Learned Primal-Dual Reconstruction

Adler, Jonas; Öktem, Ozan

doi:10.1109/tmi.2018.2799231

Cited by 740 publications

(720 citation statements)

References 23 publications

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“…A conventional BM3D denoiser also proved to be effective in jumpstarting SMS‐NEATR, but the performance was consistently better using a learned denoiser tailored for the specific application (Figure and Supporting Information Figures S3 and S4). We anticipate further gains from advanced models that could simultaneously enforce data consistency and perform learned filtering . This would also streamline the SMS‐NEATR pipeline and reduce the number of steps.…”

Section: Discussionmentioning

confidence: 99%

“…These ideas treat the iterations in gradient‐descent–type reconstructions as unrolled networks to retain fidelity to acquired data through a forward model, while learning model parameters that map the reconstruction to a reference image . Importantly, such combination of a forward model and learned filtering provided further improvement than a model‐based reconstruction followed by U‐Net denoising …”

Section: Discussionmentioning

confidence: 99%

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Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Bilgic̦

Chatnuntawech

Manhard

et al. 2019

Magnetic Resonance in Med

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Purpose To introduce a combined machine learning (ML)‐ and physics‐based image reconstruction framework that enables navigator‐free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high‐resolution structural and diffusion imaging. Methods Single‐shot EPI is an efficient encoding technique, but does not lend itself well to high‐resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high‐quality msEPI has been elusive because of phase mismatch arising from shot‐to‐shot variations which preclude the combination of the multiple‐shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot‐to‐shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC‐SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. Results Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8‐ × 2‐fold acceleration using 2 EPI shots for multiecho imaging, so that whole‐brain T2 and T2* parameter maps could be derived from an 8.3‐second acquisition at 1 × 1 × 3‐mm3 resolution. This has also allowed high‐resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9‐ × 2‐fold acceleration. To make these possible, we extended the state‐of‐the‐art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. Conclusion Combination of ML and JVC‐SENSE enabled navigator‐free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end‐to‐end ML approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Bilgic̦

Chatnuntawech

Manhard

et al. 2019

Magnetic Resonance in Med

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show abstract

“…We demonstrate how ML can be effectively incorporated into retrospective motion correction approaches based on data consistency error minimization. Other works have balanced data consistency error with ML generated image priors (created using variational networks) to dramatically reduce reconstruction times and improve image quality for highly accelerated acquisitions . Here, we demonstrate the effective use of ML to guide each step in an iterative model‐based retrospective motion correction.…”

Section: Introductionmentioning

confidence: 85%

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

Haskell

Cauley

Bilgic̦

et al. 2019

Magnetic Resonance in Med

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Purpose We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model‐based motion minimization. Methods A convolutional neural network (CNN) trained to remove motion artifacts from 2D T2‐weighted rapid acquisition with refocused echoes (RARE) images is introduced into a model‐based data‐consistency optimization to jointly search for 2D motion parameters and the uncorrupted image. Our separable motion model allows for efficient intrashot (line‐by‐line) motion correction of highly corrupted shots, as opposed to previous methods which do not scale well with this refinement of the motion model. Final image generation incorporates the motion parameters within a model‐based image reconstruction. The method is tested in simulations and in vivo motion experiments of in‐plane motion corruption. Results While the convolutional neural network alone provides some motion mitigation (at the expense of introduced blurring), allowing it to guide the iterative joint‐optimization both improves the search convergence and renders the joint‐optimization separable. This enables rapid mitigation within shots in addition to between shots. For 2D in‐plane motion correction experiments, the result is a significant reduction of both image space root mean square error in simulations, and a reduction of motion artifacts in the in vivo motion tests. Conclusion The separability and convergence improvements afforded by the combined convolutional neural network+model‐based method shows the potential for meaningful postacquisition motion mitigation in clinical MRI.

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“…Recently, deep learning approaches have demonstrated impressive performance improvement over conventional iterative methods for low-dose CT [9][10][11] and sparse-view CT. [12][13][14][15] The main advantage of deep learning approach over the conventional MBIR approaches is that the network learns the image statistics in a fully data-driven way rather than using hand-tuned regularizations. While these approaches usually take time for training, real-time reconstruction is possible once the network is trained, making the algorithm very practical in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography

et al. 2018

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Purpose In multiphase coronary CT angiography (CTA), a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low‐dose phases is significantly degraded. Recently, deep neural network approaches based on supervised learning technique have demonstrated impressive performance improvement over conventional model‐based iterative methods for low‐dose CT. However, matched low‐ and routine‐dose CT image pairs are difficult to obtain in multiphase CT. To address this problem, we aim at developing a new deep learning framework. Method We propose an unsupervised learning technique that can remove the noise of the CT images in the low‐dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not applicable due to the differences in the underlying heart structure in two phases, the images are closely related in two phases, so we propose a cycle‐consistent adversarial denoising network to learn the mapping between the low‐ and high‐dose cardiac phases. Results Experimental results showed that the proposed method effectively reduces the noise in the low‐dose CT image while preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality. Conclusions The proposed network can learn the image distributions from the routine‐dose cardiac phases, which is a big advantage over the existing supervised learning networks that need exactly matched low‐ and routine‐dose CT images. Considering the effectiveness and practicability of the proposed method, we believe that the proposed can be applied for many other CT acquisition protocols.

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Learned Primal-Dual Reconstruction

Cited by 740 publications

References 23 publications

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography

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