Validation of semiautomated and locally resolved aortic wall thickness measurements from computed tomography

Simultaneous Highly Efficient Contrast-Free Lumen and Vessel Wall MR Imaging for Anatomical Assessment of Aortic Disease

BACKGROUND

Bright-blood lumen and black-blood vessel wall imaging are required for the comprehensive assessment of aortic disease. These images are usually acquired separately, resulting in long examinations and potential misregistration between images.

PURPOSE

To characterize the performance of an accelerated and respiratory motion-compensated three-dimensional (3D) cardiac MRI technique for simultaneous contrast-free aortic lumen and vessel wall imaging with an interleaved T2 and inversion recovery prepared sequence (iT2Prep-BOOST).

STUDY TYPE

Prospective.

POPULATION

A total of 30 consecutive patients with aortopathy referred for a clinically indicated cardiac MRI examination (9 females, mean age ± standard deviation: 32 ± 12 years).

FIELD STRENGTH/SEQUENCE

1.5-T; bright-blood MR angiography (diaphragmatic navigator-gated T2-prepared 3D balanced steady-state free precession [bSSFP], T2Prep-bSSFP), breath-held black-blood two-dimensional (2D) half acquisition single-shot turbo spin echo (HASTE), and 3D bSSFP iT2Prep-BOOST.

ASSESSMENT

iT2Prep-BOOST bright-blood images were compared to T2prep-bSSFP images in terms of aortic vessel dimensions, lumen-to-myocardium contrast ratio (CR), and image quality (diagnostic confidence, vessel sharpness and presence of artifacts, assessed by three cardiologists on a 4-point scale, 1: nondiagnostic to 4: excellent). The iT2Prep-BOOST black-blood images were compared to 2D HASTE images for quantification of wall thickness. A visual comparison between computed tomography (CT) and iT2Prep-BOOST was performed in a patient with chronic aortic dissection.

STATISTICAL TESTS

Paired t-tests, Wilcoxon signed-rank tests, intraclass correlation coefficient (ICC), Bland-Altman analysis. A P value < 0.05 was considered statistically significant.

RESULTS

Bright-blood iT2Prep-BOOST resulted in significantly improved image quality (mean ± standard deviation 3.8 ± 0.5 vs. 3.3 ± 0.8) and CR (2.9 ± 0.8 vs. 1.8 ± 0.5) compared with T2Prep-bSSFP, with a shorter scan time (7.8 ± 1.7 minutes vs. 12.9 ± 3.4 minutes) while providing a complementary 3D black-blood image. Aortic lumen diameter and vessel wall thickness measurements in bright-blood and black-blood images were in good agreement with T2Prep-bSSFP and HASTE images (<0.02 cm and <0.005 cm bias, respectively) and good intrareader (ICC > 0.96) and interreader (ICC > 0.94) agreement was observed for all measurements.

DATA CONCLUSION

iT2Prep-BOOST might enable time-efficient simultaneous bright- and black-blood aortic imaging, with improved image quality compared to T2Prep-bSSFP and HASTE imaging, and comparable measurements for aortic wall and lumen dimensions.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Magnetic Resonance Imaging and Computed Tomography for the Noninvasive Assessment of Arterial Aging: A Review by the VascAgeNet COST Action

Magnetic resonance imaging and computed tomography allow the characterization of arterial state and function with high confidence and thus play a key role in the understanding of arterial aging and its translation into the clinic. Decades of research into the development of innovative imaging sequences and image analysis techniques have led to the identification of a large number of potential biomarkers, some bringing improvement in basic science, others in clinical practice. Nonetheless, the complexity of some of these biomarkers and the image analysis techniques required for their computation hamper their widespread use. In this narrative review, current biomarkers related to aging of the aorta, their founding principles, the sequence, and postprocessing required, and their predictive values for cardiovascular events are summarized. For each biomarker a summary of reference values and reproducibility studies and limitations is provided. The present review, developed in the COST Action VascAgeNet, aims to guide clinicians and technical researchers in the critical understanding of the possibilities offered by these advanced imaging modalities for studying the state and function of the aorta, and their possible clinically relevant relationships with aging.

A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSV-UNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively.

On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.

Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model

Scalar-valued failure metrics are commonly used to assess the risk of aortic aneurysm rupture and dissection, which occurs under hypertensive blood pressures brought on by extreme emotional or physical stress. To compute failure metrics under an elevated blood pressure, a classical patient-specific computer model consists of multiple computation steps involving inverse and forward analyses. These classical procedures may be impractical for time-sensitive clinical applications that require prompt feedback to clinicians. In this study, we developed a machine learning-based surrogate model to directly predict a probabilistic and anisotropic failure metric, namely failure probability (FP), on the aortic wall using aorta geometries at the systolic and diastolic phases. Ascending thoracic aortic aneurysm (ATAA) geometries of 60 patients were obtained from their CT scans, and biaxial mechanical testing data of ATAA tissues from 79 patients were collected. Finite element simulations were used to generate datasets for training, validation, and testing of the ML-surrogate model. The testing results demonstrated that the ML-surrogate can compute the maximum FP failure metric, with 0.42% normalized mean absolute error, in 1 s. To compare the performance of the ML-predicted probabilistic FP metric with other isotropic or deterministic metrics, a numerical case study was performed using synthetic "baseline" data. Our results showed that the probabilistic FP metric had more discriminative power than the deterministic Tsai-Hill metric, isotropic maximum principal stress, and aortic diameter criterion.

Towards a Clinical Implementation of Measuring the Elastic Modulus of the Aorta from Cardiac Computed Tomography Images

OBJECTIVE

The elasticity of the aortic wall varies depending on age, vessel location, and the presence of aortic diseases. Noninvasive measurement will be a powerful tool to understand the mechanical state of the aorta in a living human body. This study aimed to determine the elastic modulus of the aorta using computed tomography images.

METHODS

We constructed our original formulae based on mechanics of materials. Then, we performed computed tomography scans of a silicon rubber tube by applying four pressure conditions to the lumen. The segment elastic modulus was calculated from the scanned images using our formulae. The actual modulus was measured using a tensile loading test for comparison.

RESULTS

The segment moduli of elasticity from the images were 0.525 [0.524, 0.527], 0.524 [0.520, 0.524], 0.520 [0.515, 0.523], and 0.522 [0.516, 0.532] (unit: MPa, median [25%, 75% quantiles]) for the four pressure conditions, respectively. The corresponding measurements in the tensile test were 0.548 [0.539, 0.566], 0.535 [0.528, 0.553], 0.526 [0.513, 0.543], and 0.523 [0.508, 0.530], respectively. These results indicated errors of 4.2%, 2.1%, 1.1%, and 0.2%, respectively.

CONCLUSION

Our formulae provided good estimations of the segment elastic moduli of a silicon rubber tube under physiological pressure conditions using the computed tomography images.

SIGNIFICANCE

In addition to the elasticity, the formulae provide the strain energy as well. These properties can be better predictors of aortic diseases. The formulae consist of clinical parameters commonly used in medical settings (pressure, diameter, and wall thickness).

Aortic Thickness: A Forgotten Paradigm in Risk Stratification of Aortic Disease

BACKGROUND

 This study aimed at risk-stratifying aortic dilatation using aortic wall thickness (AWT) and comparing methods of AWT assessment.

METHODS

 Demographic, epidemiological, and perioperative data on 72 consecutive aortic surgeries (age = 62 years[standard deviation (SD) = 12] years) performed by a single surgeon were collected from hospital database. Aortic thickness was measured on computed tomography scans, as well as intraoperatively in four quadrants, at the level of aortic sinuses, as well as midascending aorta, using calipers. Aortic wall stress was calculated using standard mathematical formulae.

RESULTS

 The ascending aorta was 48.2 (SD = 8) mm and the mean thickness at ascending aorta level was 1.9 (SD = 0.3) mm. There was congruence between imaging and intraoperative measurements of thickness, as well as between the radiologist and surgeon. Preoperatively, 16 patients had multiple imaging studies showing an average rate of growth of 1.2 mm per year without significant difference in thickness. The wider the aorta, the thinner was the lateral or convex wall. Aortic stenosis (p = 0.01), lateral to medial wall thickness ratio (p = 0.04), and history of hypertension (p = 0.00), all had protective effect on aortic root stress. The ascending aortic stress was directly affected by age (p = 0.03) and inversely related to lateral to medial wall thickness ratio (p = 0.03).

CONCLUSION

 Aortic thickness can be measured preoperatively and easily confirmed intraoperatively. Risk stratification based on both aortic thickness and diameter (stress calculations) would better predict acute aortic events in dilated aortas and define aortic resection criteria more objectively.

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans

Accurate identification of in vivo nonlinear, anisotropic mechanical properties of the aortic wall of individual patients remains to be one of the critical challenges in the field of cardiovascular biomechanics. Since only the physiologically loaded states of the aorta are given from in vivo clinical images, inverse approaches, which take into account of the unloaded configuration, are needed for in vivo material parameter identification. Existing inverse methods are computationally expensive, which take days to weeks to complete for a single patient, inhibiting fast feedback for clinicians. Moreover, the current inverse methods have only been evaluated using synthetic data. In this study, we improved our recently developed multi-resolution direct search (MRDS) approach and the computation time cost was reduced to 1~2 hours. Using the improved MRDS approach, we estimated in vivo aortic tissue elastic properties of two ascending thoracic aortic aneurysm (ATAA) patients from pre-operative gated CT scans. For comparison, corresponding surgically-resected aortic wall tissue samples were obtained and subjected to planar biaxial tests. Relatively close matches were achieved for the in vivo-identified and ex vivo-fitted stress-stretch responses. It is hoped that further development of this inverse approach can enable an accurate identification of the in vivo material parameters from in vivo image data.

3D segmentation of exterior wall surface of abdominal aortic aneurysm from CT images using variable neighborhood search

A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57 ± 4.52%, 72.47 ± 8.11%, 58.50 ± 8.86% and 76.21 ± 10.49%, respectively.

On the effect of computed tomography resolution to distinguish between abdominal aortic aneurysm wall tissue and calcification: A proof of concept

PURPOSE

The purpose of this study is to determine the optimal target CT spatial resolution for accurately imaging abdominal aortic aneurysm (AAA) wall characteristics, distinguishing between tissue and calcification components, for an accurate assessment of rupture risk.

MATERIALS AND METHODS

Ruptured and non-ruptured AAA-wall samples were acquired from eight patients undergoing open surgical aneurysm repair upon institutional review board approval and informed consent was obtained from all patients. Physical measurements of AAA-wall cross-section were made using scanning electron microscopy. Samples were scanned using high resolution micro-CT scanning. A resolution range of 15.5-155μm was used to quantify the influence of decreasing resolution on wall area measurements, in terms of tissue and calcification. A statistical comparison between the reference resolution (15.5μm) and multi-detector CT resolution (744μm) was also made.

RESULTS

Electron microscopy examination of ruptured AAAs revealed extremely thin outer tissue structure <200μm in radial distribution which is supporting the aneurysm wall along with large areas of adjacent medial calcifications far greater in area than the tissue layer. The spatial resolution of 155μm is a significant predictor of the reference AAA-wall tissue and calcification area measurements (r=0.850; p<0.001; r=0.999; p<0.001 respectively). The tissue and calcification area at 155μm is correct within 8.8%±1.86 and 26.13%±9.40 respectively with sensitivity of 87.17% when compared to the reference.

CONCLUSION

The inclusion of AAA-wall measurements, through the use of high resolution-CT will elucidate the variations in AAA-wall tissue and calcification distributions across the wall which may help to leverage an improved assessment of AAA rupture risk.

BioPARR: A software system for estimating the rupture potential index for abdominal aortic aneurysms

An abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that, if left untreated, can expand until rupture. Despite ongoing efforts, an efficient tool for accurate estimation of AAA rupture risk is still not available. Furthermore, a lack of standardisation across current approaches and specific obstacles within computational workflows limit the translation of existing methods to the clinic. This paper presents BioPARR (Biomechanics based Prediction of Aneurysm Rupture Risk), a software system to facilitate the analysis of AAA using a finite element analysis based approach. Except semi-automatic segmentation of the AAA and intraluminal thrombus (ILT) from medical images, the entire analysis is performed automatically. The system is modular and easily expandable, allows the extraction of information from images of different modalities (e.g. CT and MRI) and the simulation of different modelling scenarios (e.g. with/without thrombus). The software uses contemporary methods that eliminate the need for patient-specific material properties, overcoming perhaps the key limitation to all previous patient-specific analysis methods. The software system is robust, free, and will allow researchers to perform comparative evaluation of AAA using a standardised approach. We report preliminary data from 48 cases.

A new inverse method for estimation of in vivo mechanical properties of the aortic wall

The aortic wall is always loaded in vivo, which makes it challenging to estimate the material parameters of its nonlinear, anisotropic constitutive equation from in vivo image data. Previous approaches largely relied on either computationally expensive finite element models or simplifications of the geometry or material models. In this study, we investigated a new inverse method based on aortic wall stress computation. This approach consists of the following two steps: (1) computing an "almost true" stress field from the in vivo geometries and loading conditions, (2) building an objective function based on the "almost true" stress fields, constitutive equations and deformation relations, and estimating the material parameters by minimizing the objective function. The method was validated through numerical experiments by using the in vivo data from four ascending aortic aneurysm (AsAA) patients. The results demonstrated that the method is computationally efficient. This novel approach may facilitate the personalized biomechanical analysis of aortic tissues in clinical applications, such as in the rupture risk analysis of ascending aortic aneurysms.

Angiotensin II-induced TLR4 mediated abdominal aortic aneurysm in apolipoprotein E knockout mice is dependent on STAT3

Abdominal Aortic Aneurysm (AAA) is a major cause of mortality and morbidity in men over 65 years of age. Male apolipoprotein E knockout (ApoE(-/-)) mice infused with angiotensin II (AngII) develop AAA. Although AngII stimulates both JAK/STAT and Toll-like receptor 4 (TLR4) signaling pathways, their involvement in AngII mediated AAA formation is unclear. Here we used the small molecule STAT3 inhibitor, S3I-201, the TLR4 inhibitor Eritoran and ApoE(-/-)TLR4(-/-) mice to evaluate the interaction between STAT3 and TLR4 signaling in AngII-induced AAA formation. ApoE(-/-) mice infused for 28 days with AngII developed AAAs and increased STAT3 activation and TLR4 expression. Moreover, AngII increased macrophage infiltration and the ratio of M1 (pro-inflammatory)/M2 (healing) macrophages in aneurysmal tissue as early as 7-10 days after AngII infusion. STAT3 inhibition with S3I-201 decreased the incidence and severity of AngII-induced AAA formation and decreased MMP activity and the ratio of M1/M2 macrophages. Furthermore, AngII-mediated AAA formation, MMP secretion, STAT3 phosphorylation and the ratio of M1/M2 macrophages were markedly decreased in ApoE(-/-)TLR4(-/-) mice, and in Eritoran-treated ApoE(-/-) mice. TLR4 and pSTAT3 levels were also increased in human aneurysmal tissue. These data support a role of pSTAT3 in TLR4 dependent AAA formation and possible therapeutic roles for TLR4 and/or STAT3 inhibition in AAA.