A deep-learning-based pipeline for automatic fusion of CT coronary angiogram and stress perfusion CMR

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2026-04-10 DOI:10.1002/mp.70420

Wenting Jiang, Ming-Yen Ng, Tsun-Hei Sin, Peng Cao

{"title":"A deep-learning-based pipeline for automatic fusion of CT coronary angiogram and stress perfusion CMR","authors":"Wenting Jiang, Ming-Yen Ng, Tsun-Hei Sin, Peng Cao","doi":"10.1002/mp.70420","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Accurate evaluation of coronary artery constriction and myocardial ischemia is essential for diagnosing and managing coronary artery disease (CAD). Combining CT coronary angiography (CTCA) and stress cardiovascular magnetic resonance (CMR) imaging allows examination of both coronary artery narrowing and myocardial perfusion.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>To develop a deep learning pipeline that integrates CTCA and CMR images, which could help improve accuracy in identifying affected vessels and their associated myocardial territories.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The proposed pipeline included two deep learning models: one for automatic reorientation of 3D CTCA and another for left ventricle (LV) wall registration between CTCA and CMR images. A 3D spatial co-registration model, the reorientation spatial transformer network (Reorientation STN), predicted reorientation parameters for input CTCA volumes using ResNet18 and STN. A 2D nonrigid spatial deformation network (Nonrigid SDN) was trained for LV wall registration. Cross-modal supervision was employed during training. Evaluation criteria included aspect ratio (AR), Dice similarity coefficient (DSC), and long-axis deviation angles. The process involved quantifying LV wall perfusion on registered CMR images and extracting coronary arteries from reoriented CTCA images to fuse these results. The pipeline was trained and validated on 447 pairs of CTCA and CMR images from 75 patients and tested on 18 subjects.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The pipeline achieved an AR of 0.94 ± 0.03, long-axis deviation angles of 1.19 ± 0.83 (axial) and 1.54 ± 0.79 (coronal), a DSC of 0.66 ± 0.04 for LV wall reorientation, and a DSC of 0.92 ± 0.03 for LV wall registration between CTCA and CMR.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>This automated framework successfully fuses cardiac CTCA and CMR imaging, demonstrating its potential effectiveness.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"53 4","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13067337/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"3","ListUrlMain":"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70420","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Accurate evaluation of coronary artery constriction and myocardial ischemia is essential for diagnosing and managing coronary artery disease (CAD). Combining CT coronary angiography (CTCA) and stress cardiovascular magnetic resonance (CMR) imaging allows examination of both coronary artery narrowing and myocardial perfusion.

Purpose

To develop a deep learning pipeline that integrates CTCA and CMR images, which could help improve accuracy in identifying affected vessels and their associated myocardial territories.

Methods

The proposed pipeline included two deep learning models: one for automatic reorientation of 3D CTCA and another for left ventricle (LV) wall registration between CTCA and CMR images. A 3D spatial co-registration model, the reorientation spatial transformer network (Reorientation STN), predicted reorientation parameters for input CTCA volumes using ResNet18 and STN. A 2D nonrigid spatial deformation network (Nonrigid SDN) was trained for LV wall registration. Cross-modal supervision was employed during training. Evaluation criteria included aspect ratio (AR), Dice similarity coefficient (DSC), and long-axis deviation angles. The process involved quantifying LV wall perfusion on registered CMR images and extracting coronary arteries from reoriented CTCA images to fuse these results. The pipeline was trained and validated on 447 pairs of CTCA and CMR images from 75 patients and tested on 18 subjects.

Results

The pipeline achieved an AR of 0.94 ± 0.03, long-axis deviation angles of 1.19 ± 0.83 (axial) and 1.54 ± 0.79 (coronal), a DSC of 0.66 ± 0.04 for LV wall reorientation, and a DSC of 0.92 ± 0.03 for LV wall registration between CTCA and CMR.

Conclusions

This automated framework successfully fuses cardiac CTCA and CMR imaging, demonstrating its potential effectiveness.

Abstract Image

查看原文本刊更多论文

基于深度学习的CT冠状动脉造影与应力灌注CMR自动融合流水线。

背景：准确评估冠状动脉收缩和心肌缺血对冠状动脉疾病（CAD）的诊断和治疗至关重要。结合CT冠状动脉造影（CTCA）和应激心血管磁共振（CMR）成像可以检查冠状动脉狭窄和心肌灌注。目的：开发一种整合CTCA和CMR图像的深度学习管道，有助于提高识别受影响血管及其相关心肌区域的准确性。方法：提出的管道包括两个深度学习模型：一个用于3D CTCA的自动重定向，另一个用于CTCA和CMR图像之间的左心室（LV）壁配准。一个三维空间共配准模型，即重定向空间变压器网络（reorientation STN），利用ResNet18和STN预测了输入CTCA体的重定向参数。训练二维非刚性空间变形网络（non -刚性SDN）进行左室壁配准。培训过程中采用跨模式监督。评估标准包括宽高比（AR）、骰子相似系数（DSC）和长轴偏差角。该过程包括量化注册CMR图像上的左室壁灌注，并从重新定向的CTCA图像中提取冠状动脉以融合这些结果。该管道在来自75名患者的447对CTCA和CMR图像上进行了训练和验证，并在18名受试者上进行了测试。结果：CTCA与CMR管道的AR值为0.94±0.03，长轴偏移角分别为1.19±0.83（轴向）和1.54±0.79（冠状），左室壁重定向的DSC值为0.66±0.04，左室壁配准的DSC值为0.92±0.03。结论：该自动化框架成功融合了心脏CTCA和CMR成像，显示了其潜在的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.