Kobayashi Daichi, Tomita Hayato, Morimoto Tsuyoshi, Deguchi Yuki, Fukuchi Hirofumi, Ishida Hikaru, Miyakawa Kumie, Kobayashi Yasuyuki, Mimura Hidefumi
Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan.
Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, Kawasaki, Japan.
Nagoya J Med Sci. 2025 May;87(2):264-271. doi: 10.18999/nagjms.87.2.264.
We compared the qualitative and quantitative quality of prostate conventional T2-weighted imaging and T2-weighted imaging with deep-learning reconstruction. Patients with suspected prostate cancer undergoing magnetic resonance imaging between April 2022 and June 2023 were included. Quantitative analysis was performed to determine the signal-to-noise and contrast ratios of the perirectal fat tissue, internal obturator muscle, and pubic tubercle. Eight periprostatic anatomical structures, overall image quality, and motion artifacts were evaluated by two radiologists using 5- or 4-point scales. Qualitative analysis results were compared to determine the agreement between the two radiologists. In total, 106 patients (mean age: 71 ± 8.3 years; 106 men) were included in this study. The acquisition time for conventional T2-weighted imaging and T2-weighted imaging with deep-learning reconstruction was 4 min and 16 s and 2 min and 12 s, respectively. The signal-to-noise ratio of the perirectal fat tissue and internal obturator muscle and contrast ratio of fat/muscle and bone/muscle determined via T2-weighted imaging with deep-learning reconstruction were significantly superior to those determined via conventional T2-weighted imaging (both < 0.01). Compared with conventional T2-weighted imaging, T2-weighted imaging with deep-learning reconstruction showed significant improvement in the visualization of the periprostatic anatomy, overall image quality, and motion artifacts (both < 0.05). Compared with conventional methods, T2-weighted imaging with deep-learning reconstruction facilitated the acquisition of good-quality magnetic resonance images of the prostate within a shorter acquisition time. T2-weighted imaging with deep-learning reconstruction will aid clinicians in diagnosing prostate cancer with shortened acquisition time while maintaining quantitative and qualitative image properties.
我们比较了前列腺常规T2加权成像和深度学习重建的T2加权成像的定性和定量质量。纳入了2022年4月至2023年6月期间接受磁共振成像的疑似前列腺癌患者。进行定量分析以确定直肠周围脂肪组织、闭孔内肌和耻骨结节的信噪比和对比度。由两名放射科医生使用5分或4分制对八个前列腺周围解剖结构、整体图像质量和运动伪影进行评估。比较定性分析结果以确定两名放射科医生之间的一致性。本研究共纳入106例患者（平均年龄：71±8.3岁；106名男性）。常规T2加权成像和深度学习重建的T2加权成像的采集时间分别为4分16秒和2分12秒。通过深度学习重建的T2加权成像确定的直肠周围脂肪组织和闭孔内肌的信噪比以及脂肪/肌肉和骨/肌肉的对比度均显著优于常规T2加权成像确定的结果（均P<0.01）。与常规T2加权成像相比，深度学习重建的T2加权成像在前列腺周围解剖结构的可视化、整体图像质量和运动伪影方面均有显著改善（均P<0.05）。与传统方法相比，深度学习重建的T2加权成像能够在更短的采集时间内获得高质量的前列腺磁共振图像。深度学习重建的T2加权成像将有助于临床医生在缩短采集时间的同时诊断前列腺癌，同时保持图像的定量和定性特性。
