Generalized super-resolution 4D Flow MRI - using ensemble learning to extend across the cardiovascular system.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was therefore to explore the generalizability of SR 4D Flow MRI using a combination of existing super-resolution base models, novel heterogeneous training sets, and dedicated ensemble learning techniques; the latter-most being effectively used for improved domain adaption in other domains or modalities, however, with no previous exploration in the setting of 4D Flow MRI. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with the novel use of ensemble learning in the setting of advanced fullfield flow imaging extending utility across various clinical areas of interest.

Changes in aortic root dimensions post aortic root enlargement with Y-incision and modified aortotomy.

Background: Lifetime management in aortic stenosis (AS) can be facilitated by aortic root enlargement (ARE) to improve anatomy for future valve-in-valve (ViV) procedures. A mitral valve-sparing ARE technique ("Y-incision") and sinotubular junction (STJ) enlargement ("roof" patch aortotomy) allow upsizing by 3-4 valve sizes, but quantitative analysis of changes in root anatomy is lacking. Methods: Among 78 patients who underwent ARE by Y-incision technique (± roof aortotomy closure) we identified 45 patients with high-quality pre- and post-operative computed tomography angiography (CTA) scans to allow analysis of change in aortic root dimensions. Detailed measurements of the annulus/basilar ring and sinuses were performed by an expert imager on both pre- and post-operative CTAs. The basal ring was defined as the functional annulus when a bioprosthetic valve was present. Results: Average age was 65±11 years, the majority were female (29, 64%), and 9 (20%) had undergone prior aortic valve replacement (AVR). Valve upsizing was ≥3 sizes in 41 (91%). Post-operative mean basal ring diameter was larger compared to the native annular diameter (26.3 vs. 25.3 mm, P<0.01) and substantially larger than prior prosthetic valve in redo AVR (25.6 vs. 19.3 mm, P<0.001). Diameters of the sinuses at pre-operative computed tomography (CT) increased by +7.7±2.8 [right sinuses of Valsalva (R SVS)], +6.7±3.0 [left sinuses of Valsalva (L SVS)], and +6.6±2.9 mm [non-coronary sinuses of Valsalva (N SVS)]. Mean diameter of the STJ increased to 38.3±3.7 post-operative (+8.1±3.2 mm). Left main (LM) and right coronary artery (RCA) heights decreased by -6.3±3.3 and -3.7±3.4 mm respectively due to the supra-annular position of the valve, however, the post-operative valve-to-coronary (VTC) artery distances were 6.6±2.3 and 4.9±2.0 mm, respectively. Conclusion: The Y-incision root enlargement technique significantly enlarges the sinus and STJ diameters by 6-7 mm while preserving VTC distances despite upsizing by 3-4 valve sizes, resulting in post-operative anatomy that is favorable for future transcatheter aortic valve-in-surgical aortic valve (TAV-in-SAV).

lGeneralized super-resolution 4D Flow MRI-using ensemble learning to extend across the cardiovascular system.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.

Fully automated pipeline for measurement of the thoracic aorta using joint segmentation and localization neural network.

Purpose: Diagnosis and surveillance of thoracic aortic aneurysm (TAA) involves measuring the aortic diameter at various locations along the length of the aorta, often using computed tomography angiography (CTA). Currently, measurements are performed by human raters using specialized software for three-dimensional analysis, a time-consuming process, requiring 15 to 45 min of focused effort. Thus, we aimed to develop a convolutional neural network (CNN)-based algorithm for fully automated and accurate aortic measurements. Approach: Using 212 CTA scans, we trained a CNN to perform segmentation and localization of key landmarks jointly. Segmentation mask and landmarks are subsequently used to obtain the centerline and cross-sectional diameters of the aorta. Subsequently, a cubic spline is fit to the aortic boundary at the sinuses of Valsalva to avoid errors related inclusions of coronary artery origins. Performance was evaluated on a test set of 60 scans with automated measurements compared against expert manual raters. Result: Compared to training separate networks for each task, joint training yielded higher accuracy for segmentation, especially at the boundary (p<0.001), but a marginally worse (0.2 to 0.5 mm) accuracy for landmark localization (p<0.001). Mean absolute error between human and automated was ≤1 mm at six of nine standard clinical measurement locations. However, higher errors were noted in the aortic root and arch regions, ranging between 1.4 and 2.2 mm, although agreement of manual raters was also lower in these regions. Conclusion: Fully automated aortic diameter measurements in TAA are feasible using a CNN-based algorithm. Automated measurements demonstrated low errors that are comparable in magnitude to those with manual raters; however, measurement error was highest in the aortic root and arch.

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update.

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.

Three-Dimensional Characterization of Aortic Root Motion by Vascular Deformation Mapping.

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.

A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T.

OBJECTIVE: This study combines a deep image prior with low-rank subspace modeling to enable real-time (free-breathing and ungated) functional cardiac imaging on a commercial 0.55 T scanner. MATERIALS AND METHODS: The proposed low-rank deep image prior (LR-DIP) uses two u-nets to generate spatial and temporal basis functions that are combined to yield dynamic images, with no need for additional training data. Simulations and scans in 13 healthy subjects were performed at 0.55 T and 1.5 T using a golden angle spiral bSSFP sequence with images reconstructed using [Formula: see text]-ESPIRiT, low-rank plus sparse (L + S) matrix completion, and LR-DIP. Cartesian breath-held ECG-gated cine images were acquired for reference at 1.5 T. Two cardiothoracic radiologists rated images on a 1-5 scale for various categories, and LV function measurements were compared. RESULTS: LR-DIP yielded the lowest errors in simulations, especially at high acceleration factors (R [Formula: see text] 8). LR-DIP ejection fraction measurements agreed with 1.5 T reference values (mean bias - 0.3% at 0.55 T and - 0.2% at 1.5 T). Compared to reference images, LR-DIP images received similar ratings at 1.5 T (all categories above 3.9) and slightly lower at 0.55 T (above 3.4). CONCLUSION: Feasibility of real-time functional cardiac imaging using a low-rank deep image prior reconstruction was demonstrated in healthy subjects on a commercial 0.55 T scanner.

Inter-observer variability of expert-derived morphologic risk predictors in aortic dissection.

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

Patient-Specific Computational Modeling of Different Cannulation Strategies for Extracorporeal Membrane Oxygenation.

Institution of extracorporeal membrane oxygenation (ECMO) results in unique blood flow characteristics to the end-organ vascular beds. We studied the interplay between cardiac-driven and extracorporeal membrane oxygenation (ECMO)-driven flow to vascular beds in different ECMO configurations using a patient-specific computational fluid dynamics (CFD) analysis. A computational ECMO model (femoral artery cannulation [FAC]) was constructed using patient-specific imaging and hemodynamic data. Following model calibration, we augmented the 3D geometrical model to represent alternative ECMO configurations (ascending aorta cannulation [AAC] and subclavian artery cannulation [SAC]). We performed CFD analyses, including a novel virtual color-dye analysis to compare global and regional blood flow and pressure characteristics as well as contributions of cardiac and ECMO-derived flow to the various vascular beds. Flow waveforms at all the aortic branch vessels were pulsatile, despite low cardiac output and predominant nonpulsatile ECMO-driven hemodynamics. Virtual color-dye analysis revealed differential contribution of cardiac and ECMO-derived flow to the end-organ vascular beds in the FAC model, while this was more evenly distributed in the AAC and SAC models. While global hemodynamics were relatively similar between various ECMO configurations, several distinct hemodynamic indices, in particular wall shear stress and oscillatory shear patterns, as well as differential contribution of ECMO-derived flow to various vascular beds, showed remarkable differences. The clinical impact of this study highlighting the relevance of CFD modeling in assessment of complex hemodynamics in ECMO warrants further evaluation.

Left ventricular remodeling following aortic root and ascending aneurysm repair.

Objective: Adverse left ventricular remodeling due to a mismatch between stiffness of native aortic tissue and current polyester grafts may be under-recognized. This study was conducted to evaluate the impact of proximal aortic replacement on adverse remodeling of the left ventricle. Materials and methods: All aortic root and ascending aortic aneurysm patients were identified (n = 2,001, 2006-2019). The study cohort consisted of a subset of patients (n = 98) with two or more electrocardiogram (ECG)-gated CT angiograms, but without concomitant aortic valve disease or bicuspid aortic valve, connective tissue disease, acute aortic syndrome or prior history of aortic repair or mitral valve surgery. LV myocardial mass was measured from CT data and indexed to body surface area (LVMI). The study cohort was divided into a surgery group (n = 47) and a control group; optimal medical therapy group (OMT, n = 51). Results: The mean age was 60 ± 11 years (80% male). Beta-blocker use was significantly more frequent in the surgery group (89 vs. 57%, p < 0.001), whereas, all other antihypertensive drugs were more frequent in the OMT group. The average follow-up was 9.1 ± 4.0 months for the surgery group and 13.7 ± 6.3 months for the OMT group. Average LVMI at baseline was similar in both groups (p = 0.934). LVMI increased significantly in the surgery group compared to the OMT group (3.7 ± 4.1 vs. 0.6 ± 4.4 g/m2, p = 0.001). Surgery, baseline LVMI, age, and sex were found to be independent predictors of LVMI increased on multivariable analysis. Conclusion: Proximal aortic repair with stiff polyester grafts was associated with increased LV mass in the first-year post-operative and may promote long-term adverse cardiac remodeling. Further studies should be considered to evaluate the competing effects of aortic aneurysm related mortality against risks of long-term graft induced aortic stiffening and the potential implications on current size thresholds for intervention.

Natural history and clinical significance of aortic focal intimal flaps.

Objective: Focal intimal flaps (FIF) are a variety of defects of the aorta that result in a short, flap-like projection into the lumen, and are often encountered in asymptomatic patients undergoing computed tomography angiography (CTA) surveillance for aortic aneurysm, but the natural history and clinical significance of such lesions has not yet been studied. Methods: We retrospectively identified patients with an asymptomatic FIF and available imaging follow-up (>1 year). FIF was defined as flap-like intimal irregularity < 4 cm in length involving the thoracic aorta (TA), abdominal aorta (AA) or common iliac arteries (CIA). FIF characteristics included length and circumferential extent as well as the presence and size (width and depth) of associated penetrating aortic ulcers (PAUs). Patient characteristics, adverse events and history of surgical repair was determined by chart review. FIFs and associated PAUs were assessed for progression by comparing baseline and follow-up CTA studies. Results: A total of 84 FIFs were identified in 77 patients. Average age was 69.2 ± 10.1 years, and 81% were male (81%). Common co-morbidities included: hypertension (78%), hyperlipidemia (68%), smoking (60%), coronary artery disease (41%), aortic aneurysm (34%), type II diabetes mellitus (27%) and prior cardiovascular surgery (25%). FIFs were most commonly located in the abdominal aorta (n = 50, 60%). Nearly all FIFs were associated with local atherosclerotic plaque (93%). Mean follow-up interval was 3.5 ± 2.6 years (259 cumulative follow-up years). Change in FIF length and local aortic diameter over follow-up were 0.7 ± 2.3 mm and 0.8 ± 1.1 mm, respectively. Nearly half (47%) of FIFs were associated with penetrating aortic ulcers (PAU) with baseline depth of 7.3 mm (IQR: 6.1-10.2) and change in depth of 0.5 ± 1.4 mm. Only 12% of FIFs and 0% of associated PAUs demonstrated growth (≥3 mm) at follow-up. No acute pathology developed in the location of FIFs and no aortic interventions were performed specifically to treat FIFs. Conclusion: Focal intimal flaps identified in asymptomatic patients with aortic disease were co-localized with atherosclerotic plaque and PAUs, and demonstrated indolent behavior, not leading to significant growth or acute aortic events, supporting a conservative management approach.

Validation of a robust method for quantification of three-dimensional growth of the thoracic aorta using deformable image registration.

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 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 the 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 0.181 mm (interquartile range [IQR]: 0.214-0.143 mm) for DiN. Median relative error was 1.4% for AR and 3.3 % $3.3\%$ for DiN at the highest tested noise level (contrast-to-noise ratio [CNR] = 2.66). Error in VDM output increased with slice thickness, with the highest median relative error of 1.5% for AR and 4.1% for DiN at a 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 % $< 1\%$ ). 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 $p<0.03$ across all comparisons). CONCLUSIONS: VDM yields an 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.

Imaging features of renal malperfusion in aortic dissection.

OBJECTIVES: Malperfusion syndrome accompanying aortic dissection is an independent predictor of death with in-hospital mortality rates >60%. Asymmetrically decreased renal enhancement on computed tomography angiography is often considered evidence of renal malperfusion. We investigated the associations between renal enhancement, baseline laboratory values and the diagnosis of renal malperfusion, as defined by invasive manometry, among patients with aortic dissection. METHODS: In this retrospective cohort study, we included all patients who were referred to our institution with acute dissection and suspected visceral malperfusion between 2010 and 2020. We determined asymmetric renal enhancement by visual assessment and quantitative density measurements of the renal cortex. We collected invasive renal artery pressures during invasive angiography at the aortic root and in the renal arteries. Logistic regression was performed to evaluate independent predictors of renal malperfusion. RESULTS: Among the 161 patients analysed, the majority of patients were male (78%) and had type A dissection (52%). Invasive angiography confirmed suspected renal malperfusion in 83% of patients. Global asymmetric renal enhancement was seen in 42% of patients who did not have renal malperfusion during invasive angiography. Asymmetrically decreased renal enhancement was 65% sensitive and 58% specific for renal malperfusion. Both global [odds ratio (OR) 4.43; 1.20-16.41, P = 0.03] and focal (OR 11.23; 1.12-112.90, P = 0.04) enhancement defects were independent predictors for renal malperfusion. CONCLUSIONS: In patients with aortic dissection, we found that differential enhancement of the kidney as seen on the computed tomography angiography is predictive, but not prescriptive for renal malperfusion. While detection of renal malperfusion is aided by computed tomography angiography, its diagnosis requires close monitoring and often invasive assessment.

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth.

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 Patients with ascending and descending TAA with two or more CT angiography studies between 2006 and 2020 were retrospectively identified. The 3D aortic growth was quantified using vascular deformation mapping (VDM), a technique that uses deformable registration to warp a mesh constructed from baseline aortic anatomy. Growth assessments between VDM and clinical CT diameter measurements were compared. Aortic growth was quantified as the ratio of change in surface area at each mesh element (area ratio). Manual segmentations were performed by independent raters to assess interrater reproducibility. Registration error was assessed using manually placed landmarks. Agreement between VDM and clinical diameter measurements was assessed using Pearson correlation and Cohen κ coefficients. Results A total of 38 patients (68 surveillance intervals) were evaluated (mean age, 69 years ± 9 [standard deviation]; 21 women), with TAA involving the ascending aorta (n = 26), descending aorta (n = 10), or both (n = 2). VDM was technically successful in 35 of 38 (92%) patients and 58 of 68 intervals (85%). Median registration error was 0.77 mm (interquartile range, 0.54-1.10 mm). Interrater agreement was high for aortic segmentation (Dice similarity coefficient = 0.97 ± 0.02) and VDM-derived area ratio (bias = 0.0, limits of agreement: -0.03 to 0.03). There was strong agreement (r = 0.85, P < .001) between peak area ratio values and diameter change. VDM detected growth in 14 of 58 (24%) intervals. VDM revealed growth outside the maximally dilated segment in six of 14 (36%) growth intervals, none of which were detected with diameter measurements. Conclusion Vascular deformation mapping provided reliable and comprehensive quantitative assessment of three-dimensional aortic growth and growth patterns in patients with thoracic aortic aneurysms undergoing CT surveillance. Published under a CC BY 4.0 license Online supplemental material is available for this article. See also the editorial by Wieben in this issue.

Altered Aortic Hemodynamics and Relative Pressure in Patients with Dilated Cardiomyopathy.

Ventricular-vascular interaction is central in the adaptation to cardiovascular disease. However, cardiomyopathy patients are predominantly monitored using cardiac biomarkers. The aim of this study is therefore to explore aortic function in dilated cardiomyopathy (DCM). Fourteen idiopathic DCM patients and 16 controls underwent cardiac magnetic resonance imaging, with aortic relative pressure derived using physics-based image processing and a virtual cohort utilized to assess the impact of cardiovascular properties on aortic behaviour. Subjects with reduced left ventricular systolic function had significantly reduced aortic relative pressure, increased aortic stiffness, and significantly delayed time-to-pressure peak duration. From the virtual cohort, aortic stiffness and aortic volumetric size were identified as key determinants of aortic relative pressure. As such, this study shows how advanced flow imaging and aortic hemodynamic evaluation could provide novel insights into the manifestation of DCM, with signs of both altered aortic structure and function derived in DCM using our proposed imaging protocol.

Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) in Uncomplicated Type B Aortic Dissection: Study Design and Rationale.

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.

Comparative Study of Human and Murine Aortic Biomechanics and Hemodynamics in Vascular Aging.

Introduction: Aging has many effects on the cardiovascular system, including changes in structure (aortic composition, and thus stiffening) and function (increased proximal blood pressure, and thus cardiac afterload). Mouse models are often used to gain insight into vascular aging and mechanisms of disease as they allow invasive assessments that are impractical in humans. Translation of results from murine models to humans can be limited, however, due to species-specific anatomical, biomechanical, and hemodynamic differences. In this study, we built fluid-solid-interaction (FSI) models of the aorta, informed by biomechanical and imaging data, to compare wall mechanics and hemodynamics in humans and mice at two equivalent ages: young and older adults. Methods: For the humans, 3-D computational models were created using wall property data from the literature as well as patient-specific magnetic resonance imaging (MRI) and non-invasive hemodynamic data; for the mice, comparable models were created using population-based properties and hemodynamics as well as subject-specific anatomies. Global aortic hemodynamics and wall stiffness were compared between humans and mice across age groups. Results: For young adult subjects, we found differences between species in pulse pressure amplification, compliance and resistance distribution, and aortic stiffness gradient. We also found differences in response to aging between species. Generally, the human spatial gradients of stiffness and pulse pressure across the aorta diminished with age, while they increased for the mice. Conclusion: These results highlight key differences in vascular aging between human and mice, and it is important to acknowledge these when using mouse models for cardiovascular research.