Dynamic MRI using model‐based deep learning and SToRM priors: MoDL‐SToRM

Purpose To introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi‐channel measurements. Methods Image recovery is formulated as an optimization prob...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 82; H. 1; S. 485 - 494
Hauptverfasser:	Biswas, Sampurna, Aggarwal, Hemant K., Jacob, Mathews
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.07.2019
Schlagworte:	Algorithms alternating minimization Artificial neural networks Breathing Cardiac Imaging Techniques - methods Conjugate gradient method Consistency Data recovery Databases, Factual Deep Learning free breathing cardiac MR Heart Heart - diagnostic imaging Humans Image acquisition Image Processing, Computer-Assisted - methods Image quality Image reconstruction Iterative algorithms Iterative methods learned prior Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Manifolds Measurement methods model‐based Neural networks Noise reduction non‐local prior Optimization Regularization Respiration Smoothness subject specific prior free breathing cardiac MR non-local prior learned prior subject specific prior alternating minimization model-based
ISSN:	0740-3194, 1522-2594, 1522-2594
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Abstract	Purpose To introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi‐channel measurements. Methods Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data. Results The experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time. Conclusions The results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient‐specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework.
AbstractList	Purpose To introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi‐channel measurements. Methods Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data. Results The experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time. Conclusions The results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient‐specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework. PurposeTo introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi‐channel measurements.MethodsImage recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data.ResultsThe experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time.ConclusionsThe results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient‐specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework. To introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements.PURPOSETo introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements.Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data.METHODSImage recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data.The experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time.RESULTSThe experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time.The results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient-specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework.CONCLUSIONSThe results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient-specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework. To introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements. Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset. An iterative algorithm, which alternates between denoizing of the image data using CNN and SToRM, and conjugate gradients (CG) step that minimizes the data consistency cost is introduced. Unrolling the iterative algorithm yields a deep network, which is trained using exemplar data. The experimental results demonstrate that the proposed framework can offer fast recovery of free breathing and ungated cardiac MRI data from less than 8.2s of acquisition time per slice. The reconstructions are comparable in image quality to SToRM reconstructions from 42s of acquisition time, offering a fivefold reduction in scan time. The results show the benefit in combining deep learned CNN priors with complementary image regularization penalties. Specifically, this work demonstrates the benefit in combining the CNN prior that exploits local and population generalizable redundancies together with SToRM, which capitalizes on patient-specific information including cardiac and respiratory patterns. The synergistic combination is facilitated by the proposed framework.
Author	Aggarwal, Hemant K. Biswas, Sampurna Jacob, Mathews
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Keywords	free breathing cardiac MR non-local prior learned prior subject specific prior alternating minimization model-based
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Snippet	Purpose To introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing &... To introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated... PurposeTo introduce a novel framework to combine deep‐learned priors along with complementary image regularization penalties to reconstruct free breathing &...
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SubjectTerms	Algorithms alternating minimization Artificial neural networks Breathing Cardiac Imaging Techniques - methods Conjugate gradient method Consistency Data recovery Databases, Factual Deep Learning free breathing cardiac MR Heart Heart - diagnostic imaging Humans Image acquisition Image Processing, Computer-Assisted - methods Image quality Image reconstruction Iterative algorithms Iterative methods learned prior Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Manifolds Measurement methods model‐based Neural networks Noise reduction non‐local prior Optimization Regularization Respiration Smoothness subject specific prior
Title	Dynamic MRI using model‐based deep learning and SToRM priors: MoDL‐SToRM
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