Reduced field-of-view DWI based on deep learning reconstruction improving diagnostic accuracy of VI-RADS for evaluating muscle invasion

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2024年6月9日

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Xinxin Zhang,
Xiaojuan Xu,
Yichen Wang,
Jie Zhang,
Mancang Hu,
Jin Zhang,
Lianyu Zhang,
Sicong Wang,
Yi Li,
Xinming Zhao,
Yan Chen

摘要

Objectives

To investigate whether reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with deep learning reconstruction (DLR) can improve the accuracy of evaluating muscle invasion using VI-RADS.

Methods

Eighty-six bladder cancer participants who were evaluated by conventional full field-of-view (fFOV) DWI, standard rFOV (rFOV_STA) DWI, and fast rFOV with DLR (rFOV_DLR) DWI were included in this prospective study. Tumors were categorized according to the vesical imaging reporting and data system (VI-RADS). Qualitative image quality scoring, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC value were evaluated. Friedman test with post hoc test revealed the difference across the three DWIs. Receiver operating characteristic analysis was performed to calculate the areas under the curve (AUCs).

Results

The AUC of the rFOV_STA DWI and rFOV_DLR DWI were higher than that of fFOV DWI. rFOV_DLR DWI reduced the acquisition time from 5:02 min to 3:25 min, and showed higher scores in overall image quality with higher CNR and SNR, compared to rFOV_STA DWI (p < 0.05). The mean ADC of all cases of rFOV_STA DWI and rFOV_DLR DWI was significantly lower than that of fFOV DWI (all p < 0.05). There was no difference in mean ADC value and the AUC for evaluating muscle invasion between rFOV_STA DWI and rFOV_DLR DWI (p > 0.05).

Conclusions

rFOV DWI with DLR can improve the diagnostic accuracy of fFOV DWI for evaluating muscle invasion. Applying DLR to rFOV DWI reduced the acquisition time and improved overall image quality while maintaining ADC value and diagnostic accuracy.

Critical relevance statement

The diagnostic performance and image quality of full field-of-view DWI, reduced field-of-view (rFOV) DWI with and without DLR were compared. DLR would benefit the wide clinical application of rFOV DWI by reducing the acquisition time and improving the image quality.

Key Points

Deep learning reconstruction (DLR) can reduce scan time and improve image quality.

Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with DLR showed better diagnostic performances than full field-of-view DWI.

There was no difference of diagnostic accuracy between rFOV DWI with DLR and standard rFOV DWI.

Graphical Abstract