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前列腺3DMRI和超声波的分辨率增强使用隐式神经表示

Ghazaleh Ghorbanzadeh, Maedeh Jamali, Donna Mahboubi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

隐式神经表示 (INRs) 增强前列腺MRI和US图像,以更好地检测病变. 这种新的鼻状表征网络 (SIREN) 方法提高了诊断准确度,并降低了医疗保健成本.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

前列腺MRI和美国成像对于诊断前列腺疾病至关重要.
局限的空间和轴分辨率阻碍了精确的病变检测和临床决策.
传统的超分辨率深度学习方法具有较高的计算成本.

研究的目的:

应用隐性神经表示 (INRs),特别是SIREN,用于前列腺MRI和US图像的超分辨率.
为了提高空间和轴分辨率,并保持细致的解剖结构.
为了提高病变的可见性,以便更准确地诊断前列腺癌.

主要方法:

利用状表示网络 (SIREN),一种INR,用于超分辨率重建.
开发了一种混合损失函数,将平均平方误差 (MSE) 和结构相似度指数 (SSIM) 结合起来.
在前列腺MRI和美国成像数据集上评估了该方法.

主要成果:

基于SIREN的方法有效地恢复了前列腺图像中的高分辨率细节.
损伤的可见性得到了显著改善,有助于放射科医生进行诊断.
与传统的超分辨率技术相比,该方法显示出更高的性能.

结论:

基于INR的超分辨率,特别是使用SIREN,为提高前列腺成像质量提供了一个有希望的解决方案.
改进的图像分辨率可以导致更准确的病变检测和诊断.
这项技术有可能减少对额外手术的需求,降低成本并改善患者的治疗结果.

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