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Representative images in a patient with a 4.7-cm aneurysm in the ascending aorta who demonstrated stability of the ascending aorta over three surveillance intervals totaling 6 years by vascular deformation mapping assessment. There was no growth of the ascending aorta according to three-dimensional assessment across all surveillance intervals; however, a small focal region of growth was detected at the distal descending level in interval 2 (arrowhead).
Source publication
Background
Aortic diameter measurements in patients with a thoracic aortic aneurysm (TAA) show wide variation. There is no technique to quantify aortic growth in a three-dimensional (3D) manner.
Purpose
To validate a CT-based technique for quantification of 3D growth based on deformable registration in patients with TAA.
Materials and Methods
Pat...
Contexts in source publication
Context 1
... of these six areas of submaximal growth were detected with clinical diameter measurements. Furthermore, changes in 3D aortic growth during imaging surveillance were clearly visualized with VDM (Figs 4, 5). Among the 14 patients who had more than one surveillance interval, 11 of 14 (78%) had stable aortic dimensions with VDM at all surveillance intervals (Fig 4) Representative images in a patient with a 4.7-cm aneurysm in the ascending aorta who demonstrated stability of the ascending aorta over three surveillance intervals totaling 6 years by vascular deformation mapping assessment. ...
Context 2
... changes in 3D aortic growth during imaging surveillance were clearly visualized with VDM (Figs 4, 5). Among the 14 patients who had more than one surveillance interval, 11 of 14 (78%) had stable aortic dimensions with VDM at all surveillance intervals (Fig 4) Representative images in a patient with a 4.7-cm aneurysm in the ascending aorta who demonstrated stability of the ascending aorta over three surveillance intervals totaling 6 years by vascular deformation mapping assessment. There was no growth of the ascending aorta according to three-dimensional assessment across all surveillance intervals; however, a small focal region of growth was detected at the distal descending level in interval 2 (arrowhead). ...
Context 3
... of these six areas of submaximal growth were detected with clinical diameter measurements. Furthermore, changes in 3D aortic growth during imaging surveillance were clearly visualized with VDM (Figs 4, 5). Among the 14 patients who had more than one surveillance interval, 11 of 14 (78%) had stable aortic dimensions with VDM at all surveillance intervals (Fig 4) Representative images in a patient with a 4.7-cm aneurysm in the ascending aorta who demonstrated stability of the ascending aorta over three surveillance intervals totaling 6 years by vascular deformation mapping assessment. ...
Context 4
... changes in 3D aortic growth during imaging surveillance were clearly visualized with VDM (Figs 4, 5). Among the 14 patients who had more than one surveillance interval, 11 of 14 (78%) had stable aortic dimensions with VDM at all surveillance intervals (Fig 4) Representative images in a patient with a 4.7-cm aneurysm in the ascending aorta who demonstrated stability of the ascending aorta over three surveillance intervals totaling 6 years by vascular deformation mapping assessment. There was no growth of the ascending aorta according to three-dimensional assessment across all surveillance intervals; however, a small focal region of growth was detected at the distal descending level in interval 2 (arrowhead). ...
Citations
... Incompressibility is a crucial characteristic for image registration in moving biological tissues, such as myocardium muscles, the tongue, and the brain. The Jacobian determinant of a deformation, representing the ratio of volume change, is frequently utilized to quantify growth or shrinkage in biological tissue [10,11,30]. We adopt the determinant-based penalty proposed by DRIMET [9] L inc = x |log max (|J ϕ (x)| , ϵ)| − x min (|J ϕ (x)| , 0), where |J ϕ (x)| is the Jacobian determinant of ϕ at x. ...
Tagged magnetic resonance imaging (tMRI) has been employed for decades to measure the motion of tissue undergoing deformation. However, registration-based motion estimation from tMRI is difficult due to the periodic patterns in these images, particularly when the motion is large. With a larger motion the registration approach gets trapped in a local optima, leading to motion estimation errors. We introduce a novel "momenta, shooting, and correction" framework for Lagrangian motion estimation in the presence of repetitive patterns and large motion. This framework, grounded in Lie algebra and Lie group principles, accumulates momenta in the tangent vector space and employs exponential mapping in the diffeomorphic space for rapid approximation towards true optima, circumventing local optima. A subsequent correction step ensures convergence to true optima. The results on a 2D synthetic dataset and a real 3D tMRI dataset demonstrate our method's efficiency in estimating accurate, dense, and diffeomorphic 2D/3D motion fields amidst large motion and repetitive patterns.
... Vascular deformation mapping (VDM) is a validated medical image registration technique which allows for comprehensive assessment of the degree and extent of growth mapping of the aorta (VDM(G): VDM growth) [10][11][12] using longitudinal CTA data acquired at two different points during clinical surveillance. However, when applied to dynamic CTA data (i.e., time-resolved CTA), VDM allows for 3D assessment of the aortic deformation throughout the cardiac cycle (VDM(D): VDM dynamic). ...
... VDM employs b-spline deformable image registration techniques to quantify the 3D deformation of the aortic wall surface between two CTA images of a given subject. This approach has been previously applied to assess 3D aortic growth based on CTA images acquired at two time points spanning several years and has been validated in expert-rater and in silico phantom studies [10][11][12]. We refer to this growth assessment technique as VDM(G). ...
... Next, rigid and deformable registrations were conducted to align the two segmentations. Registration accuracy was confirmed using a dual-channel plotting technique to assure alignment of the fixed diastolic and warped systolic configurations, as previously described [10]. Lastly, the 3D displacement field resulting from the deformable registration was used to perform a vertex-wise deformation of a triangulated mesh based on the diastolic configuration. ...
The aorta is in constant motion due to the combination of cyclic loading and unloading with its mechanical coupling to the contractile left ventricle (LV) myocardium. This aortic root motion has been proposed as a marker for aortic disease progression. Aortic root motion extraction techniques have been mostly based on 2D image analysis and have thus lacked a rigorous description of the different components of aortic root motion (e.g., axial versus in-plane). In this study, we utilized a novel technique termed vascular deformation mapping (VDM(D)) to extract 3D aortic root motion from dynamic computed tomography angiography images. Aortic root displacement (axial and in-plane), area ratio and distensibility, axial tilt, aortic rotation, and LV/Ao angles were extracted and compared for four different subject groups: non-aneurysmal, TAA, Marfan, and repair. The repair group showed smaller aortic root displacement, aortic rotation, and distensibility than the other groups. The repair group was also the only group that showed a larger relative in-plane displacement than relative axial displacement. The Marfan group showed the largest heterogeneity in aortic root displacement, distensibility, and age. The non-aneurysmal group showed a negative correlation between age and distensibility, consistent with previous studies. Our results revealed a strong positive correlation between LV/Ao angle and relative axial displacement and a strong negative correlation between LV/Ao angle and relative in-plane displacement. VDM(D)-derived 3D aortic root motion can be used in future studies to define improved boundary conditions for aortic wall stress analysis.
... The natural history of patients with uTBAD and individual outcomes are more protracted and are characterized either by the occurrence of late acute events such as aortic rupture or-most commonly-by gradual FL degeneration and aneurysm formation (3,6,12,18). Elective surgical repair is required once a threshold of 6 cm of maximum diameter has been reached (3,4,12,(18)(19)(20). A plausible study end point therefore has to be an aggregate of both clinical parameters (such as acute late events) and morphologic parameters derived from imaging, such as maximum aortic diameter and aortic growth. ...
Purpose:
To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD).
Materials and methods:
The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique.
Results:
The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling.
Conclusion:
This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue.
... This approach employs deformable image registration (DIR) to quantify three-dimensional changes in the aortic wall morphology using high-resolution volumetric computed tomography angiography (CTA) data. Preliminary reports in a clinical popluation of patients with TAA have shown that the VDM technique may be useful for more complete depiction of the extent of aortic growth to inform surgical planning and for the assessment of growth during imaging surveillance 4,5 . However, the VDM approach and key algorithms have not yet been validated in a manner that supports the improved accuracy of VDM-derived measurements compared to standard diameter assessments. ...
Purpose:
Accurate assessment of thoracic aortic aneurysm (TAA) growth is important for appropriate clinical management. Maximal aortic diameter is the primary metric that is used to assess growth, but it suffers from substantial measurement variability. A recently proposed technique, termed Vascular Deformation Mapping (VDM), is able to quantify three-dimensional aortic growth using clinical computed tomography angiography (CTA) data using an approach based on deformable image registration (DIR). However, the accuracy and robustness of VDM remains undefined given the lack of a ground truth from clinical CTA data, and furthermore the performance of VDM relative to standard manual diameter measurements is unknown.
Methods:
To evaluate the performance of the VDM pipeline for quantifying aortic growth we developed a novel and systematic evaluation process to generate 76 unique synthetic CTA growth phantoms (based on 10 unique cases) with variable degrees and locations of aortic wall deformation. Aortic deformation was quantified using two metrics: Area Ratio (AR), defined as the ratio of surface area in triangular mesh elements, and the magnitude of deformation in the normal direction (DiN) relative to the aortic surface. Using these phantoms, we further investigated the effects on VDM's measurement accuracy resulting from factors that influence quality of clinical CTA data such as respiratory translations, slice thickness and image noise. Lastly, we compare the measurement error of VDM TAA growth assessments against two expert raters performing standard diameter measurements of synthetic phantom images.
Results:
Across our population of 76 synthetic growth phantoms, the median absolute error was 0.063 (IQR: 0.073-0.054) for AR and 0.181mm (IQR: 0.214-0.143mm) for DiN. Median relative error was 1.4% for AR and 3.3% for DiN at the highest tested noise level (CNR = 2.66). Error in VDM output increased with slice thickness, with highest median relative error of 1.5% for AR and 4.1% for DiN at slice thickness of 2.0 mm. Respiratory motion of the aorta resulted in maximal absolute error of 3% AR and 0.6 mm in DiN, but bulk translations in aortic position had a very small effect on measured AR and DiN values (relative errors < 1%). VDM-derived measurements of magnitude and location of maximal diameter change demonstrated significantly high accuracy and lower variability compared to two expert manual raters (p<0.03 across all comparisons).
Conclusions:
VDM yields accurate, three-dimensional assessment of aortic growth in TAA patients and is robust to factors such as image noise, respiration-induced translations and differences in patient position. Further, VDM significantly outperformed two expert manual raters in assessing the magnitude and location of aortic growth despite optimized experimental measurement conditions. These results support validation of the VDM technique for accurate quantification of aortic growth in patients and highlight several important advantages over diameter measurements. This article is protected by copyright. All rights reserved.
The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying the full-field heterogeneous elastic properties of soft materials using traditional computational and engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in using data-driven models to learn full-field mechanical responses such as displacement and strain from experimental or synthetic data. However, research studies on inferring the full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, we propose a physics-informed machine learning approach to identify the elastic modulus distribution in nonlinear, large deformation hyperelastic materials. We evaluate the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) on inferring the heterogeneous material parameter maps across three nonlinear materials with structural complexity that closely resemble real tissue patterns, such as brain tissue and tricuspid valve tissue. Our improved PINN architecture accurately estimates the full-field elastic properties of three hyperelastic constitutive models, with relative errors of less than 5% across all examples. This research has significant potential for advancing our understanding of micromechanical behaviors in biological materials, impacting future innovations in engineering and medicine.
The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying the full-field heterogeneous elastic properties of soft materials using traditional computational and engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in using data-driven models to learn full-field mechanical responses such as displacement and strain from experimental or synthetic data. However, research studies on inferring the full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, we propose a novel approach to identify the elastic modulus distribution in nonlinear, large deformation hyperelastic materials utilizing physics-informed neural networks (PINNs). We evaluate the prediction accuracies and computational efficiency of PINNs, informed by mechanic features and principles, across three synthetic materials with structural complexity that closely resemble real tissue patterns, such as brain tissue and tricuspid valve tissue. Our improved PINN architecture accurately estimates the full-field elastic properties, with relative errors of less than 5% across all examples. This research has significant potential for advancing our understanding of micromechanical behaviors in biological materials, impacting future innovations in engineering and medicine.
Tagged magnetic resonance imaging (tMRI) has been employed for decades to measure the motion of tissue undergoing deformation. However, registration-based motion estimation from tMRI is difficult due to the periodic patterns in these images, particularly when the motion is large. With a larger motion the registration approach gets trapped in a local optima, leading to motion estimation errors. We introduce a novel “momenta, shooting, and correction" framework for Lagrangian motion estimation in the presence of repetitive patterns and large motion. This framework, grounded in Lie algebra and Lie group principles, accumulates momenta in the tangent vector space and employs exponential mapping in the diffeomorphic space for rapid approximation towards true optima, circumventing local optima. A subsequent correction step ensures convergence to true optima. The results on a 2D synthetic dataset and a real 3D tMRI dataset demonstrate our method’s efficiency in estimating accurate, dense, and diffeomorphic 2D/3D motion fields amidst large motion and repetitive patterns.
Bicuspid aortic valve is the most common congenital heart disease and exposes patients to an increased risk of aortic dilation and dissection. Aortic dilation is a slow, silent process, leading to a greater risk of aortic dissection. The prevention of adverse events together with optimization of the frequency of the required lifelong imaging surveillance are important for both clinicians and patients and motivated extensive research to shed light on the physiopathologic processes involved in bicuspid aortic valve aortopathy. Two main research hypotheses have been consolidated in the last decade: one supports a genetic basis for the increased prevalence of dilation, in particular for the aortic root, and the second supports the damaging impact on the aortic wall of altered flow dynamics associated with these structurally abnormal valves, particularly significant in the ascending aorta. Current opinion tends to rule out mutually excluding causative mechanisms, recognizing both as important and potentially clinically relevant.
