Feasibility of virtual non-iodine coronary calcium scoring on dual source photon-counting coronary CT angiography: a dynamic phantom study.

BACKGROUND: The aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans. METHODS: In this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60-75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm3, respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CACCCTA and CACCSCT scoring was measured using Cohen weighted linear κ with 95% CI. RESULTS: For CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CACCCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CACCCTA was not significantly different from CACCSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671-0.853). CONCLUSION: In this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial. CLINICAL RELEVANCE STATEMENT: Photon counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice. KEY POINTS: Photon-counting CTA allows for excellent detectability of low-density calcifications at all heart rates. Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed. Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

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.

Low-dose coronary calcium scoring CT using a dedicated reconstruction filter for kV-independent calcium measurements.

In this prospective, pilot study, we tested a kV-independent coronary artery calcium scoring CT protocol, using a novel reconstruction kernel (Sa36f). From December 2018 to November 2019, we performed an additional research scan in 61 patients undergoing clinical calcium scanning. For the standard protocol (120 kVp), images were reconstructed with a standard, medium-sharp kernel (Qr36d). For the research protocol (automated kVp selection), images were reconstructed with a novel kernel (Sa36f). Research scans were sequentially performed using a higher (cohort A, n = 31) and a lower (cohort B, n = 30) dose optimizer setting within the automatic system with customizable kV selection. Agatston scores, coronary calcium volumes, and radiation exposure of the standard and research protocol were compared. A phantom study was conducted to determine inter-scan variability. There was excellent correlation for the Agatston score between the two protocols (r = 0.99); however, the standard protocol resulted in slightly higher Agatston scores (29.4 [0-139.0] vs 17.4 [0-158.2], p = 0.028). The median calcium volumes were similar (11.5 [0-109.2] vs 11.2 [0-118.0] mm3; p = 0.176), and the number of calcified lesions was not significantly different (p = 0.092). One patient was reclassified to another risk category. The research protocol could be performed at a lower kV and resulted in a substantially lower radiation exposure, with a median volumetric CT dose index of 4.1 vs 5.2 mGy, respectively (p < 0.001). Our results showed that a consistent coronary calcium scoring can be achieved using a kV-independent protocol that lowers radiation doses compared to the standard protocol. KEY POINTS: • The Sa36f kernel enables kV-independent Agatston scoring without changing the original Agatston weighting threshold. • Agatston scores and calcium volumes of the standard and research protocols showed an excellent correlation. • The research protocol resulted in a significant reduction in radiation exposure with a mean reduction of 22% in DLP and 25% in CTDIvol.

CT Angiography of Venoarterial Extracorporeal Membrane Oxygenation.

Imaging plays a central role in the workup of thromboembolic events and bleeding complications in patients treated with venoarterial extracorporeal membrane oxygenation (ECMO) (VA-ECMO), and radiologists should be familiar with the expected hemodynamic changes and flow-related artifacts associated with the VA-ECMO system. VA-ECMO is a form of temporary mechanical circulatory support for critically ill patients with acute, refractory cardiac or cardiopulmonary failure. As the use of VA-ECMO continues to increase, it is important to be aware of associated hemodynamic changes and challenges at imaging. Patients treated with VA-ECMO are at high risk for thromboembolic events and bleeding complications and, thus, often require evaluation with CT angiography (CTA). VA-ECMO can be implemented by using central or peripheral cannulation. The peripheral femorofemoral VA-ECMO circuit in particular alters the sequence and direction of contrast medium enhancement substantially, resulting in flow-related artifacts that can mimic or obscure disease at CTA. Nonopacification can be mistaken for spurious thrombus or simulate complete vascular occlusion, while mixing artifacts can mimic dissections. Misinterpretation of flow-related CTA artifacts can lead to inappropriate surgical or medical intervention. A methodical and multiphasic approach should be taken to CTA imaging strategies and interpretation for patients treated with VA-ECMO. There is no universal CTA protocol for patients on VA-ECMO. Each protocol must be designed for the study indication, with consideration of the configuration of the ECMO cannulas, contrast material injection site, region of interest, native cardiac output, and ECMO flow rate. The authors provide examples of common and unusual VA-ECMO-related artifacts, with a focus on strategies for optimizing CTA image acquisition. Online supplemental material is available for this article. ©RSNA, 2021.

Thoracic Endovascular Aortic Repair for Chronic Type B Aortic Dissection: Pre- and Postprocedural Imaging.

Aortic dissection is a chronic disease that requires lifelong clinical and imaging surveillance, long after the acute event. Imaging has an important role in prognosis, timing of repair, device sizing, and monitoring for complications, especially in the endovascular therapy era. Important anatomic features at preprocedural imaging include the location of the primary intimal tear and aortic zonal and branch vessel involvement, which influence the treatment strategy. Challenges of repair in the chronic phase include a small true lumen in conjunction with a stiff intimal flap, complex anatomy, and retrograde perfusion from distal reentry tears. The role of thoracic endovascular aortic repair (TEVAR) remains controversial for treatment of chronic aortic dissection. Standard TEVAR is aimed at excluding the primary intimal tear to decrease false lumen perfusion, induce false lumen thrombosis, promote aortic remodeling, and prevent aortic growth. In addition to covering the primary intimal tear with an endograft, several adjunctive techniques have been developed to mitigate retrograde false lumen perfusion. These techniques are broadly categorized into false lumen obliteration and landing zone optimization strategies, such as the provisional extension to induce complete attachment (PETTICOAT), false lumen embolization, cheese-wire fenestration, and knickerbocker techniques. Familiarity with these techniques is important to recognize expected changes and complications at postintervention imaging. The authors detail imaging options, provide examples of simple and complex endovascular repairs of aortic dissections, and highlight complications that can be associated with various techniques. Online supplemental material is available for this article. ©RSNA, 2022.

An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude.

OBJECTIVES: Radiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. METHODS: Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. RESULTS: The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24-74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10-2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20-0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21-0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25-31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16-50.54, p < 0.001). CONCLUSIONS: Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. KEY POINTS: • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education.

OBJECTIVES: Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. METHODS: Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. RESULTS: The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). CONCLUSIONS: Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation. KEY POINTS: • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

Aortic Dissection and Other Acute Aortic Syndromes: Diagnostic Imaging Findings from Acute to Chronic Longitudinal Progression.

Acute aortic dissection is the prototype of acute aortic syndromes (AASs), which include intramural hematoma, limited intimal tear, penetrating atherosclerotic ulcer, traumatic or iatrogenic aortic dissection, and leaking or ruptured aortic aneurysm. The manifestation is usually sudden and catastrophic with acutely severe tearing chest or back pain. However, clinical symptoms do not allow distinction between AAS types and other acute pathologic conditions. Diagnostic imaging is essential to rapidly confirm and accurately diagnose the type, magnitude, and complications of AASs. CT fast acquisition of volumetric datasets has become instrumental in diagnosis, surveillance, and intervention planning. Most critical findings affecting initial intervention and prognosis are obtained at CT, including involvement of the ascending aorta, primary intimal tear location, rupture, malperfusion, size and patency of the false lumen, complexity and extent of the dissection, maximum caliber of the aorta, and progression or postintervention complications. Involvement of the ascending aorta-Stanford type A-has the most rapid lethal complications and requires surgical intervention to affect its morbidity and mortality. Lesions not involving the ascending aorta-Stanford type B-have a lesser rate of complications in the acute phase. During the acute to longitudinal progression, various specific and nonspecific imaging findings are encountered, including pleural and pericardial effusions, fluid collections, progression including aortic enlargement, and postoperative changes that can be discerned at CT. A systematic analysis algorithm is proposed for CT of the entire aorta throughout the continuum of AASs into the chronic and posttreated disease state, which synthesizes and communicates salient findings to all care providers. Online supplemental material is available for this article. ©RSNA, 2021.

Deep flow-net for EPI distortion estimation.

INTRODUCTION: Geometric distortions along the phase encoding direction caused by off-resonant spins are a major issue in EPI based functional and diffusion imaging. The widely used blip up/down approach estimates the underlying distortion field from a pair of images with inverted phase encoding direction. Typically, iterative methods are used to find a solution to the ill-posed problem of finding the displacement field that maps up/down acquisitions onto each other. Here, we explore the use of a deep convolutional network to estimate the displacement map from a pair of input images. METHODS: We trained a deep convolutional U-net architecture that was previously used to estimate optic flow between moving images to learn to predict the distortion map from an input pair of distorted EPI acquisitions. During the training step, the network minimizes a loss function (similarity metric) that is calculated from corrected input image pairs. This approach does not require the explicit knowledge of the ground truth distortion map, which is difficult to get for real life data. RESULTS: We used data from a total of Ntrain â€‹= â€‹22 healthy subjects to train our network. A separate dataset of Ntest â€‹= â€‹12 patients including some with abnormal findings and unseen acquisition modes, e.g. LR-encoding, coronal orientation) was reserved for testing and evaluation purposes. We compared our results to FSL's topup function with default parameters that served as the gold standard. We found that our approach results in a correction accuracy that is virtually identical to the optimum found by an iterative search, but with reduced computational time. CONCLUSION: By using a deep convolutional network, we can reduce the processing time to a few seconds per volume, which is significantly faster than iterative approaches like FSL's topup which takes around 10min on the same machine (but using only 1 CPU). This facilitates the use of a blip up/down scheme for all diffusion-weighted acquisitions and potential real-time EPI distortion correction without sacrificing accuracy.

Preparing Medical Imaging Data for Machine Learning.

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

Carotid Artery Imaging Is More Strongly Associated With the 10-Year Atherosclerotic Cardiovascular Disease Score Than Coronary Artery Imaging.

PURPOSE: The aim of this study was to compare coronary and carotid artery imaging and determine which one shows the strongest association with atherosclerotic cardiovascular disease (ASCVD) score. PATIENTS AND METHODS: Two separate series patients who underwent either coronary computed tomography angiography (CTA) or carotid CTA were included. We recorded the ASCVD scores and assessed the CTA imaging. Two thirds were used to build predictive models, and the remaining one third generated predicted ASCVD scores. The Bland-Altman analysis analyzed the concordance. RESULTS: A total of 110 patients were included in each group. There was no significant difference between clinical characteristics. Three imaging variables were included in the carotid model. Two coronary models (presence of calcium or Agatston score) were created. The bias between true and predicted ASCVD scores was 0.37 ± 5.72% on the carotid model, and 2.07 ± 7.18% and 2.47 ± 7.82% on coronary artery models, respectively. CONCLUSIONS: Both carotid and coronary artery imaging features can predict ASCVD score. The carotid artery was more associated to the ASCVD score than the coronary artery.

Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score.

PURPOSE: To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS: One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model. RESULTS: Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively. CONCLUSIONS: Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

Cardiopulmonary-induced deformations of the thoracic aorta following thoracic endovascular aortic repair.

OBJECTIVES: Thoracic endovascular aortic repair has become a preferred treatment strategy for thoracic aortic aneurysms and dissections. Yet, it is not well understood if the performance of endografts is affected by physiologic strain due to cyclic aortic motion during cardiac pulsation and respiration. We aim to quantify cardiac- and respiratory-induced changes of the postthoracic endovascular aortic repair thoracic aorta and endograft geometries. METHODS: Fifteen thoracic endovascular aortic repair patients (66 ± 10 years) underwent cardiac-resolved computed tomography angiographies during inspiratory/expiratory breath holds. The computed tomography angiography images were utilized to build models of the aorta, and lumen centerlines and cross-sections were extracted. Arclength and curvature were computed from the lumen centerline. Effective diameter was computed from cross-sections of the thoracic aorta. Deformation was computed from the mid-diastole to end-systole (cardiac deformation) and expiration to inspiration (respiratory deformation). RESULTS: Cardiac pulsation induced significant changes in arclength, mean curvature, maximum curvature change, and effective diameter of the ascending aorta, as well as effective diameter of the stented aortic segment. Respiration, however, induced significant change in mean curvature and effective diameter of the ascending aorta only. Cardiac-induced arclength change of the ascending aorta was significantly greater than respiratory-induced arclength change. CONCLUSIONS: Deformations are present across the thoracic aorta due to cardiopulmonary influences after thoracic endovascular aortic repair. The geometric deformations are greatest in the ascending aorta and decline at the stented thoracic aorta. Additional investigation is warranted to correlate aortic deformation to endograft performance.

Photon-counting CT: Technical Principles and Clinical Prospects.

Photon-counting CT is an emerging technology with the potential to dramatically change clinical CT. Photon-counting CT uses new energy-resolving x-ray detectors, with mechanisms that differ substantially from those of conventional energy-integrating detectors. Photon-counting CT detectors count the number of incoming photons and measure photon energy. This technique results in higher contrast-to-noise ratio, improved spatial resolution, and optimized spectral imaging. Photon-counting CT can reduce radiation exposure, reconstruct images at a higher resolution, correct beam-hardening artifacts, optimize the use of contrast agents, and create opportunities for quantitative imaging relative to current CT technology. In this review, the authors will explain the technical principles of photon-counting CT in nonmathematical terms for radiologists and clinicians. Following a general overview of the current status of photon-counting CT, they will explain potential clinical applications of this technology.

Assessing the Relationship Between American Heart Association Atherosclerotic Cardiovascular Disease Risk Score and Coronary Artery Imaging Findings.

OBJECTIVE: The aim of this study was to characterize the relationship between computed tomography angiography imaging characteristics of coronary artery and atherosclerotic cardiovascular disease (ASCVD) score. METHODS: We retrospectively identified all patients who underwent a coronary computed tomography angiography at our institution from December 2013 to July 2016, then we calculated the 10-year ASCVD score. We characterized the relationship between coronary artery imaging findings and ASCVD risk score. RESULTS: One hundred fifty-one patients met our inclusion criteria. Patients with a 10-year ASCVD score of 7.5% or greater had significantly more arterial segments showing stenosis (46.4%, P = 0.008) and significantly higher maximal plaque thickness (1.25 vs 0.53, P = 0.001). However, among 56 patients with a 10-year ASCVD score of 7.5% or greater, 30 (53.6%) had no arterial stenosis. Furthermore, among the patients with a 10-year ASCVD score of less than 7.5%, 24 (25.3%) had some arterial stenosis. CONCLUSIONS: There is some concordance but not a perfect overlap between 10-year ASCVD risk scores and coronary artery imaging findings.

Changes in Geometry and Cardiac Deformation of the Thoracic Aorta after Thoracic Endovascular Aortic Repair.

BACKGROUND: Thoracic endovascular aortic repair (TEVAR) has dramatically expanded treatment options for patients with thoracic aortic pathology. The interaction between endografts and the dynamic anatomy of the thoracic aorta is not well characterized for repetitive physiologic stressors and subsequent issues related to long-term durability. Through three-dimensional (3D) modeling we sought to quantify cardiac-induced aortic deformation before and after TEVAR to assess the impact of endografts on dynamic aortic anatomy. METHODS: Eight patients with acute (n = 4) or chronic (n = 3) type B dissections, or chronic arch aneurysm (n = 1), underwent TEVAR with a single (n = 5) or multiple (n = 3) Gore C-TAG(s). Cardiac-resolved thoracic CT images were acquired pre- and post-TEVAR. 3D models of thoracic aorta and branch vessels were constructed in systole and diastole. Axial length, mean, and peak curvature of the ascending aorta, arch, and stented lumens were computed from the aortic lumen centerline, delineated with branch vessel landmarks. Cardiac-induced deformation was computed from mid-diastole to end-systole. RESULTS: Pre-TEVAR, there were no significant cardiac-induced changes for aortic axial length or mean curvature. Post-TEVAR, the ascending aorta increased in axial length (2.7 ± 3.1%, P < 0.05) and decreased in mean curvature (0.38 ± 0.05 → 0.36 ± 0.05 cm-1, P < 0.05) from diastole to systole. From pre- to post-TEVAR, axial length change increased in the ascending aorta (P < 0.02), mean curvature decreased in the arch and stented aorta (P < 0.03), and peak curvature decreased in the stented aorta (P < 0.05). CONCLUSIONS: TEVAR for a range of indications not only causes direct geometric changes to the stented aorta but also results in dynamic changes to the ascending and stented aorta. In our cohort, endograft placement straightens the stented aorta and mutes cardiac-induced bending due to longitudinal stiffness. This is compensated by greater length and curvature changes from diastole to systole in the ascending aorta, relative to pre-TEVAR.

Lower extremity computed tomography angiography can help predict technical success of endovascular revascularization in the superficial femoral and popliteal artery.

OBJECTIVE: Preprocedural computed tomography angiography (CTA) assists in evaluating vascular morphology and disease distribution and in treatment planning for patients with lower extremity peripheral artery disease (PAD). The aim of the study was to determine the predictive value of radiographic findings on CTA and technical success of endovascular revascularization of occlusions in the superficial femoral artery-popliteal (SFA-pop) region. METHODS: Medical records and available imaging studies were reviewed for patients undergoing endovascular intervention for PAD between January 2013 and December 2015 at a single academic institution. Radiologists reviewed preoperative CTA scans of patients with occlusions in the SFA-pop region. Radiographic criteria previously used to evaluate chronic occlusions in the coronary arteries were used. Technical success, defined as restoration of inline flow through the SFA-pop region with <30% stenosis at the end of the procedure, and intraoperative details were evaluated. RESULTS: From 2013 to 2015, there were 407 patients who underwent 540 endovascular procedures for PAD. Preprocedural CTA scans were performed in 217 patients (53.3%), and 84 occlusions in the SFA-pop region were diagnosed. Ten occlusions were excluded as no endovascular attempt to cross the lesion was made because of extensive disease or concomitant iliac intervention. Of the remaining 74 occlusions in the SFA-pop region, 59 were successfully treated (80%) and 15 were unsuccessfully crossed (20%). The indications for revascularization were claudication in 57% of patients and critical limb ischemia in the remaining patients. TransAtlantic Inter-Society Consensus A, B, and C occlusions were treated with 87% success, whereas D occlusions were treated with 68% success (P = .047). There were nine occlusions with 100% vessel calcification that was associated with technical failure (P = .014). Longer lengths of occlusion were also associated with technical failure (P = .042). Multiple occlusions (P = .55), negative remodeling (P = .69), vessel runoff (P = .56), and percentage of vessel calcification (P = .059) were not associated with failure. On multivariable analysis, 100% calcification remained the only significant predictor of technical failure (odds ratio, 9.0; 95% confidence interval, 1.8-45.8; P = .008). CONCLUSIONS: Analysis of preoperative CTA shows 100% calcification as the best predictor of technical failure of endovascular revascularization of occlusions in the SFA-pop region. Further studies are needed to determine the cost-effectiveness of obtaining preoperative CTA for lower extremity PAD.

Myocardial Bridges on Coronary Computed Tomography Angiography　- Correlation With Intravascular Ultrasound and Fractional Flow Reserve.

BACKGROUND: Myocardial bridges (MB) are commonly seen on coronary CT angiography (CCTA) in asymptomatic individuals, but in patients with recurrent typical angina symptoms, yet no obstructive coronary artery disease (CAD), evaluation of their potential hemodynamic significance is clinically relevant. The aim of this study was to compare CCTA to invasive coronary angiography (ICA), including intravascular ultrasound (IVUS), to confirm MB morphology and estimate their functional significance in symptomatic patients.Methods and Results:We retrospectively identified 59 patients from our clinical databases between 2009 and 2014 in whom the suspicion for MB was raised by symptoms of recurrent typical angina in the absence of significant obstructive CAD on ICA. All patients underwent CCTA, ICA and IVUS. MB length and depth by CCTA agreed well with length (0.6±23.7 mm) and depth (CT coverage) as seen on IVUS. The product of CT length and depth (CT coverage), (MB muscle index (MMI)), ≥31 predicted an abnormal diastolic fractional flow reserve (dFFR) ≤0.76 with a sensitivity and specificity of 74% and 62% respectively (area under the curve=0.722). CONCLUSIONS: In patients with recurrent symptoms of typical angina yet no obstructive CAD, clinicians should consider dynamic ischemia from an MB in the differential diagnosis. The product of length and depth (i.e., MMI) by CCTA may provide some non-invasive insight into the hemodynamic significance of a myocardial bridge, as compared with invasive assessment with dFFR.