Introduction: Changes in cardiac and cerebral blood flow (CBF) with aging may contribute to stroke and other cerebrovascular diseases. How cardiac flow affects CBF with age is not yet well understood. This research investigates aortic blood flow and CBF in healthy volunteers of different ages and studies the transitional change from young to older age. The purpose is to identify possible age-related differences in the heart and brain flow relationship. Methods: 15 volunteers (10 male, 5 female) from ages 20 to 44 were split into two groups, the younger 23.1 ± 2.8 and the older 38.5 ± 5.7 years (mean ± std). 4D Flow MRI images were sequentially acquired for the heart and brain for each volunteer using a Siemens 3.0 T scanner. An ECG-gated compressed sensing protocol (R=7.7) was used with a 1.4 mm and 2 mm voxel size for the brain and heart. The cerebral arteries near the Circle of Willis and thoracic aorta were imaged. 4D Flow MRI data were analyzed using Caas MR 4D Flow (Pie Medical Imaging BV, Maastricht, The Netherlands). We compared arterial volumetric flow and blood flow pulsatility over the cardiac cycle using pulsatility index (PI). Statistical analysis used Pearsons’ correlation coefficient (r). Results: In both groups, blood flows in different cerebral arteries were positively correlated (r > 0.5). Flow rates in the ICA and MCA arteries were highly correlated (r = 0.82), while the correlation decreased with distance (e.g. PCA and ACA, r = 0.41). Both groups showed a negative correlation between age and CBF, suggesting that brain blood flow decreases as people age. This effect was stronger among the older group (r = -0.58) than the younger group (r = -0.34). A negative correlation between CBF and cerebral PI was also observed in both groups. Unlike blood flow in the brain, which showed the same relationship trend in young and older groups, the relationship between aorta blood flow and CBF differed between the two groups. Among the younger group, aorta flow was not correlated with CBF (r = -0.09) and was correlated with cerebral PI (r = 0.3). The older group showed the opposite relationship; correlated with CBF (r = 0.55), and not with cerebral PI (r < 0.01). Conclusion: We observed correlations between CBF and cerebral PI in brain arteries. The correlation between blood flow in the aorta and CBF differed among the two age groups. These differences may reflect aging-related changes in the cardiovascular system, such as arterial stiffening.

Introduction: Larger intracranial aneurysms have a higher risk of rupture. Consistent with this, multiple studies have found that aneurysm growth during follow-up is a strong risk factor for rupture. In this study, we hypothesize that growing aneurysms may have different hemodynamic properties than their counterpart of stable aneurysms. To minimize errors in growth definition due to image resolution, we designed a study and performed a comprehensive review of over 20 years of single-image modality aneurysm data. We analyzed and investigated the hemodynamic differences between pairs of growing and stable aneurysms closely matched according to size and location. Methods: 257 3D CTA image studies were reviewed to study matched ICA aneurysms. Seventeen growing, saccular ICA aneurysms with imaging follow-up and without intervening treatment were identified from electronic medical records, and matching stable ICA aneurysms were found for each. Specifically, the initial diameter of aneurysm was matched within 10% and locations were matched in areas of ophthalmic arteries, the superior hypophyseal arteries, or the posterior communicating arteries. For each aneurysm, one initial and two follow-up 3D images were collected. The mean aneurysm follow-up duration was 4.10 ± 1.98 years. Blood flow within the ICA and aneurysm was modeled with a pulsatile computational fluid dynamics simulation using the 3D Navier-Stokes equations. Fisher’s exact test was used for categorical data, and the Wilcoxon signed-rank test was used to compare hemodynamics between matched samples. Results: Among aneurysm characteristics and patient history, the only significant factors distinguishing the growing and stable groups were bifurcation aneurysms (p = 0.04) and a history of smoking (p = 0.02). Paired difference testing indicated some hemodynamic differences between the two groups were consistent over follow-up. Specifically, flow pulsatility index (PI) varied in the aneurysm body (p = 0.009) and within the ICA immediately after the aneurysm (p = 0.008). Conclusion: Closely pairing growing and stable aneurysms eliminates confounding variables related to size and location. This study indicates that even under such conditions, hemodynamic differences exist. Local hemodynamics may play an important role in continued aneurysm growth.

Background/Objectives: Duchenne Muscular Dystrophy (DMD) is a prevalent fatal genetic disorder, and heart failure is the leading cause of mortality. Peak left ventricular (LV) circumferential strain (Ecc), twist, and circumferential-longitudinal shear angle (θCL) are promising biomarkers for the improved and early diagnosis of incipient heart failure. Our goals were as follows: 1) to characterize a spectrum of functional and rotational LV biomarkers in boys with DMD compared with healthy age-matched controls; and 2) to identify LV biomarkers of early cardiomyopathy in the absence of abnormal LVEF or LGE. Methods: Boys with DMD (N = 43) and age-matched healthy volunteers (N = 16) were prospectively enrolled and underwent a 3T CMR exam after obtaining informed consent. Breath-held MRI tagging was used to estimate left ventricular Ecc at the mid-ventricular level as well as the twist, torsion, and θCL between basal and apical LV short-axis slices. A two-tailed t-test with unequal variance was used to test group-wise differences. Multiple comparisons were performed with Holm–Sidak post hoc correction. Multiple-regression analysis was used to test for correlations among biomarkers. A binomial logistic regression model assessed each biomarker’s ability to distinguish the following: (1) healthy volunteers vs. DMD patients, (2) healthy volunteers vs. LGE(−) DMD patients, and (3) LGE(−) DMD patients vs. LGE(+) DMD patients. Results: There was a significant impairment in the peak mid-wall Ecc [−17.0 ± 4.2% vs. −19.5 ± 1.9%, p < 7.8 × 10−3], peak LV twist (10.4 ± 4.3° vs. 15.6 ± 3.1°, p < 8.1 × 10−4), and peak LV torsion (2.03 ± 0.82°/mm vs. 2.8 ± 0.5°/mm, p < 2.6 × 10−3) of LGE(−) DMD patients when compared to healthy volunteers. There was a further significant reduction in the Ecc, twist, torsion, and θCL for LGE(+) DMD patients when compared to LGE(−) DMD patients. In the LGE(+) DMD patients, age significantly correlated with LVEF (r2 = 0.42, p = 9 × 10−3), peak mid-wall Ecc (r2 = 0.27, p = 0.046), peak LV Twist (r2 = 0.24, p = 0.06), peak LV torsion (r2 = 0.28, p = 0.04), and peak LV θCL (r2 = 0.23, p = 0.07). In the LGE(−) DMD patients, only the peak mid-wall Ecc was significantly correlated with age (r2 = 0.25, p = 0.006). The peak LV twist outperformed the peak mid-wall LV Ecc and EF in distinguishing DMD patients from healthy volunteer groups (AUC = 0.88, 0.80, and 0.72), as well as in distinguishing LGE(−) DMD patients from healthy volunteers (AUC = 0.83, 0.74, and 0.62). The peak LV twist and peak mid-wall LV Ecc performed similarly in distinguishing the LGE(−) and LGE(+) DMD cohorts (AUC = 0.74, 0.77, and 0.79). Conclusions: The peak mid-wall LV Ecc, peak LV twist, peak LV torsion, and peak LV θCL were significantly impaired in advance of the decreased LVEF and the development of focal myocardial fibrosis in boys with DMD and therefore were apparent prior to significant irreversible injury.

DEGs between subsolid nodule and nLung C20 NKT cells

Consistent and reliable data in computed tomography (CT) is vital for developing generalizable AI algorithms for downstream tasks. However, differences in CT acquisition and reconstruction parameters lead to inconsistencies in image quality and characteristics, impacting AI model performance. We introduce CT-Norm, an open-source toolkit addressing CT variability through three primary modules: Data Characterization, Data Harmonization, and Robustness Analysis. Each module targets a distinct phase of the data understanding pipeline. The data characterization module identifies voxel-level and DICOM header-level variability for a given dataset and generates a summary report. The data harmonization module provides a solution to mitigate the identified variability. It offers a library of image-level harmonization methods, ranging from traditional image processing to convolutional neural networks (CNNs) and generative adversarial networks (GANs). This module is validated on an external test set from the TCIA low-dose CT (LDCT) dataset using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). Without the harmonization module, the observed PSNR, SSIM, and LPIPS were 19.77 +/- 0.368, 0.33 +/- 0.010, and 0.43 +/- 0.012, respectively, which increased to 26.51 +/- 0.281 and 0.57 +/- 0.011 for PSNR and SSIM, while LPIPS decreased to 0.30 +/- 0.004 after applying CNN-based harmonization. The robustness analysis module allows developers to evaluate the sensitivity of an existing deep-learning model across the identified variability within a dataset. This allows developers to identify the imaging conditions under which a model performs optimally and those where it may struggle, enabling them to optimize the model further and enhance its reliability. CT-Norm enables model developers and end users to understand data variability, harmonize inconsistencies, and optimize AI models for robust and generalizable performance.

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease and imaging plays a key role in diagnosis, pre-operative planning and follow up, with MRI as the gold standard for imaging in these patients despite echocardiography being more widely available. While static MRI sequences are suitable for evaluation of anatomical structures, dynamic imaging is required for volume and flow measurements through valves, chambers, and surgical conduits. Newer techniques with 4D data acquisitions allow for feasible 2D cine reconstruction in desired planes. Ferumoxytol, a blood pool contrast agent with long intravascular half-life, facilitates acquisition of 4D flow and 4D MUSIC (multiphase, steady-state imaging with contrast) sequences, eliminating need for repeated contrast administrations. In this article we review conglomerate of TOF anomalies, their historical and current surgical managements with respective devices, as well as cutting-edge MRI techniques for their evaluation.

Introduction: Duchenne muscular dystrophy (DMD) is fatal X-linked neuromuscular disorder characterized by progressive dilated cardiomyopathy, respiratory insufficiency, and autonomic dysfunction. Cardiac, pulmonary, and autonomic function are thought to decline with disease progression in a co-dependent manner. However, these relationships have not been systematically evaluated. Right ventricular health, which depends on normal pulmonary hemodynamics, also remains understudied in DMD. The objective of this study was to characterize the relationships of pulmonary function test (PFT) parameters and indices of autonomic function, with cardiac magnetic resonance imaging (CMR) biomarkers of biventricular function and remodeling in children and adolescents with DMD. Methods: We performed a prospective analysis of 27 boys with DMD who underwent CMR, PFT, and 24-hour ambulatory electrocardiogram (aECG) monitoring at two children’s hospitals to evaluate cardiac, pulmonary, and autonomic function, respectively. The CMR protocol included conventional biventricular volumetric and functional assessment, late gadolinium enhancement (LGE) imaging to detect focal myocardial fibrosis, and T1 mapping to assess diffuse fibrosis. PFTs were performed per institutional protocols and included spirometry and respiratory muscle strength testing. Average heart rate and the standard deviation of the time between normal heartbeats (SDNN) were obtained from aECG. The cohort was stratified based on presence of LGE and predicted forced vital capacity (FVC) <80%, both suggestive of more advanced disease. Results: Median age of the cohort was 13 years (IQR 11-15.5 years). 8 patients were LGE (+) and 19 were LGE (-). LGE (+) boys had significantly lower percent predicted maximum expiratory pressure (MEP%) (25.8 vs. 48.0, p=0.035). Other respiratory, autonomic, and right ventricular function indices did not correlate with LGE status. There were no significant differences in CMR or autonomic parameters between boys with normal (FVC ≥80%) and abnormal (FVC <80%) pulmonary function. Conclusion: Our findings suggest that cardiac, pulmonary, and autonomic function may decline independently with disease progression; dysfunction in one system did not necessarily correlate with dysfunction in the other. Decline in respiratory muscle strength, as measured by MEP%, was seen more often in patients with myocardial scarring (indicative of more advanced disease). Further longitudinal investigation involving prospective modulation of respiratory support during CMR may elucidate more subtle cardiopulmonary-autonomic interactions in DMD.

Fast radial-MRI approaches based on compressed sensing (CS) and deep learning (DL) often use non-uniform fast Fourier transform (NUFFT) as the forward imaging operator, which might introduce interpolation errors and reduce image quality. Using the polar Fourier transform (PFT), we developed fully polar CS and DL algorithms for fast 2D cardiac radial-MRI. Our methods directly reconstruct images in polar spatial space from polar k-space data, eliminating frequency interpolation and ensuring an easy-to-compute data consistency term for the DL framework via the variable splitting (VS) scheme. Furthermore, PFT reconstruction produces initial images with fewer artifacts in a reduced field of view, making it a better starting point for CS and DL algorithms, especially for dynamic imaging, where information from a small region of interest is critical, as opposed to NUFFT, which often results in global streaking artifacts. In the cardiac region, PFT-based CS technique outperformed NUFFT-based CS at acceleration rates of 5x (mean SSIM: 0.8831 vs. 0.8526), 10x (0.8195 vs. 0.7981), and 15x (0.7720 vs. 0.7503). Our PFT(VS)-DL technique outperformed the NUFFT(GD)-based DL method, which used unrolled gradient descent with the NUFFT as the forward imaging operator, with mean SSIM scores of 0.8914 versus 0.8617 at 10x and 0.8470 versus 0.8301 at 15x. Radiological assessments revealed that PFT(VS)-based DL scored 2.9±0.30 and 2.73±0.45 at 5x and 10x, whereas NUFFT(GD)-based DL scored 2.7±0.47 and 2.40±0.50, respectively. Our methods suggest a promising alternative to NUFFT-based fast radial-MRI for dynamic imaging, prioritizing reconstruction quality in a small region of interest over whole image quality.

Early-stage non-small cell lung cancer (NSCLC) patients have a relatively high recurrence rate within the first five years of surgery, reflecting a need to predict post-surgical recurrence and offer personalized adjuvant therapies. Quantitative features extracted from radiology and pathology images can provide valuable information for the NSCLC recurrence prediction task, with radiomic features capturing global tumor phenotypes and pathomic features capturing local cellular and tumor microenvironment information. In this study, we propose to combine radiomic and pathomic features to predict progression-free survival within five years of curative resection in early-stage lung adenocarcinoma (LUAD), the most common subtype of NSCLC. Using 106 cases from the National Lung Screening Trial dataset, we extracted radiomic features from lung nodules on pre-surgery computed tomography (CT) scans guided by radiologist's segmentation and pathomic features from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of the resected tissue. We leveraged both hand-crafted and deep features in each modality and used a Cox proportional hazards model. Models were trained with 5-fold cross-validation with ten repetitions, and metrics such as the concordance index (C-index) were calculated by the mean performance on the test set. The fused model using combined radiomic and pathomic features has a C-index of 0.634. Our study shows that combining radiomic and pathomic features results in a more accurate progression-free survival prediction model as compared to only using radiomic features (C-index=0.612), pathomic features (C-index=0.584), or clinical features (C-index= 0.477).

Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.

CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.

Tobacco smoking is the leading cause of preventable deaths in the United States. Beyond the risks of cardiovascular disease and several cancers, smoking contributes to lung inflammation, lung destruction, and smoking-related interstitial lung diseases (SRILD). SRILD describes a broad range of conditions that includes respiratory bronchiolitis, respiratory bronchiolitis-associated interstitial lung disease, desquamative interstitial pneumonia, pulmonary Langerhans cell histiocytosis, acute eosinophilic pneumonia, idiopathic pulmonary fibrosis, combined pulmonary fibrosis and emphysema, interstitial lung abnormality, and smoking-related interstitial fibrosis. Computed tomography (CT) is central to the diagnosis and understanding of clinicopathologic manifestations of smoking-related lung injury. Common features of SRILD on CT include low-attenuation areas, ground-glass opacities, fibrosis, and lung nodules. Although the various SRILDs are often described as distinct entities, they may manifest with nonspecific features or exhibit mixed patterns and may more accurately be described as a continuum of pathology. Understanding the broad range of radiologic features and recognizing the potential coexistence and overlap of disease processes is essential to maintaining an appropriate differential diagnosis. In this article, we review the radiologic findings associated with SRILDs with a focus on diagnostic considerations and challenges when interpreting CT images.

Purpose Older lung transplant recipients experience increased rates of adverse clinical outcomes including infection compared with younger patients, potentially related to impaired cell-mediated immunity, frailty, and sarcopenia. Methods Patients over age 55 years undergoing evaluation for lung transplantation were evaluated for sarcopenia by cross-sectional area and average attenuation of the pectoralis major muscle on chest CT. Frailty was measured using the Fried Frailty Phenotype (FFP). Immune phenotyping was performed using multichannel flow cytometry of PBMC in a total of 26 lung transplant candidates. Findings The median patient age was 65, primarily with restrictive lung disease (76.9%). Hospital readmission was associated with lower frequency of naïve CD4 (p=0.004) and CD8 T cells (p=0.026). Senescent CD4 (KLRG1+/CD28-) and CD8 T cells were also associated with readmission (p=0.014 and p=0.013, respectively), and senescent CD4 T cells were predictive of total hospital time (p=0.003). TEMRA CD4 T cells were significantly associated with frailty (p=0.015) and sarcopenia (p=0.011). Senescent CD4 and CD8 T cells were significantly associated with sarcopenia (p=0.009 and p=0.006, respectively). Conclusions These findings suggest that impaired cell-mediated immunity may underlie the associations between frailty and sarcopenia and poor clinical outcomes. A multi-faceted approach to evaluation of older patients has the potential to improve risk stratification and inform management of immunosuppression.

Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.

In this video article, Lung-RADS committee members Jared D. Christensen, MD, MBA, (who also serves as chair) and Ashley E. Prosper, MD, discuss the most recent Lung-RADS update, including key changes and implications for clinical practice.

ObjectiveLung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules.MethodsThe American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel.ResultsConsensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules.ConclusionsDespite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules.