"From Vision to Reality: Virtual Reality's Impact on Baffle Planning in Congenital Heart Disease".

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.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging.

INTRODUCTION: Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY: A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS: For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS: When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

PURPOSE: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL). METHODOLOGY: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC. RESULTS: Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]). CONCLUSION: Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

Prevalence and significance of incidental findings on computed tomography pulmonary angiograms: A retrospective cohort study.

OBJECTIVE: The purpose of this study was to analyze the prevalence and significance of incidental findings on computed tomography pulmonary angiography (CTPA) studies and to assess the diagnostic yield of CTPA in identifying an alternate diagnosis to pulmonary embolism (PE) on PE negative exams. METHODS: All patients who had a CTPA exam for PE evaluation between Jan 2016 and Dec 2018 with a negative PE result were included in the study. A total of 2083 patients were identified. We retrospectively queried the electronic medical record and the radiology report and recorded the following: Age, Sex, BMI, Patient location and Incidental findings. The incidental findings were classified into type 1 (Alternate diagnosis other than PE which could explain the patient's symptoms), type 2 (non-emergent findings which needed further work up) and type 3 findings (non-emergent findings which did not need further work up). Logistic regression analysis was performed to determine what factors affected the probability of finding a type 1 incidental (alternate diagnosis) or a type 2 incidental. RESULTS: 74.5% of the patients in our study had at least one incidental finding. Type 1 incidental findings (alternate diagnosis to PE) were found in 864 patients (41.5%). The most common type 1 finding was pneumonia followed by fluid overload. Male sex, increased age and lower BMI were significantly associated with increased odds of a type 1 incidental(p < 0.05). Similarly, all the patient locations had significantly different odds of finding a type-1 incidental, with ICU having the highest odds, followed by inpatient, ED and outpatient locations (p < 0.05). 563 patients (27%) had at least one type 2 incidental findings and the most common type 2 findings were progressive lung malignancy/ metastatic disease and new pulmonary nodule. Increased age was significantly associated with the probability of a type 2 finding (p < 0.05). CONCLUSIONS: CTPA may suggest an alternative diagnosis to pulmonary embolism in approximately 40% of the patients with a negative study. The probability of finding an alternate diagnosis (type 1 incidental) is higher in elderly patients and in patients referred from ICU and inpatient units.

Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke.

OBJECTIVES: Automated image-level detection of large vessel occlusions (LVO) could expedite patient triage for mechanical thrombectomy. A few studies have previously attempted LVO detection using artificial intelligence (AI) on CT angiography (CTA) images. To our knowledge this is the first study to detect LVO existence and location on raw 4D-CTA/ CT perfusion (CTP) images using neural network (NN) models. MATERIALS AND METHODS: Retrospective study using data from a level-I stroke center was performed. A total of 306 (187 with LVO, and 119 without) patients were evaluated. Image pre-processing included co-registration, normalization and skull stripping. Five consecutive time-points for each patient were selected to provide variable contrast density in data. Additional data augmentation included rotation and horizonal image flipping. Our model architecture consisted of two neural networks, first for classification (based on hemispheric asymmetry), followed by second model for exact site of LVO detection. Only cases deemed positive by the classification model were routed to the detection model, thereby reducing false positives and improving specificity. The results were compared with expert annotated LVO detection. RESULTS: Using a 80:20 split for training and validation, the combination of both classification and detection model achieved a sensitivity of 86.5%, a specificity of 89.5%, and an accuracy of 87.5%. A 5-fold cross-validation using the entire data achieved a mean sensitivity of 82.7%, a specificity of 89.8%, and an accuracy of 85.5% and a mean AUC of 0.89 (95% CI: 0.85-0.93). CONCLUSION: Our findings suggest that accurate image-level LVO detection is feasible on CTP raw images.

CT Perfusion Maps Improve Detection of M2-MCA Occlusions in Acute Ischemic Stroke.

OBJECTIVES: Middle cerebral artery occlusions, particularly M2 branch occlusions are challenging to identify on CTA. We hypothesized that additional review of the CTP maps will increase large vessel occlusion (LVO) detection accuracy on CTA and reduce interpretation time. MATERIALS AND METHODS: Two readers (R1 and R2) retrospectively reviewed the CT studies in 99 patients (27 normal, 26 M1-MCA, 46 M2-MCA occlusions) who presented with suspected acute ischemic stroke (AIS). The time of interpretation and final diagnosis were recorded for the CTA images (derived from CTP data), both without and with the CTP maps. The time for analysis for all vascular occlusions was compared using McNemar tests. ROC curve analysis and McNemar tests were performed to assess changes in diagnostic performance with the addition of CTP maps. RESULTS: With the addition of the CTP maps, both readers showed increased sensitivity (p = 0.01 for R1 and p = 0.04 for R2), and accuracy (p = 0.02 for R1 and p = 0.004 for R2) for M2-MCA occlusions. There was a significant improvement in diagnostic performance for both readers for detection of M2-MCA occlusions (AUC R1 = 0.86 to 0.95, R2 = 0.84 to 0.95; p < 0.05). Both readers showed reduced interpretation time for all cases combined, as well as for normal studies (p < 0.001) when CTP images were reviewed along with CTA. Both readers also showed reduced interpretation time for M2-MCA occlusions, which was significant for one of the readers (p < 0.02). CONCLUSION: The addition of CTP maps improves accuracy and reduces interpretation time for detecting LVO and M2-MCA occlusions in AIS. Incorporation of CTP in acute stroke imaging protocols may improve detection of more distal occlusions.

Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.

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.

Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose.

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.

Pulmonary embolism rule out: positivity and factors affecting the yield of CT angiography.

OBJECTIVE: CT pulmonary angiography (CTPA) is one of the most commonly ordered CT imaging tests. It is often believed to be overutilised with few recent studies showing a yield of less than 2%. This study aimed to determine the overall positivity rate of CTPA examinations and understand the factors that affect the yield of the CTPA examination. METHODS: We retrospectively analysed 2713 patients who received the CTPA exam between 2016 and 2018. Type of study ordered (CTPA chest or CTPA chest with abdomen and pelvis CT), patient location (emergency department (ED), outpatient, inpatient, intensive care unit (ICU)) and patient characteristics-age, sex and body mass index (BMI) were recorded. A logistic regression analysis was performed to determine what factors affect the positivity rate of CT scans for pulmonary embolism (PE). RESULTS: With 296 positive test results, the overall CTPA positivity was 10.9%. Male sex was associated with higher CTPA positivity, gender difference was maximum in 18-year to 35-year age group. Overweight and obese patients had significantly higher positivity as compared with BMI<25 (p<0.05). Higher positivity rate was seen in the BMI 25-40 group (11.9%) as compared with BMI>40 (10.1%) (p<0.05). Significant difference (p<0.001) was also found in CTPA examination yield from ICU (15.3%) versus inpatients (other than ICU) (12.4%) versus ED (9.6%), and outpatients (8.5%). The difference in CTPA yield based on the type of CT order (CTPA chest vs CTPA chest with CT abdomen and pelvis), patient's age and sex was not significant. CONCLUSION: CTPA yield of 10.9% in this study is comparable to acceptable positivity rate for the USA and is higher than recent studies showing positivity of <2%. Patient characteristics like obesity and ICU or inpatient location are associated with higher rate of CT positivity.

Four-dimensional virtual reality cine cardiac models using free open-source software.

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.

Neurocristopathies: Enigmatic Appearances of Neural Crest Cell-derived Abnormalities.

The neural crest is an important transient structure that develops during embryogenesis in vertebrates. Neural crest cells are multipotent progenitor cells that migrate and develop into a diverse range of cells and tissues throughout the body. Although neural crest cells originate from the ectoderm, they can differentiate into mesodermal-type or endodermal-type cells and tissues. Some of these tissues include the peripheral, autonomic, and enteric nervous systems; chromaffin cells of the adrenal medulla; smooth muscles of the intracranial blood vessels; melanocytes of the skin; cartilage and bones of the face; and parafollicular cells of the thyroid gland. Neurocristopathies are a group of diseases caused by the abnormal generation, migration, or differentiation of neural crest cells. They often involve multiple organ systems in a single person, are often familial, and can be associated with the development of neoplasms. As understanding of the neural crest has advanced, many seemingly disparate diseases, such Treacher Collins syndrome, 22q11.2 deletion syndrome, Hirschsprung disease, neuroblastoma, neurocutaneous melanocytosis, and neurofibromatosis, have come to be recognized as neurocristopathies. Neurocristopathies can be divided into three main categories: dysgenetic malformations, neoplasms, and combined dysgenetic and neoplastic syndromes. In this article, neural crest development, as well as several associated dysgenetic, neoplastic, and combined neurocristopathies, are reviewed. Neurocristopathies often have clinical manifestations in multiple organ systems, and radiologists are positioned to have significant roles in the initial diagnosis of these disorders, evaluation of subclinical associated lesions, creation of treatment plans, and patient follow-up. Online supplemental material is available for this article. ©RSNA, 2019.

Posttraumatic Renal Artery-Inferior Vena Cava Fistula-Induced High-Output Cardiac Failure: A Case Study.

High-output cardiac failure is a less prevalent form of heart failure. Most patients with heart failure are typically categorized as having either systolic or diastolic dysfunction with elevated systemic vascular resistance. Individuals with high-output cardiac failure exhibit normal cardiac function and decreased systemic vascular resistance. This reduction may stem from diffuse arteriolar dilation or potential bypass of arterioles and capillary beds, prompting the activation of neurohormones. This case report details the diagnosis and treatment of an unusual etiology of high-output cardiac failure involving an arteriovenous fistula connecting the renal artery to the inferior vena cava and right common iliac vessels, resulting in a left-to-right shunt in a 50-year-old male patient. The report explores the etiology, pathophysiology, and clinical presentation of high-output heart failure, emphasizing the crucial role of radiology in interprofessional teams.

Diaphragmatic Castleman's disease: A rare lymphoproliferative disorder: Clinical and radiological perspectives.

Castleman's disease (CD) is a rare, benign nonclonal lymphoproliferative disorder with an unclear etiology, presenting significant diagnostic challenges due to its nonspecific features. CD is categorized into unicentric (UCD) and multicentric (MCD) types, with MCD further divided into HHV-8-associated and idiopathic (iMCD) forms. Clinical manifestations include fever, weight loss, night sweats, and organomegaly, with specific symptoms depending on the subtype. Diagnostic criteria for CD involve a combination of major criteria-histopathologic examination and minor criteria. Imaging techniques, including CT, MRI, and PET-CT, play a crucial role in diagnosis, staging, and differentiation from other diseases. This paper discusses the pathophysiology, clinical features, diagnostic criteria, and imaging findings of CD, illustrated by a case of a patient with renal disease with incidentally detected a right cardiophrenic mass. The case highlights the importance of comprehensive imaging and clinical evaluation in managing CD.

Computed Tomography Angiography for Aortic Diseases.

Aortic pathologies encompass a heterogeneous group of disorders, including acute aortic syndrome, traumatic aortic injury , aneurysm, aortitis, and atherosclerosis. The clinical manifestations of these disorders can be varied and non-specific, ranging from acute presentations in the emergency department to chronic incidental findings in an outpatient setting. Given the non-specific nature of their clinical presentations, the reliance on non-invasive imaging for screening, definitive diagnosis, therapeutic strategy planning, and post-intervention surveillance has become paramount. Commonly used imaging modalities include ultrasound, computed tomography (CT), and MR imaging. Among these modalities, computed tomography angiography (CTA) has emerged as a first-line imaging modality owing to its excellent anatomic detail, widespread availability, established imaging protocols, evidence-proven indications, and rapid acquisition time.

Pulmonary Artery Stenosis After an Orthotopic Heart Transplantation: A Case Report With Cardiac Imaging Findings and a Literature Review.

Pulmonary artery stenosis is a rare complication of heart transplantation. It is typically a congenital condition or can be secondary to rheumatic fever, systemic vasculitis like Behcet's disease, or Takayasu's arteritis. It can also occur as a rarity of a delayed complication post-heart transplant. In this report, we describe the imaging findings of pulmonary artery stenosis in a patient who underwent an orthotopic heart transplant more than 10 years prior. Dynamic cardiac magnetic resonance imaging (MRI), phase contrast imaging, and MR angiography in the management of pulmonary artery stenosis helped in heart and pulmonary circulation. Functional evaluation can be achieved with current multichannel transmit-receive coils. Cardiac gated pre- and dynamic contrast-enhanced MR was performed with phase-contrast imaging for further evaluation confirming the diagnosis of pulmonary artery stenosis.

Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex. MATERIALS AND METHODS: In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots). RESULTS: The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation. CONCLUSION: Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation.

Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults.

RATIONALE AND OBJECTIVES: The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-specific strain values in healthy children and young adults using a deformable registration algorithm (DRA). MATERIALS AND METHODS: A retrospective study included 131 healthy children and young adults (62 males and 69 females) with a mean age of 16.6 ± 3.9 years. CMR examinations were conducted using 1.5T scanners, and strain analysis was performed using TrufiStrain research prototype software (Siemens Healthineers, Erlangen, Germany). Global and layer-specific strain parameters were extracted from balanced Steady-state free precession cine images. Statistical analyses were conducted to evaluate the impact of demographic variables on strain measurements. RESULTS: The peak global longitudinal strain (LS) was -16.0 ± 3.0%, peak global radial strain (RS) was 29.9 ± 6.3%, and peak global circumferential strain (CS) was -17.0 ± 1.8%. Global LS differed significantly between males and females. Transmural strain analysis showed a consistent pattern of decreasing LS and CS from endocardium to epicardium, while radial strain increased. Basal-to-apical strain distribution exhibited decreasing LS and increasing CS in both global and layer-specific analysis. CONCLUSION: This study uses DRA to provide reference values for global and layer-specific strain in healthy children and young adults. The study highlights the impact of sex and age on LS and body mass index on RS. These insights are vital for future cardiac assessments in children, particularly for early detection of heart diseases.