Comprehensive Unbiased Analysis of Vascular Tissue Changes in Accelerated Atherosclerosis Using High-Resolution Ultrasound combined with Photoacoustic Imaging
de Jong, A.; Grasso, V.; van Dijk, K.; Sluiter, T. J.; Quax, P. H. A.; Jose, J.; De Vries, M.
Show abstract
Venous bypass grafts are commonly used to circumvent complex coronary or peripheral artery occlusions. The patency rates, however, are hampered due to accelerated buildup of atherosclerotic lesions in the vein graft wall. Identification of unstable plaques is crucial to guide clinical decision making. In this study, we employ advanced high-resolution ultrasound (US) coupled with spectral photoacoustic imaging (sPAI) to enhance the accurate visualization and analysis of tissue composition in vivo. By applying unbiased spectral analysis, we investigate the composition and plaque instability in a murine vein graft model. MethodMale hypercholesterolemic ApoE3*Leiden mice and normocholesterolemic C57BL/6 mice underwent vein graft surgery in which a caval vein from a donor mouse was interpositioned into the arterial circulation of a recipient at the sight of the right common carotid artery. US imaging with sPAI was conducted on 7, 14, 21, and 28 days after surgery. Spectral curves from the near-infrared (NIR) I region, spanning 680 to 970nm, were extracted using a data-driven approach. Component discovery and cross-correlation analysis were performed with Matlab, and ImageJ reconstructed the components within 3D images. At the endpoint histological analysis of the vein grafts was performed. ResultsAnalysis of the NIRI region revealed distinct components, with 7 and 10 components tested in the cross-correlation map. Relative abundance values identified melanin, oxidized hemoglobin, deoxygenized hemoglobin, lipids, and collagen. Lipids and collagen spectra accurately identified lipid and collagen-rich tissues in vivo. The sPAI analysis of of the vein graft wall in vivo resulted in a 8.7% lipids in the vein graft wall compared to 1.8% lipids in the histological analysis at t=28d. For vein grafts from ApoE*3-Leiden mice no differences in the lipid positive area was observed between the sPAI analysis or histological quantification. The percentages collagen present in the vein graft walls from both strains analyzed via sPAI and histological showed comparable results at t=28d. ConclusionOur study demonstrates that sPAI can be utilized for compositional analysis of murine tissue in an unbiased manner. This methodology can be used to enhance our understanding of vein graft dynamics and holds promise to advance non-invasive characterization of vascular diseases to ultimately guide clinical decision making.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Method for in vivo assessment of cancer tissue inhomogeneity and accurate histology-like morphological segmentation based on Optical Coherence Elastography 94%
- 3D imaging and morphometry of the heart capillary system in spontaneously hypertensive rats and normotensive controls 94%
- Indexing Cerebrovascular Health Using Near-infrared Spectroscopy 94%
Similar papers in this journal
- Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning 94%
- Beyond Life: Exploring Hemodynamic Patterns in Postmortem Mice Brains 94%
- Evaluation of minimum-to-severe global and macrovesicular steatosis in human liver specimens: a portable ambient light-compatible spectroscopic probe 93%
Similar papers in this journal
- Quantitative susceptibility mapping of carotid arterial tissue ex vivo: assessing sensitivity to vessel microstructural composition 93%
- Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks 92%
- Efficient 3D cone trajectory design for improved combined angiographic and perfusion imaging using arterial spin labeling 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.