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Deep learning-based reconstruction of diffusion-weighted imaging images to assess the activity of thyroid-associated ophthalmopathy
WANG Yunmeng  CUI Yuanyuan  NI Shuangshuang  DAI Jiankun  WAN Xinyi  CHEN Xin  JIANG Qinling  CHENG Yuxin  ZHANG Tianran  MA Yichuan  XIAO Yi 

Cite this article as: WANG Y M, CUI Y Y, NI S S, et al. Deep learning-based reconstruction of diffusion-weighted imaging images to assess the activity of thyroid-associated ophthalmopathy[J]. Chin J Magn Reson Imaging, 2024, 15(10): 36-42, 68. DOI:10.12015/issn.1674-8034.2024.10.007.


[Abstract] Objective To investigate the value of deep learning reconstruction (DLR) orbital diffusion weighted imaging (DWI) images in the assessment of active and inactive stages of thyroid-associated ophthalmopathy (TAO).Materials and Methods This prospectively study included 73 clinically diagnosed TAO patients (46 active TAO, 27 inactive TAO) and 26 healthy controls from April to September 2023. All participants underwent orbital MRI scans using a 3.0 T MRI scanner and a 21ch head-and-neck combined coil. DWI sequences with field of view optimized and constrained undistorted single-shot imaging and multiplexed sensitivity encoding (FOCUS MUSE) were reconstructed by conventional reconstruction (ConR) and DLR. Two diagnostic radiologists independently subjectively evaluated the image quality of the two sequences using a four-point Likert scale. The image quality was objectively evaluated by measuring the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of extraocular muscle (EOM). The DWI apparent diffusion coefficient (ADC) of EOM was used to distinguishing active from inactive TAO. The Wilcoxon was applied to test the difference of SNR, CNR, and ADC between ConR and DLR DWI, separately. Using the Clinical Activity Score (CAS) as the gold standard. The Kruskal-Wallis test was used to compare the difference of ADC between healthy controls, active and inactive TAO patients. Receiver operating characteristics (ROC) curves were used to compare the diagnostic performance of EOM ADC for differentiating active from inactive TAO patients between ConR and DLR DWI. The correlation between the EOM ADC and CAS of TAO patients was analyzed using Spearman's rank correlation coefficient.Results DLR DWI had significantly higher subjective scores than ConR DWI for Sharpness of boundaries and overall image quality. The intra- and inter-reader agreement for both sequences was good (Kappa>0.650). Significantly higher SNR and CNR in EOM DLR DWI compared to ConR (all P<0.001). No significant difference of EOM ADC was observed between ConR and DLR DWI (P>0.05). In both sequences, the EOM ADC obtained was significantly higher in the active TAO than in both inactive TAO and healthy controls, respectively (all P<0.001). There was no significant difference of EOM ADC between inactive TAO and healthy controls (P>0.05). The EOM ADC extracted from both ConR DWI (r=0.637, P<0.001) and DLR DWI (r=0.662, P<0.001) was significantly positively correlated with the CAS. Compared with ConR DWI, DLR DWI presented better performance for discriminating active from inactive TAO patients (area under the curve: 0.959 vs. 0.939, P=0.020).Conclusions DLR improved the image quality of orbital DWI without increasing scan time. Compared to ConR, ADC values obtained based on DLR DWI were improved in identifying the activity of TAO and correlation with CAS.
[Keywords] thyroid-associated ophthalmopathy；extraocular muscle；diffusion weighted imaging；deep learning reconstruction；magnetic resonance imaging

WANG Yunmeng1, 2   CUI Yuanyuan2   NI Shuangshuang2   DAI Jiankun3   WAN Xinyi2   CHEN Xin2   JIANG Qinling2   CHENG Yuxin2   ZHANG Tianran2   MA Yichuan4*   XIAO Yi2*  

1 Graduate School of Bengbu Medical University, Bengbu 233000, China

2 Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China

3 General Electric Healthcare Systems Trade Development (Shanghai) Company, Shanghai 200120, China

4 Department of Radiology, the First Affiliated Hospital of Bengbu Medical University, Bengbu 233000, China

Corresponding author: XIAO Y, E-mail: xiaoyi@188.com MA Y C, E-mail: myc57688754@163.com

Conflicts of interest   None.

Received  2024-01-10
Accepted  2024-05-13
DOI: 10.12015/issn.1674-8034.2024.10.007
Cite this article as: WANG Y M, CUI Y Y, NI S S, et al. Deep learning-based reconstruction of diffusion-weighted imaging images to assess the activity of thyroid-associated ophthalmopathy[J]. Chin J Magn Reson Imaging, 2024, 15(10): 36-42, 68. DOI:10.12015/issn.1674-8034.2024.10.007.

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