KEYWORDS: Shrinkage, Optical coherence tomography, Finite element methods, Arteries, Data processing, Ultrasonography, Spectroscopy, Near infrared spectroscopy, In vivo imaging, Computer simulations
Coronary artery plaque structural stress (PSS) is associated with plaque vulnerability and is quantifiable in vivo with optical coherence tomography (OCT) and near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) but the accuracy of these is unclear. This study explored the performance of the two modalities in measuring PSS using histology as reference standard. NIRS-IVUS and OCT images obtained in vessels under physiological pressure require transformation to a zero-pressure condition to estimate PSS. Two methods were examined to achieve this – uniform and non-uniform shrinkage (which may to be superior for eccentric plaques) followed by PSS computation which was compared to histology-derived PSS. NIRS-IVUS and OCT imaging were conducted ex vivo in cadaveric human coronaries prior to histological analysis. In 93 pairs of NIRS-IVUS-histology and 88 pairs of OCT-histology sections, the correlation between the PSS estimated by histology and NIRS-IVUS using the uniform shrinkage approach was higher than that derived by OCT. Non-uniform shrinkage resulted in a numerically lower correlation but no significant difference by Bland-Altman analysis compared to uniform shrinkage.
Accurate classification of plaque composition is essential for treatment planning. Deep learning (DL) methods have been introduced for this purpose, to analyze intravascular images and characterize in a fast and subjective manner plaque types. In this study, we compared the efficacy of two DL methods, designed to process data acquired by two intravascular–an optical coherence tomography (OCT) and a near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS)–catheters to assess plaque types using histology as the reference standard. We matched histology, OCT, and NIRS-IVUS images, compared their estimations, and found that the DL method developed for NIRS-IVUS analysis had a better correlation with histology for calcific and lipidic tissue as compared with the OCT-DL method while both methods had a moderate correlation with the estimations of histology for fibrotic tissue. These findings could be attributed to the fact that OCT due to its poor penetration especially in lesions with large plaque burden fails to identify the deep-seated plaque and also to the fact that the NIRS-IVUS-DL method was developed with the use of histology instead of experts’ analysis.
SignificanceNear-infrared fluorescence imaging still lacks a standardized, objective method to evaluate fluorescent dye efficacy in oncological surgical applications. This results in difficulties in translation between preclinical to clinical studies with fluorescent dyes and in the reproduction of results between studies, which in turn hampers further clinical translation of novel fluorescent dyes.AimOur aim is to develop and evaluate a semi-automatic standardized method to objectively assess fluorescent signals in resected tissue.ApproachA standardized imaging procedure was designed and quantitative analysis methods were developed to evaluate non-targeted and tumor-targeted fluorescent dyes. The developed analysis methods included manual selection of region of interest (ROI) on white light images, automated fluorescence signal ROI selection, and automatic quantitative image analysis. The proposed analysis method was then compared with a conventional analysis method, where fluorescence signal ROIs were manually selected on fluorescence images. Dice similarity coefficients and intraclass correlation coefficients were calculated to determine the inter- and intraobserver variabilities of the ROI selections and the determined signal- and tumor-to-background ratios.ResultsThe proposed non-targeted fluorescent dyes analysis method showed statistically significantly improved variabilities after application on indocyanine green specimens. For specimens with the targeted dye SGM-101, the variability of the background ROI selection was statistically significantly improved by implementing the proposed method.ConclusionSemi-automatic methods for standardized quantitative analysis of fluorescence images were successfully developed and showed promising results to further improve the reproducibility and standardization of clinical studies evaluating fluorescent dyes.
Coronary artery trees (CATs) are often extracted to aid the fully automatic analysis of coronary artery disease on coronary computed tomography angiography (CCTA) images. Automatically extracted CATs often miss some arteries or include wrong extractions which require manual corrections before performing successive steps. For analyzing a large number of datasets, a manual quality check of the extraction results is time-consuming. This paper presents a method to automatically calculate quality scores for extracted CATs in terms of clinical significance of the extracted arteries and the completeness of the extracted CAT. Both right dominant (RD) and left dominant (LD) anatomical statistical models are generated and exploited in developing the quality score. To automatically determine which model should be used, a dominance type detection method is also designed. Experiments are performed on the automatically extracted and manually refined CATs from 42 datasets to evaluate the proposed quality score. In 39 (92.9%) cases, the proposed method is able to measure the quality of the manually refined CATs with higher scores than the automatically extracted CATs. In a 100-point scale system, the average scores for automatically and manually refined CATs are 82.0 (±15.8) and 88.9 (±5.4) respectively. The proposed quality score will assist the automatic processing of the CAT extractions for large cohorts which contain both RD and LD cases. To the best of our knowledge, this is the first time that a general quality score for an extracted CAT is presented.
Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.
KEYWORDS: Principal component analysis, Feature extraction, Visualization, Signal to noise ratio, Mass spectrometry, Spatial resolution, Zoom lenses, Spectral resolution, Denoising, Digital filtering
Imaging mass spectrometry is a technique to determine of which materials a small, physical sample is made.
Current feature extraction techniques fail to extract certain small, high resolution characteristics from these
multi-spectral datacubes. Causes are a low signal-to-noise ratio, the presence of dominant but uninteresting
features, and the huge amount of variables in the dataset. In this paper, we present a zooming technique based on principal component analysis (PCA) to select regions
in a datacube for enhanced feature extraction at the highest possible resolution. It enables the selection of
spectral and spatial regions at a low resolution and recursively apply PCA to zoom in on interesting, correlated
features. This approach is not based on complex and data-specific denoising algorithms. Moreover, it decreases
execution time when additional filters have to be applied.
The technique utilizes a higher signal-to-noise ratio in the data, without losing the high resolution characteristics.
Less interesting and/or dominating features can be excluded in the spectral and spatial dimension. For
these reasons, more features can be distinguished and in greater detail. Analysts can zoom into a feature of
interest by increasing the resolution.
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