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Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA

2024-02-10 00:00:40
Shaojie Tang, Penpen Miao, Xingyu Gao, Yu Zhong, Dantong Zhu, Haixing Wen, Zhihui Xu, Qiuyue Wei, Hongping Yao, Xin Huang, Rui Gao, Chen Zhao, Weihua Zhou

Abstract

A method was proposed for the point cloud-based registration and image fusion between cardiac single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) and cardiac computed tomography angiograms (CTA). Firstly, the left ventricle (LV) epicardial regions (LVERs) in SPECT and CTA images were segmented by using different U-Net neural networks trained to generate the point clouds of the LV epicardial contours (LVECs). Secondly, according to the characteristics of cardiac anatomy, the special points of anterior and posterior interventricular grooves (APIGs) were manually marked in both SPECT and CTA image volumes. Thirdly, we developed an in-house program for coarsely registering the special points of APIGs to ensure a correct cardiac orientation alignment between SPECT and CTA images. Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images. Finally, the image fusion between SPECT and CTA was realized after the fine registration. The experimental results showed that the cardiac orientation was aligned well and the mean distance error of the optimal registration method (CPD with affine transform) was consistently less than 3 mm. The proposed method could effectively fuse the structures from cardiac CTA and SPECT functional images, and demonstrated a potential in assisting in accurate diagnosis of cardiac diseases by combining complementary advantages of the two imaging modalities.

Abstract (translated)

提出了一种基于点云的超声心动图(SPECT)心肌灌注图像(MPI)和心脏计算机断层扫描(CTA)之间的注册和图像融合方法。首先,使用不同训练的U-Net神经网络对SPECT和CTA图像中的左心室(LV)心尖壁(LVERs)进行分割。其次,根据心脏解剖学的特点,在SPECT和CTA图像体积中手动标记前间室后腔(APIG)的特别点。第三,我们开发了一个内部程序,用于将APIG的特别点与SPECT和CTA图像中的LV心尖壁(LVERs)进行粗略对齐,以确保正确的的心脏方向对齐。第四,使用ICP、SICP或CPD算法对点云(包括APIG的特别点)进行精细对齐,以实现SPECT和CTA图像中LV心尖壁的点云对齐。最后,在对齐后进行图像融合。实验结果表明,心脏方向对齐良好,最优对齐方法的平均距离误差(通过平滑变换的CPD)始终小于3毫米。所提出的方法可以有效地将心脏的SPECT和CTA功能图像的结构融合在一起,并表明通过结合两种成像模态的互补优势,有助于准确诊断心脏疾病。

URL

https://arxiv.org/abs/2402.06841

PDF

https://arxiv.org/pdf/2402.06841.pdf


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