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使用视觉变压器从Cine心脏MRI图像进行心脏运动估计的可变形图像注册

Roshan Reddy Upendra¹, Richard Simon², Suzanne M Shontz^3,4,5

¹Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH

|October 11, 2024

概括

这项研究引入了一种混合CNN-ViT模型,用于使用4D电影心脏MRI进行准确的心脏运动估计. 这种新方法显著改善了可变形图像的记录,相对于现有的方法来更好地评估心脏功能.

科学领域:

医学成像医学成像
生物医学工程生物医学工程
医疗保健中的人工智能

背景情况:

准确的心脏运动估计对于评估心脏功能和诊断心肌疾病至关重要.
4D电影心脏MRI提供高分辨率的3D心脏图像,使心脏运动在整个心脏周期的详细分析.
估计心脏运动通常被视为图像注册问题,需要精确对准连续的3D体积.

研究的目的:

提出一种新的混合卷积神经网络 (CNN) 和视觉转换器 (ViT) 架构,用于3D电影心脏MRI的可变形图像注册.
实现一致和准确的心脏运动估计,以改善区域心脏功能量化.
评估拟议方法的性能与已建立的技术,如VoxelMorph CNN和B-spline自由形式变形 (FFD) 相比.

主要方法:

开发一种混合CNN-ViT模型用于可变形图像注册.
该模型应用于用于光流表示和运动估计的4D电影心脏MRI数据集.
使用自动心脏诊断挑战 (ACDC) 数据集进行比较分析.
与 VoxelMorph CNN 和传统的 B-spline FFD 算法进行基准测试.

主要成果:

与VoxelMorph CNN模型相比,拟议的混合CNN-ViT方法在可变形图像注册方面表现出优异的性能.
这种新的方法显著超过了传统的B-spline FFD非刚性图像注册算法.
实现了一致而准确的心脏运动估计,从而改善了区域心脏功能的量化.

结论:

混合CNN-ViT架构为4D电影心脏MRI的精确心脏运动估计提供了有希望的进步.
这种方法增强了对心室动力学和收缩性质的评估.
这些发现支持临床心脏病学中先进深度学习模型的潜力,用于疾病诊断和治疗规划.

关键词:

心脏磁力共振成像 (MRI)心脏运动估计估计深度学习 (Deep Learning) 是一种深度学习.医疗图像注册医疗图像注册视觉变压器视觉变压器

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