Joint Annual Meeting ISMRM-ESMRMB • 16-21 June 2018 • Paris, France

Electronic Poster Session
Acquisition, Reconstruction & Analysis
Monday, 18 June 2018
Electronic Poster

Machine Learning for Image Reconstruction
Electronic Poster
Acquisition, Reconstruction & Analysis

Monday, 18 June 2018
 Exhibition Hall 13:45 - 14:45

RF Pulses & Sequences
Electronic Poster
Acquisition, Reconstruction & Analysis

Monday, 18 June 2018
 Exhibition Hall 13:45 - 14:45

Image Analysis
Electronic Poster
Acquisition, Reconstruction & Analysis

Monday, 18 June 2018
 Exhibition Hall 13:45 - 14:45

Machine Learning for Image Analysis
Electronic Poster
Acquisition, Reconstruction & Analysis

Monday, 18 June 2018
 Exhibition Hall 14:45 - 15:45

Image Reconstruction Potpourri
Electronic Poster
Acquisition, Reconstruction & Analysis

Monday, 18 June 2018
 Exhibition Hall 14:45 - 15:45

 Computer # 3505. 73 Improving Parallel Imaging by Jointly Reconstructing Multi-Contrast Data Berkin Bilgic, Tae Kim, Congyu Liao, Mary Manhard, Lawrence Wald, Justin Haldar, Kawin Setsompop We propose a general joint reconstruction framework to accelerate multi-contrast acquisitions further than currently possible with conventional parallel imaging. Our joint parallel imaging techniques simultaneously exploit similarities between echoes/phase-cycles/contrasts, virtual coil concept, partial Fourier acquisition, complementary sampling across images along with limited support and smooth phase constraints. These permit highly accelerated 2D, Simultaneous MultiSlice and 3D acquisitions as well as improved calibrationless parallel imaging from multiple contrasts. Our algorithms, JVC-GRAPPA and J-LORAKS, provide over 2-fold improvement in reconstruction error compared to conventional GRAPPA, with improved mitigation of artifacts and noise amplification. 3506. 74 Parameter Optimization of Wave-CAIPI Based on Theoretical Analysis Zhilang Qiu, Haifeng Wang, Leslie Ying, Xin Liu, Dong Liang Wave-CAIPI is an novel 3D imaging technique with corkscrew trajectory in k-space to reduce g-factor penalty and speed up MRI acquisitions. The sinusoidal gradient parameters of Wave-CAIPI, amplitude and cycles, play an important role since they determine the point spread function of the trajectory and thus the final reconstruction. However, how to choose the optimal sinusoidal gradient parameters which leads to the minimal g-factor has not been exploited. In this work, we theoretically analyzed the influence of the sinusoidal gradient parameters on g-factor. An optimization algorithm which can be automatically conducted is then proposed to optimize these parameters for achieving minimal g-factor penalty. The simulations show that using the optimized sinusoidal gradient parameters can achieve lower g-factor penalty in Wave-CAIPI reconstructions. 3507. 75 Nonlinear GRAPPA Reconstruction with Virtual Coil Conception Haifeng Wang, Yuchou Chang, Leslie Ying, Xin Liu, Dong Liang Nonlinear GRAPPA is a kernel-based approach for improving parallel imaging reconstruction, by reducing noise-induced error. Virtual coil conception has been applied into the reconstruction process for parallel acquisitions, by generating virtual coils containing conjugate symmetric k-space signals from actual multiple-channel coils. In this work, we proposed a hybrid method to combine nonlinear GRAPPA and virtual coil conception for incorporating additional image- and coil-phase information into the reconstruction process. The experiments of in vivo human brain data show that the proposed method can reduce more noise and artifacts than the traditional GRAPPA and original Nonlinear GRAPPA methods. 3508. 76 A Method for Automatically Determining an Optimal Kernel Size in ESPIRiT Reconstruction Jong Bum Son, Colleen Costelloe, Tao Zhang, Jingfei Ma ESPIRiT is a hybrid-domain parallel imaging method which can estimate the coil-sensitivity information from the k-space calibration matrix. In ESPIRiT, the calibration matrix is constructed by sliding a window through the fully sampled data region of auto-calibrating signals. Presently, the kernel size of the sliding window determining the performance of ESPIRiT reconstruction is empirically chosen, even though an optimal value may vary depending on a combination of scan parameters and scan configurations. In this work, we developed an automatic data-driven method for determining an optimal kernel size in ESPIRiT to reduce the performance variation of ESPIRiT reconstructions. 3509. 77 Improved Parallel Imaging Reconstruction of EPI using Inversely Distortion Corrected FLASH as Calibration Data Mengye Lyu, Yilong Liu, Ed Wu For parallel imaging reconstruction of EPI, EPI based calibration scan may suffer from ghost artifacts, whereas non-EPI based calibration scan such as FLASH cannot provide consistent geometric distortion. In this study, we propose to employ dual-echo FLASH as the calibration scan, such that B0 field maps can be derived to match FLASH images to EPI images and the reconstruction artifact related to inconsistent distortion can be minimized. 3510. 78 Accelerated reconstruction for calibrationless parallel imaging using grouped joint nonlinear inversion and its application in myelin water imaging Zhe Wu, Hongjian He, Yi Sun, Jianhui Zhong The simultaneous estimation of images and coil sensitivities using joint nonlinear inversion (JNLINV) has been shown to be effective for calibrationless parallel imaging for multi-echo data. However, the number of unknowns grows with increasing number of echoes, so the reconstruction procedure could be lengthy. This study proposes an improved method called grouped JNLINV (gJNLINV) to enhance the reconstruction efficiency. Its reconstruction time is ~1/3 of that with JNLINV while preserving a similar root-mean-square error (RMSE) and increasing the fidelity of the coil sensitivities. We further demonstrate the application of gJNLINV on a 32-echo GRE data set for myelin water imaging. 3511. 79 In-Plane Signal Leakage (L-factor) Maps from TGRAPPA R. Allen Waggoner, Kenichi Ueno, Hideto Kuribayashi, Keiji Tanaka Residual aliasing is a well-documented problem for multiband reconstructions, but it can be an important issue with in-plane acceleration methods as well.  With GRAPPA in particular, the residually aliased signal can be distributed fairy randomly, making it appear as g-factor noise.  We demonstrate that the use of TGAPPA permits not only the elimination of the residually aliased signal but also the determination of L-factor maps, which can be a potentially useful tool in understanding how to minimize residual aliasing. 3512. 80 Parallel Imaging Reconstruction Algorithm Mitigating SNR Loss Using Phase Distribution for Fast Spin Echo Sequence Kosuke Ito, Masahiro Takizawa Parallel imaging is widely used in clinical routine practice. However, SNR degradation occurs due to undersampling and higher g-factor in higher acceleration factor. In this study, a new algorithm of parallel imaging reconstruction mitigating noise enhancement for fast spin echo sequence was proposed. The algorithm uses information of phase distribution of unaliased image, aliasing image, and folded image. SNR was compared in vivo T2 weighted image between full sampling, conventional parallel imaging, and proposed method. And higher SNR was demonstrated. 3513. 81 Whole-Volume, High-Resolution, In-Vivo Signal-to-Noise Ratio and G-factor Superiority, and Structural Similarity Index Differences, of Compressed Sensing SPACE and CAIPIRINHA SPACE over GRAPPA SPACE Neil Kumar, Sheil Kumar, Jan Fritz Compressed Sensing, CAIPIRINHA, and GRAPPA techniques reduce MRI acquisition times. We used a 3-dimensional sliding region-of-interest analysis tool to perform parameter-controlled, whole-volume average signal-to-noise ratio and g-factor comparison, and g-factor structural similarity index measurements (SSIM) of the above techniques in the setting of 3 Tesla knee MRI. We demonstrate g-factor superiority of CS SPACE over CAIPIRINHA SPACE and g-factor superiority of CAIPIRINHA SPACE over GRAPPA SPACE in living subjects. Post-processing, including pre-scan normalize and distortion correction, improves g-factors and causes variation in the g-factor SSIM results between the techniques. 3514. 82 Reduced-FOV k-space Variant Radial Parallel Imaging Reconstruction for Real-time Cardiac MR Yu Li, Shams Rashid, Yang Cheng, William Schapiro, Kathleen Gliganic, Ann-Marie Yamashita, Marie Grgas, Michelle Maragh, Jie Cao Radial imaging is k-space variant, but mostly uses k-space invariant methods in image reconstruction. This permits reconstructing images with lower computation complexity at a cost of performance. Here a k-space variant parallel imaging reconstruction technique is developed to reconstruct Cartesian data directly from multi-channel radial samples with affordable computation. It is demonstrated that this technique offers the ability to collect real-time images with a temporal resolution of 40ms and a spatial resolution of 1.7mm. The new technique outperforms those gridding-based methods with k-space invariant algorithms in a stress cardiac test. 3515. 83 Radial acquisition and PFT reconstruction allow for retrospective selection of spatial resolution in fMRI studies Banfshe Shafiei Zargar, Abbas Moghaddam Aiming for fine resolution is always a challenging compromise between various parameters. We have investigated a method for retrospective adjustment of resolution in reconstruction step. Our study of fMRI data indicates that an adjustable pixel size is obtainable in a selected central region during the PFT (Polar Fourier Transform) reconstruction of a radially acquired K-space. Preserving the functional sensitivity, this improvement of resolution results in finer activation detection and higher functional CNR. 3516. 84 Comparison of leading reconstruction techniques for real-time speech MRI Weiyi Chen, Yongwan Lim, Yannick Bliesener, Shrikanth Narayanan, Krishna Nayak Real-time MRI (RT-MRI) has revolutionized the study of human speech production. Two state-of-the-art reconstruction techniques have been adopted by different groups to accelerate real time imaging, constrained SENSE, and regularized nonlinear inversion. In this study, we describe our best performing implementations of both classes of reconstructions, and compare performance on common data from spiral RT-MRI of human speech at 1.5T. 3517. 85 Partial Fourier Acquisitions in Myocardial First Pass Perfusion Revisited Tobias Hoh, Jonas Walheim, Mareike Gastl, Alexander Gotschy, Sebastian Kozerke The inflow of a paramagnetic contrast agent (CA) in cardiac dynamic contrast-enhanced (DCE) MRI effects the local phase of magnetization. In this work the impact of phase variations on Partial Fourier (PF) reconstruction is simulated for k-space zero filling, homodyne (HR) and projections onto convex sets (POCS) reconstruction and consequently assessed in in-vivo first-pass perfusion. CA induced phase variations in DCE MRI are seen to compromise HR and POCS reconstruction of PF data to an extent where they do not convey any benefit over simple zero-filling reconstruction. 3518. 86 Optimization-Based Simultaneous Combination and Unwrapping for MR Phase Imaging John Baxter, Zahra Hosseini, Olivia Stanley, Ravi Menon, Maria Drangova, Terry Peters MRI phase allows for the extraction of inherent tissue contrasts arising from differences in magnetic susceptibility. However, in order to enhance signal-to-noise ratio and accelerate acquisition, modern MRI uses multiple receiver coils. Extracting susceptibility information relies on combining phase information from these multiple channels. Once combined, phase unwrapping beyond the [$$-\pi$$$, $$\pi$$$] range allows for further processing and visualization. These processes can be sensitive to noise and errors which are compounded during serial processing, motivating more robust integrated algorithms. This paper introduces simultaneous combination and unwrapping (SCAU) that simultaneously estimates channel phase offset images and a combined unwrapped image. 3519. 87 Reconstruction of Accelerated DCE-MRI Guided by Image Quality Metrics James Rioux, Nathan Murtha, Chris Bowen, Sharon Clarke, Steven Beyea Golden-angle sampling allows arbitrary retrospective selection of temporal resolution in dynamic MRI scans.  To select the fastest temporal resolution that preserves time course fidelity, we propose the use of image quality metrics (IQMs).  We demonstrate multiple IQMs that correlate strongly with the accuracy of fitted pharmacokinetic parameters up to at least an acceleration factor of R=12.  For a fixed undersampling factor, these metrics can also inform the selection of reconstruction parameters such as regularization weights for compressed sensing. This approach may enable rational, individual-level tuning of temporal resolution following a prospectively accelerated DCE-MRI scan. 3520. 88 PEC-GRAPPA Reconstruction of Simultaneous Multislice EPI with Slice-Dependent 2D Nyquist Ghost Correction Zheyuan Yi, Yilong Liu, Mengye Lyu, Ed Wu Nyquist ghost correction is challenging for simultaneous multislice (SMS) EPI due to the slice-dependent 2D phase error between positive and negative echoes. For this problem, phase error correction SENSE (PEC-SENSE) has been proposed recently, which incorporates slice-dependent 2D phase error maps into coil sensitivity maps. In this study, we extend the concept of PEC-SENSE to k-space based implementation termed as PEC-GRAPPA. It outperforms 1D LPC based GRAPPA reconstruction and requires less tuning than PEC-SENSE such as excluding background areas. 3521. 89 Optimal Partial Fourier MRI reconstruction: Homodyne vs POCS Venkata kadimesetty, Harsh Agarwal Partial Fourier MRI (PF-MRI) is a common fast MRI technique to reduce the scan time. While POCS PF-MRI is known to produce MRI images with least amount of RMSE error, homodyne PF-MRI is popularly used in clinical practice. In this abstract we did digital phantom experiments to show that for smoothly varying phase, such as for FSE, POCS localises the error while an over-/under-estimation in image intensity is observed for Homodyne PF-MRI technique. However for fast varying phase such as for GRE, error is localised for Homodyne compared to POCS PF-MRI technique. 3522. 90 Easy-to-Implement and Rapid Image Reconstruction of Accelerated Cine and 4D Flow MRI Using TensorFlow Valery Vishnevskiy, Jonas Walheim, Hannes Dillinger, Sebastian Kozerke Many MR image reconstruction algorithms can be formulated as optimization problems and solved with gradient-based optimization methods of choice. In this work, we present and analyze the performance of the TensorFlow framework for modeling and solving MR image reconstruction problems. We test our approach on undersampled cine cardiac and 4D flow datasets. It is demonstrated that MR image reconstruction is easy to implement in TensorFlow, TensorFlow performs comparably to sophisticated optimization algorithms with theoretical convergence guarantees, and that TensorFlow is as fast as or faster compared to standard MR reconstruction toolboxes. 3523. 91 A Python-based MRI Reconstruction Toolbox, “MRIPY”, for Compressed Sensing, Parallel Imaging and Machine Learning Peng Cao, Xucheng Zhu, Jing Liu, Yan Wang, Peder Larson A python-based open-source package, “MRIPY” combines the existing MRI reconstruction methods, i.e. compressed sensing and parallel imaging, with deep neural networks that are implemented in the Tensorflow software. 3524. 92 Uniform Combined Reconstruction (UNICORN) of Multi-channel Surface-coil Data at 7T without use of a Reference ScanVideo Permission Withheld Venkata Veerendranadh Chebrolu, Peter Kollasch, Vibhas Deshpande, John Grinstead, Thomas Benner, Robin Heidemann, Daniel Spence, Joel Felmlee, Matthew Frick, Kimberly Amrami An algorithm for correcting the intensity non-uniformity in MR images without the use of a calibration/reference scan was proposed and its efficacy was demonstrated at ultra-high-field in musculoskeletal MRI. The algorithm was shown to provide better sensitivity in the inferior/superior regions of the knee compared to state-of-the-art inhomogeneity correction filters. Without the use of a reference scan, the algorithm was also shown to provide image uniformity equivalent to calibration based methods. 3525. 93 MR Fingerprinting using a Gadgetron-based reconstruction Wei-Ching Lo, Yun Jiang, Dominique Franson, Mark Griswold, Vikas Gulani, Nicole Seiberlich Gadgetron-based online MRF reconstruction enables rapid generation of quantitative tissue property maps directly at the scanner before completing acquisition of the following slice. This technique can facilitate multicenter clinical studies and facilitate easier and direct comparisons of quantitative maps from different scanners. 3526. 94 Sparsely Sampled Cardiac Diffusion Tensor Imaging Using Phase-Corrected Joint Low-Rank and Sparsity Constraints Sen Ma, Christopher Nguyen, Anthony Christodoulou, Sang-Eun Lee, Hyuk-Jae Chang, Debiao Li We propose to sparsely sample in vivo cardiac diffusion tensor imaging (CDTI) by combining a phase-corrected low-rank model and sparsity constraint. The proposed method was evaluated on 7 hypertrophic cardiomyopathy patients. Helix angle and mean diffusivity maps were compared against employing single constraint, and changes in helix angle transmurality and mean diffusivity were evaluated using Wilcoxon signed rank test to statistically determine the highest achievable acceleration factors preserving CDTI measurements with no significant difference. Our framework shows promise in accelerating acquisition window while preserving myofiber architecture features, and may allow higher spatial resolution or shorter temporal footprint in the future. 3527. 95 Minimum-variance weighted image reconstruction and the application to MRIVideo Permission Withheld Jyh-Miin Lin, Philippe Ciuciu Non-stationary MRI noise occurs in sparse and non-uniform k-space. Weighted least squares regression has been used to handle data with non-stationary noise. A minimum-variance weighting function may reduce the variance (image noise) of the image, and it may also relax the regularization needed for MRI reconstruction. To obtain the optimal weighting in non-uniform MRI reconstruction, this study uses the Monte Carlo method to determine the minimum-variance weighting function in Shepp-Logan phantom and breast MRI. The parameter $$\alpha=-0.5$$\$ provides a weighting function with the minimum-variance in the reconstructed images. 3528. 96 Deep-SENSE: Learning Coil Sensitivity Functions for SENSE Reconstruction Using Deep Learning Xi Peng, Kevin Perkins, Bryan Clifford, Brad Sutton, Zhi-Pei Liang Parallel imaging is an essential tool for accelerating image acquisition by exploiting the spatial encoding effects of RF receiver coil sensitivity functions. In practice, the coil sensitivity functions are often estimated from low-resolution auto-calibration signals (ACS) which limits estimation accuracy and in turn results in aliasing artifacts in the final reconstructions. This paper presents a novel deep learning based method for coil sensitivity estimation which exploits empirical and physics-based prior information to produce high-accuracy estimates of coil sensitivity functions from low-resolution ACS. Results are given which demonstrate the proposed method provides a significant reduction in aliasing over standard methods.
Compressive MRI
Electronic Poster
Acquisition, Reconstruction & Analysis

Monday, 18 June 2018
 Exhibition Hall 14:45 - 15:45