Literature DB >> 30834638

Reconstruction of coronary circulation networks: A review of methods.

Vibujithan Vigneshwaran1,2, Gregory B Sands1, Ian J LeGrice3, Bruce H Smaill1, Nicolas P Smith1,2.   

Abstract

Building anatomically accurate models of the coronary vascular system enables potentially deeper understandings of coronary circulation. To achieve this, (a) images at different levels of vascular network-arteries, arterioles, capillaries, venules, and veins-need to be obtained through suitable imaging modalities; and (b) from images, morphological and topological information needs to be extracted using image processing techniques. While there are several modalities that enable the imaging of large vessels, microcirculation imaging-capturing vessels having diameter lesser than 100 μm-has to date been typically confined to small regions of the heart. This spatially limited microcirculatory information has often been used within cardiac models, with the potentially erroneous assumption that it is representative of the whole organ. However, with the recent advancements in imaging and image processing, it is rapidly becoming feasible to acquire, process, and quantify microcirculation data at the scale of whole organ. In this review, we summarize the progress toward this goal followed through a presentation of the current state-of-the-art imaging and image processing techniques in the context of coronary microcirculation extraction, prominently but not exclusively, from small animals.
© 2019 John Wiley & Sons Ltd.

Keywords:  confocal imaging; coronary vascular system; deep learning; image processing; vessel extraction

Mesh:

Year:  2019        PMID: 30834638     DOI: 10.1111/micc.12542

Source DB:  PubMed          Journal:  Microcirculation        ISSN: 1073-9688            Impact factor:   2.628


  2 in total

1.  Simulation of oxygen transport and estimation of tissue perfusion in extensive microvascular networks: Application to cerebral cortex.

Authors:  Jose T Celaya-Alcala; Grace V Lee; Amy F Smith; Bohan Li; Sava Sakadžić; David A Boas; Timothy W Secomb
Journal:  J Cereb Blood Flow Metab       Date:  2020-06-05       Impact factor: 6.200

2.  Sequential vessel segmentation via deep channel attention network.

Authors:  Dongdong Hao; Song Ding; Linwei Qiu; Yisong Lv; Baowei Fei; Yueqi Zhu; Binjie Qin
Journal:  Neural Netw       Date:  2020-05-13
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.