Convolutional neural network-based reconstruction for acceleration of prostate T2 weighted MR imaging: a retro-and prospective study

Woojin Jung, Eu Hyun Kim, Jingyu Ko, Geunu Jeong, Moon Hyung Choi

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7 Scopus citations

Abstract

Objective: The aim of this study was to develop a deep neural network (DNN)-based parallel imaging reconstruction for highly accelerated 2D turbo spin echo (TSE) data in prostate MRI without quality degradation compared to conventional scans. Methods: 155 participant data were acquired for training and testing. Two DNN models were generated according to the number of acquisitions (NAQ) of input images: DNN-N1 for NAQ = 1 and DNN-N2 for NAQ = 2. In the test data, DNN and TSE images were compared by quantitative error metrics. The visual appropriateness of DNN reconstructions on accelerated scans (DNN-N1 and DNN-N2) and conventional scans (TSE-Conv) was assessed for nine parameters by two radiologists. The lesion detection was evaluated at DNNs and TES-Conv by prostate imaging-reporting and data system. Results: The scan time was reduced by 71% at NAQ = 1, and 42% at NAQ = 2. Quantitative evaluation demonstrated the better error metrics of DNN images (29–43% lower NRMSE, 4–13% higher structure similarity index, and 2.8–4.8 dB higher peak signal-to-noise ratio; p < 0.001) than TSE images. In the assessment of the visual appropriateness, both radiologists evaluated that DNN-N2 showed better or comparable performance in all parameters compared to TSE-Conv. In the lesion detection, DNN images showed almost perfect agree-ment (κ > 0.9) scores with TSE-Conv. Conclusions: DNN-based reconstruction in highly accelerated prostate TSE imaging showed comparable quality to conventional TSE. Advances in knowledge: Our framework reduces the scan time by 42% of conventional prostate TSE imaging without sequence modification, revealing great potential for clinical application.

Original languageEnglish
JournalBritish Journal of Radiology
Volume95
Issue number1133
DOIs
StatePublished - 1 May 2022

Bibliographical note

Funding Information:
This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: KMDF_PR_20200901_0062, 9991006735).

Publisher Copyright:
© 2022 The Authors. Published by the British Institute of Radiology.

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