Deep learning speeds MRI scans

Marks, Paul A.

doi:10.1145/3449060

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…However, due to the promising potential that CS-DL showed in supporting various commercial applications, during the following years CS-DL will come of age and the challenges will shift from proving the concept towards integrating it into commercial products. Already, Facebook developed a DL-based faster MRI system that is currently undergoing a study in collaboration with market-leading MRI scanner vendors Siemens, General Electric, and Philips Healthcare (Marks 2021).…”

Section: Key Takeaway Ideasmentioning

confidence: 99%

Deep learning for compressive sensing: a ubiquitous systems perspective

Machidon

Pejović

2022

Artif Intell Rev

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Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS–DL efficient, outline major trends in the CS–DL research space, and derive guidelines for the future evolution of CS–DL within the ubiquitous computing domain.

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Section: Key Takeaway Ideasmentioning

confidence: 99%

Deep learning for compressive sensing: a ubiquitous systems perspective

Machidon

Pejović

2022

Artif Intell Rev

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A New Technique of the Virtual Reality Visualization of Complex Volume Images from the Computer Tomography and Magnetic Resonance Imaging

Vasic

Pierdicca

Frontoni

et al. 2021

Lecture Notes in Computer Science

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A Novel Compression Framework for Electro Cardiac Signals

Reddy¹,

Krishnamoorthi²,

Priyanka³

2021

J. Phys.: Conf. Ser.

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Electrocardiography (ECG) is a method of measuring cardiovascular electrical movement over a span of time using skin-situated electrodes. In patient surveillance, large volumes of ECG information must be preserved. So as long as we essential to track a patient heart working condition, we should have to save more facts. However, it is very expensive to storage, transmit in addition distribute bandwidth. A powerful compression strategy has been practical to solve this problem so that all the excellent scientifically important functionality can be conserved. We used the MIT-BIH ECG information base for ECG research.

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Deep learning speeds MRI scans

Abstract: Machine intelligence significantly reduces the time needed for an MRI scan, which can help reduce patient anxiety.

Cited by 3 publications

References 2 publications

Deep learning for compressive sensing: a ubiquitous systems perspective

Deep learning for compressive sensing: a ubiquitous systems perspective

A New Technique of the Virtual Reality Visualization of Complex Volume Images from the Computer Tomography and Magnetic Resonance Imaging

A Novel Compression Framework for Electro Cardiac Signals

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