optimization problems, which could be highly time-consuming and sensitive to noise. We proposed a hybrid DL-based MR image reconstruction method by combining two state-of-art deep learning networks, U-Net and CycleGAN (Generative adversarial network with cycle loss) and a traditional method: projectiononto convex set (POCS). Our result shows a high reconstruction accuracy and this method can be further used to increase the sample size, which may find many applications in situations where the training samples are limited such as medical images. 4787. 69 Accelerated Targeted Coronary MRI Using Sparsity-Regularized SPIRiT-RAKI Seyed Amir Hossein Hosseini, Steen Moeller, Sebastian Weingärtner, Kâmil Ugurbil, Mehmet Akçakaya Long scan duration remains a challenge in coronary MRI. A scan-specific machine learning technique, called Robust Artificial-neural-network for k-space Interpolation (RAKI) has recently shown promising results in accelerating MRI. However, RAKI was originally designed for uniform undersampling patterns. In this study, we propose a technique, called SPIRiT-RAKI that enables RAKI with arbitrary undersampling using scan-specific convolutional neural networks to enforce self-consistency among coils. Regularization terms are also incorporated in the new formulation. Our results indicate that SPIRiT-RAKI can successfully accelerate 3D targeted coronary MRI. 4788. 70 A divide-and-conquer strategy to overcome memory limitations of current GPUs for high resolution MRI reconstruction via a domain transform deep learning method Chengzhu Zhang, Dalton Griner, Yinsheng Li, Yijing Wu, Guang-hong Chen Direct learning of a domain transform to reconstruct images with flexible data acquisition schemes represents a step to achieve intelligence in image reconstruction. However, a technical challenge that is encountered with the domain transform type of learning strategy is that current network architectures and training strategies are GPU memory hungry. As a result, given the currently available GPUs with memory on the order of 24 GB, it is very difficult to achieve high resolution (beyond 128x128) MRI reconstruction. The main purpose of this paper is to present a divide-and-conquer strategy to reconstruct high resolution (better than 256x256) MRI images via domain transform learning while staying within the current GPU memory restrictions. 4789 71 A New Deep Learning Structure for Improving Image Quality of a Low-field Portable MRI SystemVideo Permission Withheld WENCHUAN MU, Liang Zheng, Danial C. Alexander, Jia Gong, Wenwei Yu, Shao Ying Huang A permanent magnet based low-field MRI system provides portability and affordability. However, the quality of the image is low due to a low signal-to-noise ratio (SNR). We propose a new deep learning structure which effectively integrates denoising-networks end-to-end to super-resolution-networks, to transfer the rich information available from one-o? experimental imaging from a mid-field MRI scanner (1.5T) to the lower-quality data from a portable system. The procedure uses matched pairs to learn mappings from low-quality to the corresponding high-quality images. Using the proposed method, the quality and resolution of an image from a low-field MRI system is significantly improved. 4790. 72 Simultaneous Multi-Slice Deep RecOnstruction NEtwork (SMS-DRONE) Ouri Cohen Recently, MR fingerprinting (MRF) has been proposed as a means of disentangling simultaneously excited slices by exciting each slice with a distinct acquisition schedule. A notable drawback of this approach, which is particularly acute for multi-parametric dictionaries, is the linear increase in reconstruction time with the number of slices and the potential reduction in accuracy. Here we describe an extension to our previously described MRF-DRONE method that can overcome these issues. Our method can enable larger acceleration factors and faster reconstruction of multi-parametric data. 4791. 73 Deep Learning Super-FOV for Accelerated bSSFP Banding Reduction Nicholas McKibben, Michael Mendoza, Edward DiBella, Neal Bangerter We present a technique for bSSFP band removal using two undersampled phase-cycled bSSFP image acquisitions. 4792. 74 Convolutional Neural Network for Real-Time High Spatial Resolution Functional Magnetic Resonance Imaging Cagan Alkan, Zhongnan Fang, Jin Hyung Lee We propose a convolutional neural network (CNN) based real-time high spatial resolution fMRI method that can reconstruct a 3D volumetric image (140x140x28 matrix size) in 150 ms. We achieved 4x spatial resolution improvement using variable density spiral (VDS) trajectory design. The proposed method achieves similar reconstruction performance as our earlier compressed sensing reconstructions while achieving 17x faster reconstruction time. We demonstrate that this method accurately detects cortical layer specific activity. 4793. 75 Spatio-Temporal Undersampling Artefact Reduction with Neural Networks for Fast 2D Cine MRI with Limited Data Andreas Kofler, Marc Dewey, Tobias Schaeffter, Christian Wald, Christoph Kolbitsch A well-known bottleneck of neural networks is the requirement of large datasets for successful training. We present a method for reduction of 2D radial cine MRI images which allows to properly train a neural network on limited datasets. The network is trained on spatio-temporal slices of healthy volunteers which are previously extracted from the image sequences and is tested on patients data with known heart dysfunction. The image sequences are reassembled from the processed spatio-temporal slices. Our method is shown to have several advantages compared to other Deep Learning-based methods and achieves comparable results to a state-of-the-art Compressed Sensing-based method.
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Segmentation 2
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45

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Software & Tools
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45

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Machine Learning for Image Enhancement, Quality Assessment & Synthetic Image Generation
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45

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Machine Learning for Prediction & Image Analysis
Digital Poster
Acquisition, Reconstruction & Analysis

Thursday, 16 May 2019
 Exhibition Hall 13:45 - 14:45