CT imaging of peripheral pulmonary vessel disease

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images

Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation .

From Early Morphometrics to Machine Learning-What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure-function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.

Healthy Lung Vessel Morphology Derived From Thoracic Computed Tomography

Knowledge of the lung vessel morphology in healthy subjects is necessary to improve our understanding about the functional network of the lung and to recognize pathologic deviations beyond the normal inter-subject variation. Established values of normal lung morphology have been derived from necropsy material of only very few subjects. In order to determine morphologic readouts from a large number of healthy subjects, computed tomography pulmonary angiography (CTPA) datasets, negative for pulmonary embolism, and other thoracic pathologies, were analyzed using a fully-automatic, in-house developed artery/vein separation algorithm. The number, volume, and tortuosity of the vessels in a diameter range between 2 and 10 mm were determined. Visual inspection of all datasets was used to exclude subjects with poor image quality or inadequate artery/vein separation from the analysis. Validation of the algorithm was performed manually by a radiologist on randomly selected subjects. In 123 subjects (men/women: 55/68), aged 59 ± 17 years, the median overlap between visual inspection and fully-automatic segmentation was 94.6% (69.2–99.9%). The median number of vessel segments in the ranges of 8–10, 6–8, 4–6, and 2–4 mm diameter was 9, 34, 134, and 797, respectively. Number of vessel segments divided by the subject's lung volume was 206 vessels/L with arteries and veins contributing almost equally. In women this vessel density was about 15% higher than in men. Median arterial and venous volumes were 1.52 and 1.54% of the lung volume, respectively. Tortuosity was best described with the sum-of-angles metric and was 142.1 rad/m (138.3–144.5 rad/m). In conclusion, our fully-automatic artery/vein separation algorithm provided reliable measures of pulmonary arteries and veins with respect to age and gender. There was a large variation between subjects in all readouts. No relevant dependence on age, gender, or vessel type was observed. These data may provide reference values for morphometric analysis of lung vessels.

The imaging of pulmonary hypertension

Pulmonary hypertension (PH) is the remarkable hemodynamic consequence of widespread structural and functional changes within the pulmonary circulation. Elevated pulmonary vascular resistance leads to increased mean pulmonary arterial pressure and, ultimately, right ventricular dysfunction. PH carries a poor prognosis and warrants timely and accurate diagnosis for appropriate intervention. The 2008 Dana Point classification system provides the categorical framework currently guiding therapy and surveillance. Radiologic imaging is an essential tool in the detection and diagnostic evaluation of patients with PH. Echocardiography, ventilation-perfusion scintigraphy, multidetector computed tomography, and cardiac magnetic resonance imaging provide insights into vascular morphology, pulmonary parenchymal status, cardiac function, and underlying etiology of the disorder. Emerging techniques of functional pulmonary and cardiac imaging hold great promise for the assessment and monitoring of these patients in the future.

From the Archives of the AFIP: pulmonary veno-occlusive disease and pulmonary capillary hemangiomatosis

Pulmonary veno-occlusive disease (PVOD) and pulmonary capillary hemangiomatosis (PCH) are two unusual idiopathic disorders that almost uniformly manifest to the clinician as pulmonary arterial hypertension (PAH). Impressive clinical signs and symptoms often obscure the true underlying capillary or postcapillary disorder, thus severely compromising timely and appropriately directed therapy. The hemodynamics of PVOD and PCH are the consequence of a widespread vascular obstructive process that originates in either the alveolar capillary bed (in cases of PCH) or the pulmonary venules and small veins (in PVOD). Since the earliest descriptions of PVOD and PCH, there has been a debate as to whether these are two distinct diseases or varied expressions of a single disorder. The cause of PVOD or PCH has not yet been identified, although there are several reported associations. Without curative lung or heart-lung transplantation, patients with these conditions face inexorable clinical deterioration and death within months to a few short years of initial presentation. Surgical lung biopsy is the definitive diagnostic test, but it is a risky undertaking in such critically ill patients. The imaging manifestations of PVOD and PCH often reflect the underlying hemodynamic derangements, and these findings may assist the clinician in discerning PAH from an underlying capillary or postcapillary process with findings of septal lines, characteristic ground-glass opacities, and occasionally pleural effusion.

CT of the chest in the evaluation of idiopathic pulmonary arterial hypertension in children

BACKGROUND

Idiopathic pulmonary arterial hypertension (IPAH) is a rare disease in children. By definition it is a diagnosis of exclusion, and CT of the chest is primarily performed to exclude other causes. Previous studies have defined CT features suggestive of the diagnosis of IPAH, but these have all been limited to the adult population.

OBJECTIVE

Contrast-enhanced chest CT and high-resolution CT findings in IPAH were evaluated in an attempt to define features consistently seen in children with this condition.

MATERIALS AND METHODS

The chest CT scans performed at initial presentation were reviewed in 17 children with echocardiographic or angiographic evidence of IPAH.

RESULT

There were nine boys and eight girls, ranging in age from 1 month to 17 years. The extrapulmonary findings included cardiomegaly with right-sided cardiac enlargement, which was seen in 13 children. The central pulmonary arteries were enlarged in 15 children, with peripheral enlargement in two. In six children this resulted in bronchial compression. In addition, mediastinal and hilar lymphadenopathy was noted in three children. Prominent intrapulmonary features included a peripheral vasculopathy, with enlarged tortuous vessels, seen in eight children. Ill-defined ground-glass centrilobular opacities were also noted in eight children, representing the most common parenchymal abnormality. Other findings included septal lines in five, diffuse ground-glass opacification in four and focal hyperlucent zones in three. Mosaic attenuation was seen in one child.

CONCLUSION

A variety of imaging findings are identified in IPAH. Features particularly consistent with the diagnosis include peripheral vasculopathy and centrilobular opacities in the setting of cardiomegaly and central pulmonary arterial enlargement.