Suppr超能文献

基于时间分辨位移的CDTI心肌细胞取向配准

TIME RESOLVED DISPLACEMENT-BASED REGISTRATION OF CDTI CARDIOMYOCYTE ORIENTATIONS.

作者信息

Verzhbinsky Ilya A, Magrath Patrick, Aliotta Eric, Ennis Daniel B, Perotti Luigi E

机构信息

Department of Radiological Sciences, University of California, Los Angeles, USA.

Department of Bioengineering, University of California, Los Angeles, USA.

出版信息

Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:474-478. doi: 10.1109/ISBI.2018.8363619.

Abstract

cardiac microstructure acquired using cardiac diffusion tensor imaging (cDTI) is a critical component of patient-specific models of cardiac electrophysiology and mechanics. In order to limit bulk motion artifacts and acquisition time, cDTI microstructural data is acquired at a single cardiac phase necessitating registration to the reference configuration on which the patient-specific computational models are based. Herein, we propose a method to register subject-specific microstructural data to an arbitrary cardiac phase using measured cardiac displacements. We validate our approach using a subject-specific computational phantom based on data from human subjects. Compared to a geometry-based non-rigid registration method, the displacement-based registration leads to improved accuracy (less than 1° versus 10° average median error in cardiomyocyte angular differences) and tighter confidence interval (3° versus 65° average upper limit of the 95% confidence interval).

摘要

使用心脏扩散张量成像(cDTI)获取的心脏微观结构是心脏电生理学和力学患者特异性模型的关键组成部分。为了限制整体运动伪影和采集时间,cDTI微观结构数据在单个心动周期采集,这就需要将其配准到患者特异性计算模型所基于的参考构型上。在此,我们提出一种方法,利用测量的心脏位移将个体特异性微观结构数据配准到任意心动周期。我们使用基于人类受试者数据的个体特异性计算模型验证了我们的方法。与基于几何的非刚性配准方法相比,基于位移的配准提高了准确性(心肌细胞角度差异的平均中位数误差从10°降至小于1°),并缩小了置信区间(95%置信区间的平均上限从65°降至3°)。

相似文献

1
TIME RESOLVED DISPLACEMENT-BASED REGISTRATION OF CDTI CARDIOMYOCYTE ORIENTATIONS.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:474-478. doi: 10.1109/ISBI.2018.8363619.
4
5
In Vivo Super-Resolution Cardiac Diffusion Tensor MRI: A Feasibility Study.
Diagnostics (Basel). 2022 Mar 31;12(4):877. doi: 10.3390/diagnostics12040877.
6
Myofiber strain in healthy humans using DENSE and cDTI.
Magn Reson Med. 2021 Jul;86(1):277-292. doi: 10.1002/mrm.28724. Epub 2021 Feb 22.
7
The effects of noise in cardiac diffusion tensor imaging and the benefits of averaging complex data.
NMR Biomed. 2016 May;29(5):588-99. doi: 10.1002/nbm.3500. Epub 2016 Feb 18.
8
Microstructurally Anchored Cardiac Kinematics by Combining DENSE MRI and cDTI.
Funct Imaging Model Heart. 2017 Jun;10263:381-391. doi: 10.1007/978-3-319-59448-4_36. Epub 2017 May 23.
9
Cardiac motion correction based on partial angle reconstructed images in x-ray CT.
Med Phys. 2015 May;42(5):2560-71. doi: 10.1118/1.4918580.

引用本文的文献

2
Estimating cardiomyofiber strain in vivo by solving a computational model.
Med Image Anal. 2021 Feb;68:101932. doi: 10.1016/j.media.2020.101932. Epub 2020 Dec 5.
3
High-Resolution Microstructural MRI After Restoring Ventricular Geometry via 3D Printing.
Funct Imaging Model Heart. 2019 Jun;11504:177-186. doi: 10.1007/978-3-030-21949-9_20. Epub 2019 May 30.
4
Estimating Aggregate Cardiomyocyte Strain Using In Vivo Diffusion and Displacement Encoded MRI.
IEEE Trans Med Imaging. 2020 Mar;39(3):656-667. doi: 10.1109/TMI.2019.2933813. Epub 2019 Aug 8.

本文引用的文献

1
Microstructurally Anchored Cardiac Kinematics by Combining DENSE MRI and cDTI.
Funct Imaging Model Heart. 2017 Jun;10263:381-391. doi: 10.1007/978-3-319-59448-4_36. Epub 2017 May 23.
2
Assessment of Myocardial Microstructural Dynamics by In Vivo Diffusion Tensor Cardiac Magnetic Resonance.
J Am Coll Cardiol. 2017 Feb 14;69(6):661-676. doi: 10.1016/j.jacc.2016.11.051.
3
Electrophysiology of Heart Failure Using a Rabbit Model: From the Failing Myocyte to Ventricular Fibrillation.
PLoS Comput Biol. 2016 Jun 23;12(6):e1004968. doi: 10.1371/journal.pcbi.1004968. eCollection 2016 Jun.
5
Second-order motion-compensated spin echo diffusion tensor imaging of the human heart.
Magn Reson Med. 2016 Apr;75(4):1669-76. doi: 10.1002/mrm.25784. Epub 2015 May 28.
6
Linear invariant tensor interpolation applied to cardiac diffusion tensor MRI.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):494-501. doi: 10.1007/978-3-642-33418-4_61.
8
Tracking myocardial motion from cine DENSE images using spatiotemporal phase unwrapping and temporal fitting.
IEEE Trans Med Imaging. 2007 Jan;26(1):15-30. doi: 10.1109/TMI.2006.884215.
9
Spatial transformations of diffusion tensor magnetic resonance images.
IEEE Trans Med Imaging. 2001 Nov;20(11):1131-9. doi: 10.1109/42.963816.
10
Nonrigid registration using free-form deformations: application to breast MR images.
IEEE Trans Med Imaging. 1999 Aug;18(8):712-21. doi: 10.1109/42.796284.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验