利用超分辨率深度学习重建增强薄片三维 T2 加权前列腺 MRI:对图像质量和 PI-RADS 评估的影响。

IF 2.1 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic resonance imaging Pub Date : 2024-12-10 DOI:10.1016/j.mri.2024.110308
Kaori Shiraishi, Takeshi Nakaura, Naoki Kobayashi, Hiroyuki Uetani, Yasunori Nagayama, Masafumi Kidoh, Junji Yatsuda, Ryoma Kurahashi, Tomomi Kamba, Yuichi Yamahita, Toshinori Hirai
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引用次数: 0

摘要

研究目的本研究旨在评估超级分辨率深度学习重建(SR-DLR)--一种基于深度学习的技术,可在核磁共振成像重建过程中提高图像分辨率和质量--在改善前列腺核磁共振成像(MRI)中薄片三维T2加权成像(T2WI)和前列腺成像报告和数据系统(PI-RADS)评估的图像质量方面的有效性:这项回顾性研究纳入了2022年11月至2023年4月期间接受SR-DLR前列腺磁共振成像的33名患者。我们获得了前列腺的薄片三维-T2WI,并在有SR-DLR和没有SR-DLR的情况下进行了重建(矩阵分别为720 × 720和240 × 240)。我们计算了前列腺内外腺体之间的对比度和对比度-噪声比(CNR),以及盆腔骨和脂肪组织的斜率。两名放射科医生对图像质量进行了定性评估,并对每次重建进行了 PI-RADS 评分:最终分析包括 28 名男性患者(年龄范围:47-88 岁;平均年龄:70.8 岁)。使用 SR-DLR 的 CNR 明显高于不使用 SR-DLR 的 CNR(1.93 [IQR: 0.79, 3.83] vs. 1.88 [IQR: 0.63, 3.82], p = 0.002)。使用 SR-DLR 和不使用 SR-DLR 的图像对比度无明显差异(p = 0.864)。使用 SR-DLR 的斜率明显高于未使用 SR-DLR 的斜率(0.21 [IQR: 0.15, 0.25] vs. 0.15 [IQR: 0.12, 0.19],p 结论:SR-DLR 有可能成为一种新的诊断方法:摘要:超级分辨率深度学习重建(SR-DLR)显著改善了薄片三维 T2 加权成像(T2WI)的图像质量,且无需延长磁共振成像采集时间。此外,SR-DLR 三维 T2WI 的 PI-RADS 评分与二维 T2WI 的 PI-RADS 评分显示出更高的一致性。
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Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment.

Purposes: This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the image quality of thin-slice 3D T2-weighted imaging (T2WI) and Prostate Imaging-Reporting and Data System (PI-RADS) assessment in prostate Magnetic Resonance Imaging (MRI).

Methods: This retrospective study included 33 patients who underwent prostate MRI with SR-DLR between November 2022 and April 2023. Thin-slice 3D-T2WI of the prostate was obtained and reconstructed with and without SR-DLR (matrix: 720 × 720 and 240 × 240, respectively). We calculated the contrast and contrast-to-noise ratio (CNR) between the internal and external glands of the prostate, as well as the slope of pelvic bone and adipose tissue. Two radiologists evaluated qualitative image quality and assessed PI-RADS scores of each reconstruction.

Results: The final analysis included 28 male patients (age range: 47-88 years; mean age: 70.8 years). The CNR with SR-DLR was significantly higher than without SR-DLR (1.93 [IQR: 0.79, 3.83] vs. 1.88 [IQR: 0.63, 3.82], p = 0.002). No significant difference in contrast was observed between images with and without SR-DLR (p = 0.864). The slope with SR-DLR was significantly higher than without SR-DLR (0.21 [IQR: 0.15, 0.25] vs. 0.15 [IQR: 0.12, 0.19], p < 0.01). Qualitative scores for contrast, sharpness, artifacts, and overall image quality were significantly higher with SR-DLR than without SR-DLR (p < 0.05 for all). The kappa values for 2D-T2WI and 3D-T2WI increased from 0.694 and 0.640 to 0.870 and 0.827 with SR-DLR for both readers.

Conclusions: SR-DLR has the potential to improve image quality and the ability to assess PI-RADS scores in thin-slice 3D-T2WI of the prostate without extending MRI acquisition time.

Summary: Super-Resolution Deep Learning Reconstruction (SR-DLR) significantly improved image quality of thin-slice 3D T2-weighted imaging (T2WI) without extending the acquisition time. Additionally, the PI-RADS scores from 3D-T2WI with SR-DLR demonstrated higher agreement with those from 2D-T2WI.

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来源期刊
Magnetic resonance imaging
Magnetic resonance imaging 医学-核医学
CiteScore
4.70
自引率
4.00%
发文量
194
审稿时长
83 days
期刊介绍: Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.
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