Quantitative 3D Analysis of Coronary Wall Morphology in Heart Transplant Patients: OCT-Assessed Cardiac Allograft Vasculopathy Progression

The accuracy of detailed analysis of optical coherence tomography in detection of plaque lipid content: dual-imaging study with optical coherence tomography and near-infrared spectroscopy

BACKGROUND

Lipid-rich plaque covered by a thin fibrous cap (FC) has been identified as a frequent morphological substrate for the development of acute coronary syndrome. Optical coherence tomography (OCT) permits the identification and measurement of the FC. Near-infrared spectroscopy (NIRS) has been approved for detection of coronary lipids.

AIMS

We aimed to assess the ability of detailed OCT analysis to identify coronary lipids, using NIRS as the reference method.

METHODS

In total, 40 patients with acute coronary syndrome underwent imaging of a non-culprit lesion by both NIRS and OCT. For each segment, the NIRS-derived 4 mm segment with maximal lipid core burden index (maxLCBI4mm) was assessed. OCT analysis was performed using a semi-automated method including measurement of the fibrous cap thickness (FCT) of all detected fibroatheromas. Subsequent quantitative volumetric evaluation furnished FCT, FC surface area (FC SA), lipid arc, and FC (fibrous cap) volume data. OCT features of lipid plaques were compared with maxLCBI4mm. Predictors of maxLCBI4mm >400 was assessed by using univariable and multivariable analysis.

RESULTS

OCT features (mean FCT, total FC SA, FC volume, maximal, mean, and total lipid arcs) strongly correlated with the maxLCBI4mm (p = 0.012 for the mean FCT, respectively p < 0.001 for all other aforementioned features). The strongest predictors of maxLCBI4mm >400 were the maximal (p = 0.002) and mean (p = 0.002) lipid arc, and total FC SA (p = 0.012).

CONCLUSIONS

We found a strong correlation between the OCT-derived features and NIRS findings. Detailed OCT analysis may be reliably used for detection of the presence of coronary lipids.

Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses

Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.

Automatic lumen and anatomical layers segmentation in IVOCT images using meta learning

Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning-based methods usually require well-annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta-learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi-level gradient strategy to train a meta-learner for capturing the shared meta-knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw-type network and a contrast consistency loss were designed to better learn the meta-knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state-of-art performance.

Validation of a semi-automatic software for optical coherence tomography - analysis in heart transplanted patients

Optical Coherence Tomography (OCT) is an intravascular imaging modality enabling detailed evaluation of cardiac allograft vasculopathy (CAV) after heart transplantation (HTx). However, its clinical application remains hampered by time-consuming manual quantitative analysis. We aimed to validate a semi-automated quantitative OCT analysis software (Iowa Coronary Wall Analyzer, ICWA-OCT) to improve OCT-analysis in HTx patients. 23 patients underwent OCT evaluation of all three major coronary arteries at 3 months (3M) and 12 months (12M) after HTx. We analyzed OCT recordings using the semiautomatic software and compared results with measurements from a validated manual software. For semi-automated analysis, 31,228 frames from 114 vessels were available. The validation was based on a subset of 4287 matched frames. We applied mixed model statistics to accommodate the multilevel data structure with method as a fixed effect. Lumen (minimum, mean, maximum) and media (mean, maximum) metrics showed no significant differences. Mean and maximum intima area were underestimated by the semi-automated method (β-methodmean = - 0.289 mm2, p < 0.01; β-methodmax = - 0.695 mm2, p < 0.01). Bland-Altman analyses showed increasing semi-automatic underestimation of intima measurements with increasing intimal extent. Comparing 3M to 12M progression between methods, mean intimal area showed minor underestimation (β-methodmean = - 1.03 mm2, p = 0.04). Lumen and media metrics showed excellent agreement between the manual and semi-automated method. Intima metrics and progressions from 3M to 12M were slightly underestimated by the semi-automated OCT software with unknown clinical relevance. The semi-automated software has the future potential to provide robust and time-saving evaluation of CAV progression.

Role of genetics in the development of cardiac allograft vasculopathy

BACKGROUND

The association between genetic polymorphisms and early cardiac allograft vasculopathy (CAV) development is relatively unexplored. Identification of genes involved in the CAV process may offer new insights into pathophysiology and lead to a wider range of therapeutic options.

METHODS

This prospective study of 109 patients investigated 44 single nucleotide polymorphisms (SNPs) within the susceptibility loci potentially related to coronary artery disease, carotid artery intima-media thickness (cIMT), and in nitric oxide synthase gene. Genotyping was done by the Fluidigm SNP Type assays and Fluidigm 48.48 Dynamic Array IFC. The intima thickness progression (IT) was evaluated by coronary optical coherence tomography performed 1 month and 12 months after heart transplantation (HTx).

RESULTS

During the first post-HTx year, the mean intima thickness (IT) increased by 24.0 ± 34.2 µm (p < 0.001) and lumen area decreased by ‒0.9 ± 1.8 mm2 (p < 0.001). The rs1570360 (A/G) SNP of the vascular endothelial growth factor A (VEGFA) gene showed the strongest association with intima thickness progression, even in the presence of the traditional CAV risk factors. SNPs previously related to carotid artery intima-media thickness rs11785239 (PRAG1), rs6584389 (PAX2), rs13225723 (LINC02577) and rs17477177 (CCDC71L), were among the five most significantly associated with IT progression but lost their significance once traditional CAV risk factors had been added.

CONCLUSION

Results of this study suggest that genetic variability may play an important role in CAV development. The vascular endothelial growth factor A gene SNP rs1570360 showed the strongest association with intima thickness (IT) progression measured by OCT, even in the presence of the traditional CAV risk factors (Tab. 3, Fig. 3, Ref. 36). Text in PDF www.elis.sk Keywords: cardiac allograft vasculopathy, optical coherence tomography, vascular endothelial growth factor A, intimal thickening, genetic polymorphism.

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

Introduction

Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.

Methods

We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.

Results

Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.

Conclusion

Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

Fibrous Cap Thickness Predicts Stable Coronary Plaque Progression: Early Clinical Validation of a Semiautomated OCT Technology

BACKGROUND

Imaging-based characteristics associated with the progression of stable coronary atherosclerotic lesions are poorly defined. Utilizing a combination of optical coherence tomography (OCT) and intravascular ultrasound (IVUS) imaging, we aimed to characterize the lesions prone to progression through clinical validation of a semiautomated OCT computational program.

METHODS

Patients with stable coronary artery disease underwent nonculprit vessel imaging with IVUS and OCT at baseline and IVUS at the 12-month follow-up. After coregistration of baseline and follow-up IVUS images, paired 5-mm segments from each patient were identified, demonstrating the greatest plaque progression and regression as measured by the change in plaque burden. Experienced readers identified plaque features on corresponding baseline OCT segments, and predictors of plaque progression were assessed by multivariable analysis. Each segment then underwent volumetric assessment of the fibrous cap (FC) using proprietary software.

RESULTS

Among 23 patients (70% men; median age, 67 years), experienced-reader analysis demonstrated that for every 100 μm increase in mean FC thickness, plaques were 87% less likely to progress (P = .01), which persisted on multivariable analysis controlling for baseline plaque burden (P = .05). Automated FC analysis (n = 17 paired segments) confirmed this finding (P = .01) and found thinner minimal FC thickness (P = .01) and larger FC surface area of <65 μm (P = .02) and <100 μm (P = .04) in progressing segments than in regressing segments. No additional imaging features predicted plaque progression.

CONCLUSIONS

A semiautomated FC analysis tool confirmed the significant association between thinner FC and stable coronary plaque progression along entire vessel segments, illustrating the diffuse nature of FC thinning and suggesting a future clinical role in predicting the progression of stable coronary artery disease.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

Structure attention co-training neural network for neovascularization segmentation in intravascular optical coherence tomography

PURPOSE

To development and validate a Neovascularization (NV) segmentation model in intravascular optical coherence tomography (IVOCT) through deep learning methods.

METHODS AND MATERIALS

A total of 1950 2D slices of 70 IVOCT pullbacks were used in our study. We randomly selected 1273 2D slices from 44 patients as the training set, 379 2D slices from 11 patients as the validation set, and 298 2D slices from the last 15 patients as the testing set. Automatic NV segmentation is quite challenging, as it must address issues of speckle noise, shadow artifacts, high distribution variation, etc. To meet these challenges, a new deep learning-based segmentation method is developed based on a co-training architecture with an integrated structural attention mechanism. Co-training is developed to exploit the features of three consecutive slices. The structural attention mechanism comprises spatial and channel attention modules and is integrated into the co-training architecture at each up-sampling step. A cascaded fixed network is further incorporated to achieve segmentation at the image level in a coarse-to-fine manner.

RESULTS

Extensive experiments were performed involving a comparison with several state-of-the-art deep learning-based segmentation methods. Moreover, the consistency of the results with those of manual segmentation was also investigated. Our proposed NV automatic segmentation method achieved the highest correlation with the manual delineation by interventional cardiologists (the Pearson correlation coefficient is 0.825).

CONCLUSION

In this work, we proposed a co-training architecture with an integrated structural attention mechanism to segment NV in IVOCT images. The good agreement between our segmentation results and manual segmentation indicates that the proposed method has great potential for application in the clinical investigation of NV-related plaque diagnosis and treatment. This article is protected by copyright. All rights reserved.

Optical Coherence Tomography in Cardiac Allograft Vasculopathy: State-of-the-Art Review

Cardiac allograft vasculopathy (CAV) is a challenging complication of heart transplantation. CAV pathophysiology is incompletely understood, standard screening modalities such as angiography have significant limitations, and currently available therapies have only modest efficacy in preventing progression. Optical coherence tomography is a light-based technique that provides microscopic level catheter-based intravascular imaging and has dramatically expanded our understanding of CAV, demonstrating it to be a complex, heterogeneous, and dynamic process. This review covers characteristics and uses of optical coherence tomography, including vessel characterization, serial use to assess progression of disease, guiding percutaneous intervention, and monitoring response to CAV therapies. We also discuss the potential of optical coherence tomography in providing individualized assessment and enable customized CAV therapies, which may lead to improvements in long-term transplant outcomes.

Optical coherence tomography and multiphoton microscopy offer new options for the quantification of fibrotic aortic valve disease in ApoE-/- mice

Aortic valve sclerosis is characterized as the thickening of the aortic valve without obstruction of the left ventricular outflow. It has a prevalence of 30% in people over 65 years old. Aortic valve sclerosis represents a cardiovascular risk marker because it may progress to moderate or severe aortic valve stenosis. Thus, the early recognition and management of aortic valve sclerosis are of cardinal importance. We examined the aortic valve geometry and structure from healthy C57Bl6 wild type and age-matched hyperlipidemic ApoE-/- mice with aortic valve sclerosis using optical coherence tomography (OCT) and multiphoton microscopy (MPM) and compared results with histological analyses. Early fibrotic thickening, especially in the tip region of the native aortic valve leaflets from the ApoE-/- mice, was detectable in a precise spatial resolution using OCT. Evaluation of the second harmonic generation signal using MPM demonstrated that collagen content decreased in all aortic valve leaflet regions in the ApoE-/- mice. Lipid droplets and cholesterol crystals were detected using coherent anti-Stokes Raman scattering in the tissue from the ApoE-/- mice. Here, we demonstrated that OCT and MPM, which are fast and precise contactless imaging approaches, are suitable for defining early morphological and structural alterations of sclerotic murine aortic valves.

Donor specific anti-HLA antibodies and cardiac allograft vasculopathy: A prospective study using highly automated 3-D optical coherence tomography analysis

INTRODUCTION

Recent studies suggested potential positive correlations between HLA-specific antibodies and development of cardiac allograft vasculopathy (CAV).

METHODS

This prospective two-center study investigated early progression of CAV by coronary optical coherence tomography in 1 month and 12 months after heart transplantation (HTx) in 104 patients. Detection and characterization of donor specific (DSA) and MHC class-I polypeptide-related sequence A (MICA) antibodies were performed before, 1, 6 and 12 months after transplantation.

RESULTS

During the first post-HTx year, we observed a significant reduction in the mean coronary luminal area (P < .001), and progression in mean intimal thickness (IT) (P < .001). DSA and anti-MICA occurred in 17% of all patients, but no significant relationship was observed between presence of DSA/anti-MICA and IT progression within 12 months after HTx. In contrast, we observed significant association between presence of DSA (p=0.031), de-novo DSA (p=0.031), HLA Class II DSA (p=0.017) and media thickness (MT) progression.

CONCLUSION

Results of our study did not identify a direct association between presence of DSA/anti-MICA and intimal thickness progression in an early period after HTx. However, we found significant relationships between DSA and media thickness progression that may identify a newly recognized immune-pathological aspect of CAV.

Effects of implanting a long-term left ventricle assist device on post-transplant outcomes

BACKGROUND

An increasing number of patients are receiving left ventricle assist devices as a bridge to heart transplantation. The aim of this study was to determine the difference between patients who received transplants from a left ventricle assist device and those who underwent heart transplantation without a prior left ventricle assist device implantation.

MATERIAL AND METHODS

The study included patients who underwent heart transplantation in our institute between January 2010 and November 2018. The following clinical variables were evaluated: donor characteristics, patient's pre-transplant demographical data, post-transplant data, and patient survival. Cardiac allograft vasculopathy progression was prospectively examined (after 1 month and 12 months after heart transplantation) by coronary optical coherence tomography. We were interested in the difference in 1- and 5-year survival between the left ventricle assist device and non-left ventricle assist device groups.

RESULTS

A total of 248 patients were identified; out of them, 48 patients received a left ventricle assist device before heart transplantation, whereas 200 had transplants with no prior left ventricle assist device implantation. There were no significant differences in any donor characteristics. The mean duration of cardiopulmonary bypass time in the non-left ventricle assist device group was 156 versus 175 min in the left ventricle assist device group (p = 0.009), blood loss was 650 versus 1045 mL (p < 0.001), the need to implant an extracorporeal membrane oxygenation was 10% versus 23% (p = 0.02). There was no difference in cardiac allograft vasculopathy progression between the groups 1 year after heart transplantation (p = 0.528). The 1- and 5-year survival, according to Kaplan-Meier, was 80% and 70% in the left ventricle assist device group, compared to 80% and 73%, respectively, in the non-left ventricle assist device group (Log-rank test: p = 0.945).

CONCLUSION

Our results indicate that patients undergoing heart transplantation from left ventricle assist devices suffer significantly more from intraoperative and post-operative complications; however, only insignificant cardiac allograft vasculopathy progression and survival differences between the two groups were observed.

Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging

Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR ≥ 40 dB for images with 8 BPC, for example) and maximum (PSNR = ∞ dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.

Heart rate and early progression of cardiac allograft vasculopathy: A prospective study using highly automated 3-D optical coherence tomography analysis

INTRODUCTION

Heart rate slowing agents are frequently prescribed to manage heart transplant (HTx) patients with the assumption that higher heart rate is a risk factor in cardiovascular disease.

PATIENTS AND METHODS

This prospective two-center study investigated early progression of cardiac allograft vasculopathy (CAV) in 116 HTx patients. Examinations by coronary optical coherence tomography and 24-hour ambulatory ECG monitoring were performed both at baseline (1 month after HTx) and during follow-up (12 months after HTx).

RESULTS

During the first post-HTx year, we observed a significant reduction in the mean coronary luminal area from 9.0 ± 2.5 to 8.0 ± 2.4 mm² (P < .001), and progression in mean intimal thickness (IT) from 106.5 ± 40.4 to 130.1 ± 53.0 µm (P < .001). No significant relationship was observed between baseline and follow-up mean heart rates and IT progression (R = .02, P = .83; R = -.13, P = .18). We found a mild inverse association between beta-blocker dosage at 12 months and IT progression (R = -.20, P = .035).

CONCLUSION

Our study did not confirm a direct association between mean heart rate and progression of CAV. The role of beta blockers warrants further investigation, with our results indicating that they may play a protective role in early CAV development.