Multispectral optoacoustic tomography resolves smart probe activation in vulnerable plaques

2011 ◽  
Author(s):  
Daniel Razansky ◽  
Niels J. Harlaar ◽  
Jan-Luuk Hillebrands ◽  
Adrian Taruttis ◽  
Eva Herzog ◽  
...  
Keyword(s):  
Optoacoustic Tomography ◽  
Vulnerable Plaques

Video rate optoacoustic tomography of mouse kidney perfusion

2010 ◽  
Vol 35 (14) ◽  
pp. 2475 ◽  
Author(s):  
Andreas Buehler ◽  
Eva Herzog ◽  
Daniel Razansky ◽  
Vasilis Ntziachristos
Keyword(s):  
Mouse Kidney ◽  
Optoacoustic Tomography ◽  
Kidney Perfusion

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

scholarly journals Validation of a New Animal Model of Vulnerable Plaques by Intravascular Optical Coherence TomographyIn Vivo

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Yan Fang ◽  
Sining Hu ◽  
Jingbo Hou ◽  
Lingbo Meng ◽  
Shaosong Zhang ◽  
...  
Keyword(s):  
Animal Model ◽  
Optical Coherence ◽  
Vulnerable Plaques

Sparsity regularized data-space restoration in optoacoustic tomography

2012 ◽  
Author(s):  
Kun Wang ◽  
Richard Su ◽  
Alexander A. Oraevsky ◽  
Mark A. Anastasio
Keyword(s):  
Optoacoustic Tomography ◽  
Data Space

scholarly journals Current and Emerging Clinical Applications of Multispectral Optoacoustic Tomography (MSOT) in Oncology

2016 ◽  
Vol 22 (14) ◽  
pp. 3432-3439 ◽  
Author(s):  
Lacey R. McNally ◽  
Megan Mezera ◽  
Desiree E. Morgan ◽  
Peter J. Frederick ◽  
Eddy S. Yang ◽  
...  
Keyword(s):  
Clinical Applications ◽  
Optoacoustic Tomography

Imaging the distribution of photoswitchable probes with temporally-unmixed multispectral optoacoustic tomography

2016 ◽  
Author(s):  
X. Luís Deán-Ben ◽  
Andre C. Stiel ◽  
Yuanyuan Jiang ◽  
Vasilis Ntziachristos ◽  
Gil G. Westmeyer ◽  
...  
Keyword(s):  
Optoacoustic Tomography

Side branch long lesion with main vessel vulnerable plaques by optical coherence tomography predict target-vessel myocardial infarction after provisional stenting in patients with coronary bifurcation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S.L Chen
Keyword(s):  
Side Branch ◽  
Lesion Group ◽  
Lesion Length ◽  
Coronary Bifurcation ◽  
Coronary Bifurcation Lesions ◽  
Vulnerable Plaques ◽  
Main Vessel ◽  
Bifurcation Lesions ◽  
Provisional Stenting ◽  
One Year

Abstract Background Provisional side branch (SB) stenting is correlated with target-vessel myocardial infarction (TVMI) in patients with coronary bifurcation lesions. However, the underlying mechanisms remain unknown. Objectives We aimed to determine the correlation of SB lesion length with vulnerable plaques using optical coherence tomography (OCT) and TVMI in patients with coronary bifurcation lesions treated by a provisional approach. Methods A total of 405 patients with 405 bifurcation lesions who underwent pre-PCI OCT imaging of both main vessel (MV) and SB was prospectively enrolled. Patients were defined as Long-SB lesion (SB lesion length ≥10 mm) and Short-SB lesion (SB lesion length <10 mm) groups according to quantitative coronary analysis and were also stratified by the presence of vulnerable plaques based on OCT findings. The primary endpoint was the occurrence of TVMI after provisional stenting at one-year follow-up. Results 178 (43.9%) patients had long SB lesions. Vulnerable plaques predominantly localized in the main vessel (MV) and more frequently in the Long-SB lesion group (42.7%) compared to 24.2% in the Short-SB lesion group (p<0.001). At one-year follow-up after provisional stenting, there were 31 (8.1%) TVMIs, with 11.8% in the Long-SB lesion group and 4.4% in the Short-SB lesion group (p=0.009), leading to significant difference in target lesion failure between two groups (15.2% vs. 6.6%, p=0.007). The rate of cardiac death, revascularization, and stent thrombosis was comparable between study groups. By multivariate regression analysis, long SB lesion length (p=0.011), presence of vulnerable plaques in the polygon of confluence (p=0.001), and true coronary bifurcation lesions (p=0.004) were three independent factors of TVMI. Conclusions Long-SB lesion length with MV vulnerable plaques predict increased TVMI after provisional stenting in patients with true coronary bifurcation lesions. Further study is warranted to identify the better stenting techniques for coronary bifurcation lesions with long lesion in the SB Kaplan-Meier survival curve Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): NSFC

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