Dynamic Contrast-Enhanced MRI in Abdominal Aortic Aneurysms as a Potential Marker for Disease Progression.

BACKGROUND: Abdominal aortic aneurysms (AAAs) may rupture before reaching maximum diameter (Dmax ) thresholds for repair. Aortic wall microvasculature has been associated with elastin content and rupture sites in specimens, but its relation to progression is unknown. PURPOSE: To investigate whether dynamic contrast-enhanced (DCE) MRI of AAA is associated with Dmax or growth. STUDY TYPE: Prospective. POPULATION: A total of 27 male patients with infrarenal AAA (mean age ± standard deviation = 75 ± 5 years) under surveillance with DCE MRI and 2 years of prior follow-up intervals with computed tomography (CT) or MRI. FIELD STRENGTH/SEQUENCE: A 3-T, dynamic three-dimensional (3D) fast gradient-echo stack-of-stars volumetric interpolated breath-hold examination (Star-VIBE). ASSESSMENT: Wall voxels were manually segmented in two consecutive slices at the level of Dmax . We measured slope to 1-minute and area under the curve (AUC) to 1 minute and 4 minutes of the signal intensity change postcontrast relative to that precontrast arrival, and, Ktrans , a measure of microvascular permeability, using the Patlak model. These were averaged over all wall voxels for association to Dmax and growth rate, and, over left/right and anterior/posterior quadrants for testing circumferential homogeneity. Dmax was measured orthogonal to the aortic centerline and growth rate was calculated by linear fit of Dmax measurements. STATISTICAL TESTS: Pearson correlation and linear mixed effects models. A P value <0.05 was considered statistically significant. RESULTS: In 44 DCE MRIs, mean Dmax was 45 ± 7 mm and growth rate in 1.5 ± 0.4 years of prior follow-up was 1.7 ± 1.2 mm per year. DCE measurements correlated with each other (Pearson r = 0.39-0.99) and significantly differed between anterior/posterior versus left/right quadrants. DCE measurements were not significantly associated with Dmax (P = 0.084, 0.289, 0.054 and 0.255 for slope, AUC at 1 minute and 4 minutes, and Ktrans , respectively). Slope and 4 minutes AUC significantly associated with growth rate after controlling for Dmax . CONCLUSION: Contrast uptake may be increased in lateral aspects of the AAA. Contrast enhancement 1-minute slope and 4-minutes AUC may be associated with a period of recent AAA growth that is independent of Dmax . EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Contrast-enhanced CT radiomics improves the prediction of abdominal aortic aneurysm progression.

OBJECTIVES: To determine if three-dimensional (3D) radiomic features of contrast-enhanced CT (CECT) images improve prediction of rapid abdominal aortic aneurysm (AAA) growth. METHODS: This longitudinal cohort study retrospectively analyzed 195 consecutive patients (mean age, 72.4 years ± 9.1) with a baseline CECT and a subsequent CT or MR at least 6 months later. 3D radiomic features were measured for 3 regions of the AAA, viz. the vessel lumen only; the intraluminal thrombus (ILT) and aortic wall only; and the entire AAA sac (lumen, ILT, and wall). Multiple machine learning (ML) models to predict rapid growth, defined as the upper tercile of observed growth (> 0.25 cm/year), were developed using data from 60% of the patients. Diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) in the remaining 40% of patients. RESULTS: The median AAA maximum diameter was 3.9 cm (interquartile range [IQR], 3.3-4.4 cm) at baseline and 4.4 cm (IQR, 3.7-5.4 cm) at the mean follow-up time of 3.2 ± 2.4 years (range, 0.5-9 years). A logistic regression model using 7 radiomic features of the ILT and wall had the highest AUC (0.83; 95% confidence interval [CI], 0.73-0.88) in the development cohort. In the independent test cohort, this model had a statistically significantly higher AUC than a model including maximum diameter, AAA volume, and relevant clinical factors (AUC = 0.78, 95% CI, 0.67-0.87 vs AUC = 0.69, 95% CI, 0.57-0.79; p = 0.04). CONCLUSION: A radiomics-based method focused on the ILT and wall improved prediction of rapid AAA growth from CECT imaging. KEY POINTS: • Radiomic analysis of 195 abdominal CECT revealed that an ML-based model that included textural features of intraluminal thrombus (if present) and aortic wall improved prediction of rapid AAA progression compared to maximum diameter. • Predictive accuracy was higher when radiomic features were obtained from the thrombus and wall as opposed to the entire AAA sac (including lumen), or the lumen alone. • Logistic regression of selected radiomic features yielded similar accuracy to predict rapid AAA progression as random forests or support vector machines.

Improved Appropriateness of Advanced Diagnostic Imaging After Implementation of Clinical Decision Support Mechanism.

The Protecting Access to Medicare Act (PAMA) mandates clinical decision support mechanism (CDSM) consultation for all advanced imaging. There are a growing number of studies examining the association of CDSM use with imaging appropriateness, but a paucity of multicenter data. This observational study evaluates the association between changes in advanced imaging appropriateness scores with increasing provider exposure to CDSM. Each provider's first 200 consecutive anonymized requisitions for advanced imaging (CT, MRI, ultrasound, nuclear medicine) using a single CDSM (CareSelect, Change Healthcare) between January 1, 2017 and December 31, 2019 were collected from 288 US institutions. Changes in imaging requisition proportions among four appropriateness categories ("usually appropriate" [green], "may be appropriate" [yellow], "usually not appropriate" [red], and unmapped [gray]) were evaluated in relation to the chronological order of the requisition for each provider and total provider exposure to CDSM using logistic regression fits and Wald tests. The number of providers and requisitions included was 244,158 and 7,345,437, respectively. For 10,123 providers with ≥ 200 requisitions (2,024,600 total requisitions), the fraction of green, yellow, and red requisitions among the last 10 requisitions changed by +3.0% (95% confidence interval +2.6% to +3.4%), -0.8% (95% CI -0.5% to -1.1%), and -3.0% (95% CI 3.3% to -2.7%) in comparison with the first 10, respectively. Providers with > 190 requisitions had 8.5% (95% CI 6.3% to 10.7%) more green requisitions, 2.3% (0.7% to 3.9%) fewer yellow requisitions, and 0.5% (95% CI -1.0% to 2.0%) fewer red (not statistically significant) requisitions relative to providers with ≤ 10 requisitions. Increasing provider exposure to CDSM is associated with improved appropriateness scores for advanced imaging requisitions.

RadioGraphics Update: Medical 3D Printing for the Radiologist.

Editor's Note.-Articles in the RadioGraphics Update section provide current knowledge to supplement or update information found in full-length articles previously published in RadioGraphics. Authors of the previously published article provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes. Articles in this section are published solely online and are linked to the original article.

Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models.

Recent technological innovations have created new opportunities for the increased adoption of virtual reality (VR) and augmented reality (AR) applications in medicine. While medical applications of VR have historically seen greater adoption from patient-as-user applications, the new era of VR/AR technology has created the conditions for wider adoption of clinician-as-user applications. Historically, adoption to clinical use has been limited in part by the ability of the technology to achieve a sufficient quality of experience. This article reviews the definitions of virtual and augmented reality and briefly covers the history of their development. Currently available options for consumer-level virtual and augmented reality systems are presented, along with a discussion of technical considerations for their adoption in the clinical environment. Finally, a brief review of the literature of medical VR/AR applications is presented prior to introducing a comprehensive conceptual framework for the viewing and manipulation of medical images in virtual and augmented reality. Using this framework, we outline considerations for placing these methods directly into a radiology-based workflow and show how it can be applied to a variety of clinical scenarios.

Common First-Pass CT Angiography Findings Associated With Rapid Growth Rate in Abdominal Aorta Aneurysms Between 3 and 5 cm in Largest Diameter.

OBJECTIVE: The purpose of this study was to describe CT angiography (CTA) findings of lumen contrast heterogeneity and intraluminal thrombus volume and to evaluate their relationship with rapid aneurysm growth in patients with abdominal aortic aneurysms (AAA) between 3 and 5 cm. MATERIALS AND METHODS: This institutional review board-approved and HIPAA-compliant single-center retrospective study included CTA studies obtained between January 2004 and December 2014 in 140 patients with AAA (101 men, 39 women; mean age ± SD, 70 ± 9 years old; age range, 22-87 years old). Standardized measurements for aneurysm intraluminal thrombus volume and a relatively new metric termed "lumen contrast heterogeneity" were obtained from the CTA images. AAA growth rate data were acquired from all subsequent cross-sectional studies. The association between the imaging findings and rapid aneurysm growth (> 0.4 cm/y) was evaluated using multivariate logistic regression. Patient comorbidities and medications were added to the regression model to assess for further associations with rapid growth rate. RESULTS: Using a baseline logistic regression model, lumen contrast heterogeneity (odds ratio [OR], 1.16; 95% CI, 1.05-1.32), intraluminal thrombus volume (OR, 2.15; 95% CI, 1.26-3.86), and maximum AAA diameter (OR, 1.69; 95% CI, 1.03-2.84) were independently associated with increased likelihood of rapid aneurysm growth. None of the patient comorbidities or medications were significantly associated with the outcome when added to the baseline model. CONCLUSION: Both intraluminal thrombus and lumen contrast heterogeneity are seen on AAA CTA studies and can be quantified; both of these metrics are independently associated with rapid growth rate and should be recognized by radiologists evaluating patients with AAA during surveillance.

Early animal model evaluation of an implantable contrast agent to enhance magnetic resonance imaging of arterial bypass vein grafts.

Background Non-invasive monitoring of autologous vein graft (VG) bypass grafts is largely limited to detecting late luminal narrowing. Although magnetic resonance imaging (MRI) delineates vein graft intima, media, and adventitia, which may detect early failure, the scan time required to achieve sufficient resolution is at present impractical. Purpose To study VG visualization enhancement in vivo and delineate whether a covalently attached MRI contrast agent would enable quicker longitudinal imaging of the VG wall. Material and Methods Sixteen 12-week-old male C57BL/6J mice underwent carotid interposition vein grafting. The inferior vena cava of nine donor mice was treated with a gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA)-based contrast agent, with control VGs labeled with a vehicle. T1-weighted (T1W) MRI was performed serially at postoperative weeks 1, 4, 12, and 20. A portion of animals was sacrificed for histopathology following each imaging time point. Results MRI signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significantly higher for treated VGs in the first three time points (1.73 × higher SNR, P = 0.0006, and 5.83 × higher CNR at the first time point, P = 0.0006). However, MRI signal enhancement decreased consistently in the study period, to 1.29 × higher SNR and 2.64 × higher CNR, by the final time point. There were no apparent differences in graft morphometric analyses in Masson's trichrome-stained sections. Conclusion A MRI contrast agent that binds covalently to the VG wall provides significant increase in T1W MRI signal with no observed adverse effects in a mouse model. Further optimization of the contrast agent to enhance its durability is required.

Utility and reproducibility of 3-dimensional printed models in pre-operative planning of complex thoracic tumors.

BACKGROUND AND OBJECTIVES: 3D-printed models are increasingly used for surgical planning. We assessed the utility, accuracy, and reproducibility of 3D printing to assist visualization of complex thoracic tumors for surgical planning. METHODS: Models were created from pre-operative images for three patients using a standard radiology 3D workstation. Operating surgeons assessed model utility using the Gillespie scale (1 = inferior to 4 = superior), and accuracy compared to intraoperative findings. Model variability was assessed for one patient for whom two models were created independently. The models were compared subjectively by surgeons and quantitatively based on overlap of depicted tissues, and differences in tumor volume and proximity to tissues. RESULTS: Models were superior to imaging and 3D visualization for surgical planning (mean score = 3.4), particularly for determining surgical approach (score = 4) and resectability (score = 3.7). Model accuracy was good to excellent. In the two models created for one patient, tissue volumes overlapped by >86.5%, and tumor volume and area of tissues ≤1 mm to the tumor differed by <15% and <1.8 cm2 , respectively. Surgeons considered these differences to have negligible effect on surgical planning. CONCLUSION: 3D printing assists surgical planning for complex thoracic tumors. Models can be created by radiologists using routine practice tools with sufficient accuracy and clinically negligible variability.

Measuring and Establishing the Accuracy and Reproducibility of 3D Printed Medical Models.

Despite the rapid growth of three-dimensional (3D) printing applications in medicine, the accuracy and reproducibility of 3D printed medical models have not been thoroughly investigated. Although current technologies enable 3D models to be created with accuracy within the limits of clinical imaging spatial resolutions, this is not always achieved in practice. Inaccuracies are due to errors that occur during the imaging, segmentation, postprocessing, and 3D printing steps. Radiologists' understanding of the factors that influence 3D printed model accuracy and the metrics used to measure this accuracy is key in directing appropriate practices and establishing reference standards and validation procedures. The authors review the various factors in each step of the 3D model printing process that contribute to model inaccuracy, including the intrinsic limitations of each printing technology. In addition, common sources of model inaccuracy are illustrated. Metrics involving comparisons of model dimensions and morphology that have been developed to quantify differences between 3D models also are described and illustrated. These metrics can be used to define the accuracy of a model, as compared with the reference standard, and to measure the variability of models created by different observers or using different workflows. The accuracies reported for specific indications of 3D printing are summarized, and potential guidelines for quality assurance and workflow assessment are discussed. Online supplemental material is available for this article. ©RSNA, 2017.

Natural Language Processing Technologies in Radiology Research and Clinical Applications.

The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications.

On the lorentzian versus Gaussian character of time-domain spin-echo signals from the brain as sampled by means of gradient-echoes: Implications for quantitative transverse relaxation studies.

PURPOSE: To determine whether Lorentzian or Gaussian intra-voxel frequency distributions are better suited for modeling data acquired with gradient-echo sampling of single spin-echoes for the simultaneous characterization of irreversible and reversible relaxation rates. Clinical studies (e.g., of brain iron deposition) using such acquisition schemes have typically assumed Lorentzian distributions. THEORY AND METHODS: Theoretical expressions of the time-domain spin-echo signal for intra-voxel Lorentzian and Gaussian distributions were used to fit data from a human brain scanned at both 1.5 Tesla (T) and 3T, resulting in maps of irreversible and reversible relaxation rates for each model. The relative merits of the Lorentzian versus Gaussian model were compared by means of quality of fit considerations. RESULTS: Lorentzian fits were equivalent to Gaussian fits primarily in regions of the brain where irreversible relaxation dominated. In the multiple brain regions where reversible relaxation effects become prominent, however, Gaussian fits were clearly superior. CONCLUSION: The widespread assumption that a Lorentzian distribution is suitable for quantitative transverse relaxation studies of the brain should be reconsidered, particularly at 3T and higher field strengths as reversible relaxation effects become more prominent. Gaussian distributions offer alternate fits of experimental data that should prove quite useful in general. Magn Reson Med 74:51-62, 2015. © 2014 Wiley Periodicals, Inc.

Height and body surface area versus wall stress for stratification of mid-term outcomes in ascending aortic aneurysm.

Objectives: Current diameter-based guidelines for ascending thoracic aortic aneurysms (aTAA) do not consistently predict risk of dissection/rupture. ATAA wall stresses may enhance risk stratification independent of diameter. The relation of wall stresses and diameter indexed to height and body surface area (BSA) is unknown. Our objective was to compare aTAA wall stresses with indexed diameters in relation to all-cause mortality at 3.75 years follow-up. Methods: Finite element analyses were performed in a veteran population with aortas ≥ 4.0 cm. Three-dimensional geometries were reconstructed from computed tomography with models accounting for pre-stress geometries. A fiber-embedded hyperelastic material model was applied to obtain wall stress distributions under systolic pressure. Peak wall stresses were compared across guideline thresholds for diameter/BSA and diameter/height. Hazard ratios for all-cause mortality and surgical aneurysm repair were estimated using cause-specific Cox proportional hazards models. Results: Of 253 veterans, 54 (21 %) had aneurysm repair at 3.75 years. Indexed diameter alone would have prompted repair at baseline in 17/253 (6.7 %) patients, including only 4/230 (1.7 %) with diameter < 5.5 cm. Peak wall stresses did not significantly differ across guideline thresholds for diameter/BSA (circumferential: p = 0.15; longitudinal: p = 0.18), but did differ for diameter/height (circumferential: p = 0.003; longitudinal: p = 0.048). All-cause mortality was independently associated with peak longitudinal stresses (p = 0.04). Peak longitudinal stresses were best predicted by diameter (c-statistic = 0.66), followed by diameter/height (c-statistic = 0.59), and diameter/BSA (c-statistic = 0.55). Conclusions: Diameter/height improved stratification of peak wall stresses compared to diameter/BSA. Peak longitudinal stresses predicted all-cause mortality independent of age and indexed diameter and may aid risk stratification for aTAA adverse events.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Coronary pressure-derived fractional flow reserve in the assessment of coronary artery stenoses.

OBJECTIVES: Catheter-based angiography is the reference-standard to establish coronary anatomy. While routinely employed clinically, lumen assessment correlates poorly with physiological measures of ischaemia. Moreover, functional studies to identify and localise ischaemia before elective angiography are often not available. This article reviews fractional flow reserve (FFR) and its role in guiding patient management for patients with a potentially haemodynamic significant coronary lesion. METHODS: This review discusses the theory, evidence, indications, and limitations of FFR. Also included are emerging non-invasive imaging FFR surrogates currently under evaluation for accuracy with respect to standard FFR. RESULTS: Coronary pressure-derived fractional flow reserve (FFR) rapidly assesses the haemodynamic significance of individual coronary artery lesions and can readily be performed in the catheterisation laboratory. The use of FFR has been shown to effectively guide coronary revascularization procedures leading to improved patient outcomes. CONCLUSIONS: FFR is an invaluable modality in guiding coronary disease treatment decisions. It is safe, cost-effective and leads to improved patient outcomes. Non-invasive imaging modalities to assess the physiologic significance of CAD are currently being developed and evaluated.

Computed tomography angiography gravitational gradient as an imaging sign of slow flow.

Intraluminal layering of iodinated contrast on a single axial computed tomography image is described as a possible sign of slow arterial flow and is quantified using a new region of interest-based metric, that is, gravitational gradient. The metric was measured in 4 patients who demonstrated this phenomenon and in the aorta of 10 patients with no clinical sign suggestive of slow flow. Future studies are needed to validate the relationship between gravitational gradient, slow flow, and patient outcome.

Aortic area/height ratio, peak wall stresses, and outcomes in veterans with tricuspid versus bicuspid aortic valve-associated ascending thoracic aortic aneurysms.

BACKGROUND: In ascending thoracic aortic aneurysm risk stratification, aortic area/height ratio is a reasonable alternative to maximum diameter. Biomechanically, aortic dissection may be initiated by wall stress exceeding wall strength. Our objective was to evaluate the association between aortic area/height and peak aneurysm wall stresses in relation to valve morphology and 3-year all-cause mortality. METHODS: Finite element analysis was performed on 270 ascending thoracic aortic aneurysms (46 associated with bicuspid and 224 with tricuspid aortic valves) in veterans. Three-dimensional aneurysm geometries were reconstructed from computed tomography and models developed accounting for prestress geometries. Fiber-embedded hyperelastic material model was applied to obtain aneurysm wall stresses during systole. Correlations of aortic area/height ratio and peak wall stresses were compared across valve types. Area/height ratio was evaluated across peak wall stress thresholds obtained from proportional hazards models of 3-year all-cause mortality, with aortic repair treated as a competing risk. RESULTS: Aortic area/height 10 cm2/m or greater coincided with 23/34 (68%) 5.0 to 5.4 cm and 20/24 (83%) 5.5 cm or greater aneurysms. Area/height correlated weakly with peak aneurysm stresses: for tricuspid valves, r = 0.22 circumferentially and r = 0.24 longitudinally; and for bicuspid valves, r = 0.42 circumferentially and r = 0.14 longitudinally. Age and peak longitudinal stress, but not area/height, were independent predictors of all-cause mortality (age: hazard ratio, 2.20 per 9-year increase, P = .013; peak longitudinal stress: hazard ratio, 1.78 per 73-kPa increase, P = .035). CONCLUSIONS: Area/height was more predictive of high circumferential stresses in bicuspid than tricuspid valve aneurysms, but similarly less predictive of high longitudinal stresses in both valve types. Peak longitudinal stress, not area/height, independently predicted all-cause mortality. VIDEO ABSTRACT.

Association of 3-Year All-Cause Mortality and Peak Wall Stresses of Ascending Thoracic Aortic Aneurysms in Veterans.

Risk of aortic dissection in ascending thoracic aortic aneurysms is not sufficiently captured by size-based metrics. From a biomechanical perspective, dissection may be initiated when wall stress exceeds wall strength. Our objective was to assess the association between aneurysm peak wall stresses and 3-year all-cause mortality. Finite element analysis was performed in 273 veterans with chest computed tomography for surveillance of ascending thoracic aortic aneurysms. Three-dimensional geometries were reconstructed and models developed accounting for prestress geometries. A fiber-embedded hyperelastic material model was applied to obtain circumferential and longitudinal wall stresses under systolic pressure. Patients were followed up to 3 years following the scan to assess aneurysm repair and all-cause mortality. Fine-Gray subdistribution hazards were estimated for all-cause mortality based on age, aortic diameter, and peak wall stresses, treating aneurysm repair as a competing risk. When accounting for age, subdistribution hazard of mortality was not significantly increased by peak circumferential stresses (p = 0.30) but was significantly increased by peak longitudinal stresses (p = 0.008). Aortic diameter did not significantly increase subdistribution hazard of mortality in either model (circumferential model: p = 0.38; longitudinal model: p = 0.30). The effect of peak longitudinal stresses on subdistribution hazard of mortality was maximized at a binary threshold of 355kPa, which captured 34 of 212(16%) patients with diameter <5 cm, 11 of 36(31%) at 5.0-5.4 cm, and 11 of 25(44%) at ≥5.5 cm. Aneurysm peak longitudinal stresses stratified by age and diameter were associated with increased hazard of 3-year all-cause mortality in a veteran cohort. Risk prediction may be enhanced by considering peak longitudinal stresses.

Measurement of Abdominal Aortic Aneurysm Strain Using MR Deformable Image Registration: Accuracy and Relationship to Recent Aneurysm Progression.

BACKGROUND: Management of asymptomatic abdominal aortic aneurysm (AAA) based on maximum aneurysm diameter and growth rate fails to preempt many ruptures. Assessment of aortic wall biomechanical properties may improve assessment of progression and rupture risk. This study aimed to assess the accuracy of AAA wall strain measured by cine magnetic resonance imaging (MRI) deformable image registration (MR strain) and investigate its relationship with recent AAA progression. METHODS: The MR strain accuracy was evaluated in silico against ground truth strain in 54 synthetic MRIs generated from a finite element model simulation of an AAA patient's abdomen for different aortic pulse pressures, tissue motions, signal intensity variations, and image noise. Evaluation included bias with 95% confidence interval (CI) and correlation analysis. Association of MR strain with AAA growth rate was assessed in 25 consecutive patients with >6 months of prior surveillance, for whom cine balanced steady-state free-precession imaging was acquired at the level of the AAA as well as the proximal, normal-caliber aorta. Univariate and multivariate regressions were used to associate growth rate with clinical variables, maximum AAA diameter (Dmax), and peak circumferential MR strain through the cardiac cycle. The MR strain interoperator variability was assessed using bias with 95% CI, intraclass correlation coefficient, and coefficient of variation. RESULTS: In silico experiments revealed an MR strain bias of 0.48% ± 0.42% and a slope of correlation to ground truth strain of 0.963. In vivo, AAA MR strain (1.2% ± 0.6%) was highly reproducible (bias ± 95% CI, 0.03% ± 0.31%; intraclass correlation coefficient, 97.8%; coefficient of variation, 7.14%) and was lower than in the nonaneurysmal aorta (2.4% ± 1.7%). Dmax (ß = 0.087) and MR strain (ß= -1.563) were both associated with AAA growth rate. The MR strain remained an independent factor associated with growth rate (ß= -0.904) after controlling for Dmax. CONCLUSIONS: Deformable image registration analysis can accurately measure the circumferential strain of the AAA wall from standard cine MRI and may offer patient-specific insight regarding AAA progression.