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Clinical feasibility of 2D FSE sequences of the knee MRI protocol using deep-learning image reconstruction
WU Xiaxia  LU Xuefang  LIU Changsheng  QUAN Guangnan  LIU Weiyin  ZHA Yunfei 

Cite this article as: WU X X, LU X F, LIU C S, et al. Clinical feasibility of 2D FSE sequences of the knee MRI protocol using deep-learning image reconstruction[J]. Chin J Magn Reson Imaging, 2023, 14(5): 53-59. DOI:10.12015/issn.1674-8034.2023.05.011.


[Abstract] Objective To propose a rapid knee imaging based on two-dimensional fast spin echo sequence and examined the reliability and diagnostic performance of deep learning-based reconstruction images on knee joint pathology via comparison of images with and without deep learning reconstruction algorithm (DLR).Materials and Methods A total of 92 patients, a protocol including accelerated two dimensional (2D) fast spin echo (FSE) sequences with autocalibrating reconstruction for cartesian sampling (ARC) as a kind of parallel imaging were enrolled in this prospective study. All MR data was reconstructed with and without DLR as original images of FSE (FSEO) and deep learning reconstruction images of FSE (FSEDL), respectively. Two radiologists subjectively assessed images at the aspects of overall image quality, sharpness and diagnostic confidence using a Likert scale (1-5, 5=best), and also objectively evaluated signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). SNR of femoral marrow, cartilage, synovial fluid, infrapatellar fat pad, anterior cruciate ligament and CNR of cartilage/synovial fluid were measured on proton density weighted imaging (PDWI) sequence and T1 weighted imaging (T1WI) sequence of the knee. Inter-observer and intra-observer subjective score consistency were also computed.Results The overall image quality, sharpness and diagnostic confidence for FSEDL were higher compared to FSE0, showing significantly improved sharpness (P＜0.05). Inter- and intra-reader agreement was substantial to almost perfect (ICC: 0.710-0.898). For objective evaluation, SNR and CNR of PDWIDL and T1WIDL images were significantly higher than that of PDWI0 and T1WI0 images (P＜0.05). Two radiologists respectively assessed the sequences regarding structural abnormalities of the knee based on FSE0 and FSEDL. Inter- and intra-reader agreement were excellent consistent (κ: 0.954-1.000) for the detection of internal derangement. Intra-reader agreement was substantial to almost perfect (κ=0.769, 0.771) for the assessment of cartilage defects and almost perfect (κ: 0.944-1.000) for the assessment of meniscal, ligament, bone marrow, syn-ovial fluid. There were no detection differences of structural abnormalities between FSEDL and FSE0.Conclusions DLR can be used for knee joint PI ARC technology, which can improve the image quality and ensure the clinical diagnosis efficiency at the same time to complete the image acquisition within 5 min, suitable for clinical patients with various knee joint diseases.
[Keywords] knee joint；convolutional neural network；deep learning；image reconstruction；parallel imaging；magnetic resonance imaging

WU Xiaxia1   LU Xuefang1   LIU Changsheng1   QUAN Guangnan2   LIU Weiyin2   ZHA Yunfei1*  

1 Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

2 GE Healthcare, Beijing 100176, China

Corresponding author: Zha YF, E-mail: zhayunfei999@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Xiangyang Medical and Health Field Science and Technology Project (No. 2022YL31B).
Received  2022-10-12
Accepted  2023-01-12
DOI: 10.12015/issn.1674-8034.2023.05.011
Cite this article as: WU X X, LU X F, LIU C S, et al. Clinical feasibility of 2D FSE sequences of the knee MRI protocol using deep-learning image reconstruction[J]. Chin J Magn Reson Imaging, 2023, 14(5): 53-59. DOI:10.12015/issn.1674-8034.2023.05.011.

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