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19.02.2022 | Imaging Informatics and Artificial Intelligence

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging

verfasst von: Caohui Duan, Yongqin Xiong, Kun Cheng, Sa Xiao, Jinhao Lyu, Cheng Wang, Xiangbing Bian, Jing Zhang, Dekang Zhang, Ling Chen, Xin Zhou, Xin Lou

Erschienen in: European Radiology | Ausgabe 8/2022

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Abstract

Objectives

Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and evaluate the clinical feasibility of this approach.

Methods

A complex-valued convolutional neural network (ComplexNet) was developed to reconstruct high-quality SWI from highly accelerated k-space data. ComplexNet can leverage the inherently complex-valued nature of SWI data and learn richer representations by using complex-valued network. SWI data were acquired from 117 participants who underwent clinical brain MRI examination between 2019 and 2021, including patients with tumor, stroke, hemorrhage, traumatic brain injury, etc. Reconstruction quality was evaluated using quantitative image metrics and image quality scores, including overall image quality, signal-to-noise ratio, sharpness, and artifacts.

Results

The average reconstruction time of ComplexNet was 19 ms per section (1.33 s per participant). ComplexNet achieved significantly improved quantitative image metrics compared to a conventional compressed sensing method and a real-valued network with acceleration rates of 5 and 8 (p < 0.001). Meanwhile, there was no significant difference between fully sampled and ComplexNet approaches in terms of overall image quality and artifacts (p > 0.05) at both acceleration rates. Furthermore, ComplexNet showed comparable diagnostic performance to the fully sampled SWI for visualizing a wide range of pathology, including hemorrhage, cerebral microbleeds, and brain tumor.

Conclusions

ComplexNet can effectively accelerate SWI while providing superior performance in terms of overall image quality and visualization of pathology for routine clinical brain imaging.

Key Points

• The complex-valued convolutional neural network (ComplexNet) allowed fast and high-quality reconstruction of highly accelerated SWI data, with an average reconstruction time of 19 ms per section.

• ComplexNet achieved significantly improved quantitative image metrics compared to a conventional compressed sensing method and a real-valued network with acceleration rates of 5 and 8 (p < 0.001).

• ComplexNet showed comparable diagnostic performance to the fully sampled SWI for visualizing a wide range of pathology, including hemorrhage, cerebral microbleeds, and brain tumor.

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Titel: Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging
verfasst von: Caohui Duan
Yongqin Xiong
Kun Cheng
Sa Xiao
Jinhao Lyu
Cheng Wang
Xiangbing Bian
Jing Zhang
Dekang Zhang
Ling Chen
Xin Zhou
Xin Lou
Publikationsdatum: 19.02.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 8/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08638-1

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Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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Abstract

Objectives

Methods

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Conclusions

Key Points

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