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This study aims to evaluate the feasibility and utility of virtual reality (VR) for baffle planning in congenital heart disease (CHD), specifically by creating patient-specific 3D heart models and assessing a user-friendly VR interface. Patient-specific 3D heart models were created using high-resolution imaging data and a VR interface was developed for baffle planning. The process of model creation and the VR interface were assessed for their feasibility, usability, and clinical relevance. Collaborative and interactive planning within the VR space were also explored. The study findings demonstrate the feasibility and usefulness of VR in baffle planning for CHD. Patient-specific 3D heart models generated from imaging data provided valuable insights into complex spatial relationships. The developed VR interface allowed clinicians to interact with the models, simulate different baffle configurations, and assess their impact on blood flow. The VR space's collaborative and interactive planning enhanced the baffle planning process. This study highlights the potential of VR as a valuable tool in baffle planning for CHD. The findings demonstrate the feasibility of using patient-specific 3D heart models and a user-friendly VR interface to enhance surgical planning and patient outcomes. Further research and development in this field are warranted to harness the full benefits of VR technology in CHD surgical management. [ABSTRACT FROM AUTHOR]

To evaluate subclinical cardiac dysfunction in student athletes after COVID-19 infection using feature tracking cardiac MRI strain analysis. Student athletes with history of COVID-19 infection underwent cardiac MRI as part of screening before return to competitive play. Subjects were enrolled if they had no or mild symptoms, normal cardiac MRI findings with no imaging evidence of myocarditis. Feature tracking strain analysis was performed using short and long axis cine MRI images of athletes and a separate cohort of healthy controls. Differences between the cardiac strain parameters were statistically analyzed by Mann-Whitney U test. The study cohort included 122 athletes (49 females, mean age 20 years ± 1.5 standard deviations) who had a history of COVID-19, and 35 healthy controls (24 females, mean age 34 years ± 18 standard deviations). COVID-19 positive athletes had normal physiologic cardiac adaptations, including significantly higher left and right ventricle end-diastolic volumes (p = 0.00001) when compared to healthy controls. There was no significant difference between biventricular ejection fraction between athletes and control subjects (p > 0.05). Cardiac MRI parameters, including left ventricle global longitudinal strain (LV-GLS), global circumferential strain (LV-GCS), and global radial strain (LV-GRS) values were normal but slightly lower in athletes compared to controls. LV-GCS and LV-GRS were significantly lower in athletes compared to controls (p = 0.007 and p = 0.005 respectively), but there was no significant difference for LV-GLS (p = 0.088). In this study of 122 athletes, there was no evidence of subclinical myocardial alterations following recovery from COVID-19 found on cardiac MRI strain analysis. When compared to healthy controls, the competitive athletes had higher end-diastolic volume indices and reduced, albeit normal, strain values of LV-GLS, LV-GCS, and LV-GRS. [ABSTRACT FROM AUTHOR]

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T 1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time. The non-linear function is realized using a convolutional neural networks (CNN) generator, while the CNN parameters, as well as the phase information, are estimated from the measured k-t space data. We use a dense conditional auto-encoder to estimate the cardiac and respiratory phases from the central multi-channel k-space samples acquired at each frame. The latent vectors of the auto-encoder are constrained to be bandlimited functions with appropriate frequency bands, which enables the disentanglement of the latent vectors into cardiac and respiratory phases, even when the data are acquired with intermittent inversion pulses. Once the phases are estimated, we pose the image recovery as the learning of the parameters of the CNN generator from the measured k-t space data. The learned CNN generator is used to generate synthetic data on demand by feeding it with appropriate latent vectors. The proposed approach capitalizes on the synergies between cine MRI and T 1 mapping to reduce the scan time and improve patient comfort. The framework also enables the generation of synthetic breath-held cine movies with different inversion contrasts, which improves the visualization of the myocardium. In addition, the approach also enables the estimation of the T 1 maps with specific phases, which is challenging with breath-held approaches. [ABSTRACT FROM AUTHOR]

Takotsubo cardiomyopathy (TC) is an acute but reversible non-ischemic heart failure syndrome. It is characterized by a transient form of ventricular dysfunction typically manifesting as basal hyperkinesis with hypokinesia and ballooning of left ventricle mid-cavity and apex. Imaging helps in both diagnosis and follow up. Echocardiogram is the first-line modality to assess the typical contractile dysfunction in suspected patients with catheter angiography showing normal coronary arteries. Cardiac MRI is currently the modality of choice for the non-invasive initial assessment of TC and for follow up imaging. The current review focusses on historical background of TC, its pathophysiology, diagnostic work up and differential diagnosis and provides multimodality imaging work up of TC including role of echocardiogram, invasive catheterization, nuclear imaging, cardiac computed tomography and cardiac MRI including basic and advanced MRI sequences. • Takotsubo cardiomyopathy (TC) is reversible syndrome mimicking acute coronary syndrome. • TC is typically characterized by basal hyperkinesis and apical akinesis. • Echocardiography and catheter angiography are often first line modality. • Cardiac MRI is modality of choice for the non-invasive initial assessment. • Cardiac MRI provides comprehensive evaluation for initial diagnosis and follow up. [ABSTRACT FROM AUTHOR]

Objectives: Despite the robust diagnostic performance of MRI-based radiomic features for differentiating between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) reported on prior studies, the best sequence or a combination of sequences and model performance across various machine learning pipelines remain undefined. Herein, we compare the diagnostic performance of multiple radiomics-based models to differentiate GBM from PCNSL. Methods: Our retrospective study included 94 patients (34 with PCNSL and 60 with GBM). Model performance was assessed using various MRI sequences across 45 possible model and feature selection combinations for nine different sequence permutations. Predictive performance was assessed using fivefold repeated cross-validation with five repeats. The best and worst performing models were compared to assess differences in performance. Results: The predictive performance, both using individual and a combination of sequences, was fairly robust across multiple top performing models (AUC: 0.961–0.977) but did show considerable variation between the best and worst performing models. The top performing individual sequences had comparable performance to multiparametric models. The best prediction model in our study used a combination of ADC, FLAIR, and T1-CE achieving the highest AUC of 0.977, while the second ranked model used T1-CE and ADC, achieving a cross-validated AUC of 0.975. Conclusion: Radiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences. Key Points: • Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably. • ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model. • Embedded feature selection models perform better than models using a priori feature reduction. [ABSTRACT FROM AUTHOR]

Side experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination. [ABSTRACT FROM AUTHOR]

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature. [ABSTRACT FROM AUTHOR]

Objectives: Computed tomography pulmonary angiogram (CTPA) is one of the most commonly ordered and frequently overused tests. The purpose of this study was to evaluate the mean radiation dose to patients getting CTPA and to identify factors that are associated with higher dose. Material and Methods: This institutionally approved retrospective study included all patients who had a CTPA to rule out acute pulmonary embolism between 2016 and 2018 in a tertiary care center. Patient data (age, sex, body mass index [BMI], and patient location), CT scanner type, image reconstruction methodology, and radiation dose parameters (dose-length product [DLP]) were recorded. Effective dose estimates were obtained by multiplying DLP by conversion coefficient (0.014 mSv·mGy-1·cm-1). Multivariate logistic regression analysis was performed to determine the factors affecting the radiation dose. Results: There were 2342 patients (1099 men and 1243 women) with a mean age of 58.1 years (range 0.2-104.4 years) and BMI of 31.3 kg/m² (range 12-91.5 kg/m²). The mean effective radiation dose was 5.512 mSv (median - 4.27 mSv; range 0.1-43.0 mSv). Patient factors, including BMI >25 kg/m², male sex, age >18 years, and intensive care unit (ICU) location, were associated with significantly higher dose (P < 0.05). CT scanning using third generation dual-source scanner with model-based iterative reconstruction (IR) had significantly lower dose (mean: 4.90 mSv) versus single-source (64-slice) scanner with filtered back projection (mean: 9.29 mSv, P < 0.001). Conclusion: Patients with high BMI and ICU referrals are associated with high CT radiation dose. They are most likely to benefit by scanning on newer generation scanner using advance model-based IR techniques. [ABSTRACT FROM AUTHOR]

This is a proof-of-concept study to create a four-dimensional (4-D) cine model of the heart and visualize it in virtual reality by using freely available open-source software and inexpensive hardware. Four-dimensional cine models allow for real-time visualization of cardiac structures during processes such as complex congenital heart disease. Such models can be used for patient and trainee education, and potentially for surgical planning. Currently, 3-D printed models are more commonly used, but they are static, showing only one selected phase of the cardiac cycle. Second, they are limited by the selection of clipping planes before printing. Four-dimensional segmentation and virtual reality visualization overcome these limitations. Currently, most of the work in virtual/augmented reality models involves the segmentation of one cardiac phase or the use of expensive software for multiphase segmentation. In this study, we show an approach for multiphase cardiac segmentation as well as its display using free open-source software and relatively inexpensive hardware. [ABSTRACT FROM AUTHOR]

Purpose: We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA). Methods: Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values. Results: Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51–0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001). Conclusion: Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure. [ABSTRACT FROM AUTHOR]

A cervical aortic arch is a rare vascular malformation that is characterized as a high positioned aortic arch, above the clavicle. The knowledge of its branching pattern is essential to characterize the entity further accurately. Noninvasive cross-sectional imaging, including computed tomography angiography or magnetic resonance angiography, is the imaging methods of choice. Due to highly complex anatomy, three-dimensional (3D) images help in providing improved anatomical visualization. Virtual reality is a relatively new computer-generated simulation technique that allows the interactive display of high-resolution models using a wearable headset and interactive controllers. We describe a rare form of a cervical arch and briefly discuss the latest methods of improved visualization using 3D virtual reality displays and smartphones. [ABSTRACT FROM AUTHOR]

The article presents a case study of 36-year-old man with Subclavian Arterial Aneurysms (SCA). Topics discussed include observation of fusiform bilateral SCA through magnetic resonance angiograms and computed tomographic imaging systems; presence of cerebral artery aneurysm in bifurcation prevalent region; and analysis of intracranial aneurysms relationship with aortic coarctation.

Simple Summary: Prior radiomic studies have addressed a two-class tumor classification problem (glioblastoma (GBM) versus primary CNS lymphoma (PCNSL) or GBM versus metastasis). However, this approach is prone to bias and excludes other common brain tumor types. We addressed a real-life clinical problem by including the three most common brain tumor types (GBM, PCNSL, and metastasis). We investigated two key issues using different MRI sequence combinations: performance variation based on tumor subregions (necrotic, enhancing, edema and combined enhancing, and necrotic masks), and performance metrics based on the chosen classifier model/feature selection combination. Our study provides evidence that radiomics-based three-class tumor differentiation is feasible, and that embedded models perform better than those with a priori feature selection. We found that T1 contrast enhanced is the single best sequence with comparable performance to that of multiparametric MRI, and model performance varies based on tumor subregion and the combination of model/feature selection methods. Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311–0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods. [ABSTRACT FROM AUTHOR]

The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions. [ABSTRACT FROM AUTHOR]

A case study is presented of a 60-year-old patient, magnetic resonance venography images revealed cystic lesion; computed tomographic imaging showed in-stent thrombosis; further the patient was treated with cystic adventitial disease involving external iliac vein.

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80–100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human. [ABSTRACT FROM AUTHOR]

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multi-slice free breathing and ungated cardiac MRI data from highly undersampled measurements. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions. [ABSTRACT FROM AUTHOR]

To explore the diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) to detect the primary tumor site in patients with extracervical metastases from carcinoma of unknown primary (CUP). We evaluated patient outcomes as overall survival (OS). In a single-center, retrospective study (2005–2019), patients with extracervical metastases from CUP underwent FDG PET/CT to detect primary tumor sites. The final diagnosis was based on histopathology/or clinical follow-up of at least 12 months. A total of 83 patients [Male 41 (49%), mean age 59 ± 14 years, range: 32–83 years] fulfilled the inclusion/exclusion criteria and were enrolled for analysis. The primary tumor was detected in 36 out of 83 (43%) patients based on histopathology/or clinical follow-up. PET/CT suggested the primary tumor site in 39 (47%) patients with diagnostic accuracy of 87%, sensitivity 89%, specificity 85%, PPV 82%, NPV 91% and detection rate 39%. Patients with oligometastases (<3) (2.16 years, 1.04–2.54) and primary unidentified (1 year, 0.34–2.14) had longer median survival time compared to the patients with multiple metastases (0.67 years, 0.17–1.58, p = 0.009) and primary identified (0.67 years,0.16–1.33, p = 0.002). The SUVmax of the primary or metastatic lesions with maximum uptake was not significantly related to survival. PET/CT could reveal the primary tumor site in 39% of the patients. It demonstrated the metastatic disease burden and distribution in patients with 'primary obscured', which directs management. Patients with multiple metastases and primary identified had a poorer prognosis. In patients with primary unidentified after PET/CT, a further search was futile. • 18FDG PET/CT could reveal the primary tumor site in greater than one-third of the patients with good diagnostic performance. • In most patients where FDG PET/CT could not find the primary origin, the primary tumor's further search was futile. • The overall survival was lower in patients with multiple metastatic lesions or primary identified. [ABSTRACT FROM AUTHOR]

A 44‑year‑old male was referred to our clinic (2015) to evaluate multiple lung nodules with increasing fatigue, dyspnea, and weight loss. He was being assessed to an outside hospital for the same since 2010. The X‑ray and computed‑tomography (CT)‑chest showed numerous pulmonary nodules and bilateral hilar adenopathy. Imaging workup at our institute (2015) redemonstrated extensive calcified pulmonary nodules. 18fluoro‑2‑deoxy‑d‑glucose positron emission tomographyCT showed widespread pulmonary nodules with low‑grade uptake. Video‑assisted thoracic surgery lung biopsy revealed pulmonary hyalinizing granuloma (PHG). Recently because of increasing symptoms, he is being evaluated for a lung transplant. This case represents a rare diagnosis of PHG with a decade follow‑up. [ABSTRACT FROM AUTHOR]

Posterior reversible encephalopathy syndrome (PRES) is a neurotoxic state often characterized by altered mental state and is seen in various clinical settings. Although it is often reversible, it may result in long term sequelae. The typical and atypical neuroimaging findings seen in PRES have been previously well-described in the literature. However, the presence of other co-existing intracranial lesions along with PRES can occasionally confound the imaging findings and poses a diagnostic challenge. Herein, the authors report four cases of PRES with etiologically different co-existing intracranial lesions confounding the clinical and imaging manifestations. When presented with atypical imaging findings in PRES, the possibility of a co-existing intracranial pathology with superimposed imaging findings should be considered. • PRES is a well-recognized entity presenting with acute symptoms such as encephalopathy, headache, and visual disturbances. • PRES can be co-existing with other intracranial pathologies complicationg the neuroimaging picture • Differentiation of a PRES from co-existing pathology may be feasible and help guide appropriate therapy. • This case series presents four such cases where PRES was present along with other intracranial patholgies. [ABSTRACT FROM AUTHOR]

The aortic pathologies are well recognized on imaging. However, conventionally cardiac and proximal aortic abnormalities were only seen on dedicated cardiac or aortic studies due to need for ECG gating. Advances in CT technology have allowed motionless imaging of the chest and abdomen, leading to an increased visualization of cardiac and aortic root diseases on non-ECG-gated imaging. The advances are mostly driven by high pitch due to faster gantry rotation and table speed. The high-pitch scans are being increasingly used for variety of clinical indications because the images are free of motion artifact (both breathing and pulsation) as well as decreased radiation dose. Recognition of aortic root pathologies may be challenging due to lack of familiarity of radiologists with disease spectrum and their imaging appearance. It is important to recognize some of these conditions as early diagnosis and intervention is key to improving prognosis. We present a comprehensive review of proximal aortic anatomy, pathologies commonly seen at the aortic root, and their imaging appearances to familiarize radiologists with the diseases of this location. [ABSTRACT FROM AUTHOR]