Novel deep learning-based T2-weighted imaging of the prostate provides superior image quality

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KE Zan LI Liang SONG Xinyang WEN Zhi GAO Yufan LIU Weiyin QUAN Guangnan ZHA Yunfei

Cite this article as: KE Z, LI L, SONG X Y, et al. Novel deep learning-based T2-weighted imaging of the prostate provides superior image quality[J]. Chin J Magn Reson Imaging, 2023, 14(5): 41-47. DOI:10.12015/issn.1674-8034.2023.05.009.

Abstract

[Abstract] Objective To introduce a novel deep learning-based reconstruction (DLR, which is now commercially available as AIR^TM Recon DL, GE Healthcare) T2-weighted imaging (T2WIDL) sequence in prostate MRI and investigate its image quality and diagnostic confidence compared to conventional T2-weighted imaging (T2WIC).Materials and Methods Seventy-eight patients who underwent prostate MRI examinations (T2WIC and T2WIDL with the same parameters) were included in this retrospective study. For the qualitative and diagnostic confidence evaluation, double-blinded evaluation was performed by both three- and seven-year experienced radiologists according to the Likert Scale (5=excellent, 1=very poor), and then the difference among the scores were evaluated using Wilcoxon test and the intra- /inter- observer agreement were evaluated using κ statistics. The evaluation indicators of T2WI image quality and diagnostic confidence including: prostate capsule, lesion contrast and edge sharpness, anatomical details (urethra, zone of prostate, seminal vesicle), skeleton and muscle clarity, overall image quality, lesion location and morphology, lesion is benign or malignant. In addition, the time spent by two radiologists browsing each set of images was recorded respectively, and the paired t test was used for statistical analysis. As for quantitative evaluation, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measured between each prostate lesion on the MR images acquired with different sequences were analyzed, paired t test and Mann-Whitney U test were used for statistical analysis.Results Seventy-eight patients at the mean age of (67.1±9.9) years were included in this retrospective study. Based on the subjective scoring criteria, overall image quality scores were rated significantly superior by both readers with (4.6±0.6) and (4.3±0.7) on T2WIDL compared to (3.4±0.7) and (3.0±0.8) on T2WIC (P＜0.05). For T2WIDL, the score consistency ranged from 0.6 to 0.8; there were significant differences in the scores between the two readers only for anatomical details and overall image quality (P＜0.05). Besides, overall diagnostic confidence scores also were rated significantly superior by both readers with (4.8±0.3) and (4.8±0.4) on T2WIDL compared to (3.8±0.4) and (3.7±0.5) on T2WIC (P＜0.05), with fewer time to spend. Based on objective evaluation, SNR and CNR of T2WIDL were higher than those of T2WIC, and the differences were statistically significant (P＜0.05). The SNR of T2WIC and T2WIDL in benign and malignant lesions were (12.4±2.4), (28.7±8.1) and (10.1±1.8), (27.7±5.4), respectively, with significant differences (P＜0.01). There was no significant difference in CNR between benign and malignant lesions with and without DL (P＞0.05).Conclusions The prostate T2WIDL images have high subjective rating scores, clearer lesion contrast, high SNR and CNR. In addition, the radiologists had more diagnostic confidence in T2WIDL image with less diagnostic time. Therefore, the novel DLR technique is helpful to improve the image quality of prostate T2WI within the same scanning time, which provides a more accurate imaging basis for clinical diagnosis and treatment.

[Keywords] prostate；prostate cancer；deep learning；magnetic resonance imaging；image quality；signal-to-noise ratio；contrast-to-noise ratio

Contributor Information

KE Zan¹ LI Liang¹ SONG Xinyang² WEN Zhi¹ GAO Yufan¹ LIU Weiyin³ QUAN Guangnan³ ZHA Yunfei^1*

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

² Department of Radiology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China

³ GE Healthcare (China) Co., Ltd., Beijing 100176, China

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

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81601461).

Received 2022-09-14

Accepted 2022-11-29

DOI: 10.12015/issn.1674-8034.2023.05.009

Text

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