Characterizing thrombus with multiple red blood cell compositions by optical coherence tomography attenuation coefficient

QOCT-Net: A Physics-Informed Neural Network for Intravascular Optical Coherence Tomography Attenuation Imaging

Intravascular optical coherence tomography (IVOCT) provides high-resolution, depth-resolved images of coronary arterial microstructure by acquiring backscattered light. Quantitative attenuation imaging is important for accurate characterization of tissue components and identification of vulnerable plaques. In this work, we proposed a deep learning method for IVOCT attenuation imaging based on the multiple scattering model of light transport. A physics-informed deep network named Quantitative OCT Network (QOCT-Net) was designed to recover pixel-level optical attenuation coefficients directly from standard IVOCT B-scan images. The network was trained and tested on simulation and in vivo datasets. Results showed superior attenuation coefficient estimates both visually and based on quantitative image metrics. The structural similarity, energy error depth and peak signal-to-noise ratio are improved by at least 7%, 5% and 12.4%, respectively, compared with the state-of-the-art non-learning methods. This method potentially enables high-precision quantitative imaging for tissue characterization and vulnerable plaque identification.

MicroCT Can Characterize Clots Retrieved With Mechanical Thrombectomy From Acute Ischemic Stroke Patients–A Preliminary Report

Background

Characterization of the clot occluding the arteries in acute ischemic stroke received ample attention, in terms of elucidating the relationship between the clot composition, its etiology and its amenability for pharmacological treatment and mechanical thrombectomy approaches. Traditional analytical techniques such as conventional 2D histopathology or electron microscopy sample only small parts of the clot. Visualization and analysis in 3D are necessary to depict and comprehend the overall organization of the clot. The aim of this study is to investigate the potential of microCT for characterizing the clot composition, structure, and organization.

Methods

In a pilot study, we analyzed with microCT clots retrieved from 14 patients with acute ischemic stroke. The following parameters were analyzed: overall clot density, clot segmentation with various density thresholds, clot volume.

Results

Our findings show that human clots are heterogeneous in terms of CT intra-clot density distribution. After fixation in formalin, the clots display a shift toward negative values. On average, we found the mean HU values of red clots retrieved from patients to be −153 HU, with SD = 23.8 HU, for the intermediate clots retrieved from patients −193 HU, SD = 23.7 HU, and for the white clots retrieved from patients −229 HU, SD = 64.8 HU.

Conclusion

Our study shows that volumetric and density analysis of the clot opens new perspectives for clot characterization and for a better understanding of thrombus structure and composition.

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm

OBJECTIVE

To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).

METHODS

A total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results.

RESULTS

Attenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively.

CONCLUSION

It is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.

Studying Stroke Thrombus Composition After Thrombectomy: What Can We Learn?

The composition of ischemic stroke thrombi has gained an increasing amount of interest in recent years. The implementation of endovascular procedures in standard stroke care has granted researchers the unique opportunity to examine patient thrombus material. Increasing evidence indicates that stroke thrombi are complex and heterogenous, consisting of various biochemical (eg, fibrin, von Willebrand Factor, and neutrophil extracellular traps) and cellular (eg, red blood cells, platelets, leukocytes, and bacteria) components. This complex composition may explain therapeutic limitations and also offer novel insights in several aspects of stroke management. Better understanding of thrombus characteristics could, therefore, potentially lead to improvements in the management of patients with stroke. In this review, we provide a comprehensive overview of the lessons learned by examining stroke thrombus composition after endovascular thrombectomy and its potential relevance for thrombectomy success rates, thrombolysis, clinical outcomes, stroke etiology, and radiological imaging.

Novel Human Acute Ischemic Stroke Blood Clot Analogs for In Vitro Thrombectomy Testing

BACKGROUND AND PURPOSE

Previous studies have successfully created blood clot analogs for in vitro endovascular device testing using animal blood of various species. Blood components vary greatly among species; therefore, creating clot analogs from human blood is likely a more accurate representation of thrombi formed in the human vasculature.

MATERIALS AND METHODS

Following approval from the Mayo Clinic institutional review board, human whole-blood and platelet donations were obtained from the blood transfusion service. Twelve clot analogs were created by combining different ratios of red blood cells + buffy coat, plasma, and platelets. Thrombin and calcium chloride were added to stimulate coagulation. Clot composition was assessed using histologic and immunohistochemical staining. To assess the similarities of mechanical properties to patient clots, 3 types of clot analogs (soft, elastic, and stiff) were selected for in vitro thrombectomy testing.

RESULTS

The range of histopathologic compositions produced is representative of clots removed during thrombectomy procedures. The red blood cell composition ranged from 8.9% to 91.4%, and fibrin composition ranged from 3.1% to 53.4%. Platelets (CD42b) and von Willebrand Factor ranged from 0.5% to 47.1% and 1.0% to 63.4%, respectively. The soft clots had the highest first-pass effect and successful revascularization rates followed by the elastic and stiff clots. Distal embolization events were observed when clot ingestion could not be achieved, requiring device pullback. The incidence rate of distal embolization was the highest for the stiff clots due to the weak clot/device integration.

CONCLUSIONS

Red blood cell-rich, fibrin-rich, and platelet-rich clot analogs that mimic clots retrieved from patients with acute ischemic stroke were created in vitro. Differing retrieval outcomes were confirmed using in vitro thrombectomy testing in a subset of clots.