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1. End-to-End Sequential Sampling and Reconstruction for MR Imaging

   Yin, Tianwei; Wu, Zihui; Sun, He; Dalca, Adrian V; Yue, Yisong; Bouman, Katherine L (December 2021, Proceedings of the Machine Learning for Health Conference)

   Accelerated MRI shortens acquisition time by subsampling in the measurement k-space. Recovering a high-fidelity anatomical image from subsampled measurements requires close cooperation between two components: (1) a sampler that chooses the subsampling pattern and (2) a reconstructor that recovers images from incomplete measurements. In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy. This co-designed framework is able to adapt during acquisition in order to capture the most informative measurements for a particular target (see the figure above). Experimental results on the fastMRI knee dataset demonstrate that the proposed approach successfully utilizes intermediate information during the sampling process to boost reconstruction performance. In particular, our proposed method outperforms the current state-of-the-art learned k-space sampling baseline on over 96% of test samples. We also investigate the individual and collective benefits of the sequential sampling and co-design strategies. 

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2. Self‐calibrated interpolation of non‐Cartesian data with GRAPPA in parallel imaging

   https://doi.org/10.1002/mrm.28033  

   Chieh, Seng‐Wei; Kaveh, Mostafa; Akçakaya, Mehmet; Moeller, Steen (November 2019, Magnetic Resonance in Medicine)

   PurposeTo develop a non‐Cartesian k‐space reconstruction method using self‐calibrated region‐specific interpolation kernels for highly accelerated acquisitions. MethodsIn conventional non‐Cartesian GRAPPA with through‐time GRAPPA (TT‐GRAPPA), the use of region‐specific interpolation kernels has demonstrated improved reconstruction quality in dynamic imaging for highly accelerated acquisitions. However, TT‐GRAPPA requires the acquisition of a large number of separate calibration scans. To reduce the overall imaging time, we propose Self‐calibrated Interpolation of Non‐Cartesian data with GRAPPA (SING) to self‐calibrate region‐specific interpolation kernels from dynamic undersampled measurements. The SING method synthesizes calibration data to adapt to the distinct shape of each region‐specific interpolation kernel geometry, and uses a novel local k‐space regularization through an extension of TT‐GRAPPA. This calibration approach is used to reconstruct non‐Cartesian images at high acceleration rates while mitigating noise amplification. The reconstruction quality of SING is compared with conjugate‐gradient SENSE and TT‐GRAPPA in numerical phantoms and in vivo cine data sets. ResultsIn both numerical phantom and in vivo cine data sets, SING offers visually and quantitatively similar reconstruction quality to TT‐GRAPPA, and provides improved reconstruction quality over conjugate‐gradient SENSE. Furthermore, temporal fidelity in SING and TT‐GRAPPA is similar for the same acceleration rates. G‐factor evaluation over the heart shows that SING and TT‐GRAPPA provide similar noise amplification at moderate and high rates. ConclusionThe proposed SING reconstruction enables significant improvement of acquisition efficiency for calibration data, while matching the reconstruction performance of TT‐GRAPPA. 

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3. B-spline Parameterized Joint Optimization of Reconstruction and K-space Sampling Patterns (BJORK) for Accelerated 2D Acquisition

   Wang, G; Luo, T; Nielsen, J.F.; Fessler, J.A.; Noll, D.C. (January 2021, International Society Magnetic Resonance in Medicine)

   The proposed approach, BJORK, provides a robust and generalizable workflow to jointly optimize non-Cartesian sampling patters and a physics-informed reconstruction. Several approaches, including re-parameterization of trajectories, multi-level optimization, and non-Cartesian unrolled neural networks, are introduced to improve training effect and avoid sub-optimal local minima. The invivo experiments show that the networks and trajectories learned on simulation dataset are transferable to the real acquisition even with different parameter-weighted MRI contrasts and noise-levels, and demonstrate improved image quality compared with previous learning-based and model-based trajectory optimization methods. 

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4. Efficient NUFFT backpropagation for stochastic sampling optimization

   Wang, G.; Noll, D.C.; Fessler, J.A. (January 2021, Proceeedings International Society of Magnetic Resonance in Medicine)

   Optimization-based k-space sampling pattern design often involves the Jacobian matrix of non-uniform fast Fourier transform (NUFFT) operations. Previous works relying on auto-differentiation can be time-consuming and less accurate. This work proposes an approximation method using the relationship between exact non-uniform DFT (NDFT) and NUFFT, demonstrating improved results for the sampling pattern optimization problem. 

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5. Deep compressed imaging via optimized pattern scanning

   https://doi.org/10.1364/PRJ.410556  

   Zhang, Kangning; Hu, Junjie; Yang, Weijian (March 2021, Photonics Research)

   The need for high-speed imaging in applications such as biomedicine, surveillance, and consumer electronics has called for new developments of imaging systems. While the industrial effort continuously pushes the advance of silicon focal plane array image sensors, imaging through a single-pixel detector has gained significant interest thanks to the development of computational algorithms. Here, we present a new imaging modality, deep compressed imaging via optimized-pattern scanning, which can significantly increase the acquisition speed for a single-detector-based imaging system. We project and scan an illumination pattern across the object and collect the sampling signal with a single-pixel detector. We develop an innovative end-to-end optimized auto-encoder, using a deep neural network and compressed sensing algorithm, to optimize the illumination pattern, which allows us to reconstruct faithfully the image from a small number of measurements, with a high frame rate. Compared with the conventional switching-mask-based single-pixel camera and point-scanning imaging systems, our method achieves a much higher imaging speed, while retaining a similar imaging quality. We experimentally validated this imaging modality in the settings of both continuous-wave illumination and pulsed light illumination and showed high-quality image reconstructions with a high compressed sampling rate. This new compressed sensing modality could be widely applied in different imaging systems, enabling new applications that require high imaging speeds. 

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