Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Generative-based airway and vessel morphology quantification on chest CT images

Accurately and precisely characterizing the morphology of small pulmonary structures from Computed Tomography (CT) images, such as airways and vessels, is becoming of great importance for diagnosis of pulmonary diseases. The smaller conducting airways are the major site of increased airflow resistance in chronic obstructive pulmonary disease (COPD), while accurately sizing vessels can help identify arterial and venous changes in lung regions that may determine future disorders. However, traditional methods are often limited due to image resolution and artifacts. We propose a Convolutional Neural Regressor (CNR) that provides cross-sectional measurement of airway lumen, airway wall thickness, and vessel radius. CNR is trained with data created by a generative model of synthetic structures which is used in combination with Simulated and Unsupervised Generative Adversarial Network (SimGAN) to create simulated and refined airways and vessels with known ground-truth. For validation, we first use synthetically generated airways and vessels produced by the proposed generative model to compute the relative error and directly evaluate the accuracy of CNR in comparison with traditional methods. Then, in-vivo validation is performed by analyzing the association between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter, two well-known measures of lung function and airway disease, for airways. For vessels, we assess the correlation between our estimate of the small-vessel blood volume and the lungs' diffusing capacity for carbon monoxide (DLCO). The results demonstrate that Convolutional Neural Networks (CNNs) provide a promising direction for accurately measuring vessels and airways on chest CT images with physiological correlates.

A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images

Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary AV classification using a random forest (RF) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary CT phantoms, and 26 lungs from noncontrast CT images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy ( ∼ 20%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.

Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins

The purpose of this paper was to extend the extended cardiac-torso (XCAT) series of computational phantoms to include a detailed lung architecture including airways and pulmonary vasculature. Eleven XCAT phantoms of varying anatomy were used in this paper. The lung lobes and initial branches of the airways, pulmonary arteries, and veins were previously defined in each XCAT model. These models were extended from the initial branches of the airways and vessels to the level of terminal branches using an anatomically-based volume-filling branching algorithm. This algorithm grew the airway and vasculature branches separately and iteratively without intersecting each other using cylindrical models with diameters estimated by order-based anatomical measurements. Geometrical features of the extended branches were compared with the literature anatomy values to quantitatively evaluate the models. These features include branching angle, length to diameter ratio, daughter to parent diameter ratio, asymmetrical branching pattern, diameter, and length ratios. The XCAT phantoms were then used to simulate CT images to qualitatively compare them with the original phantom images. The proposed growth model produced 46369 ± 12521 airways, 44737 ± 11773 arteries, and 39819 ± 9988 veins to the XCAT phantoms. Furthermore, the growth model was shown to produce asymmetrical airway, artery, and vein networks with geometrical attributes close to morphometry and model based studies. The simulated CT images of the phantoms were judged to be more realistic, including more airways and pulmonary vessels compared with the original phantoms. Future work will seek to add a heterogeneous parenchymal background into the XCAT lungs to make the phantoms even more representative of human anatomy, paving the way towards the use of XCAT models as a tool to virtually evaluate the current and emerging medical imaging technologies.

Anthropomorphic dual-lattice voxel models for optimizing image quality and dose

Using numerical simulations, the influence of various imaging parameters on the resulting image can be determined for various imaging technologies. To achieve this, visualization of fine tissue structures needed to evaluate the image quality with different radiation quality and dose is essential. The present work examines a method that employs simulations of the imaging process using Monte Carlo methods and a combination of a standard and higher resolution voxel models. A hybrid model, based on nonlinear uniform rational B-spline and polygon mesh surfaces, was constructed from an existing voxel model of a female patient of a resolution in the range of millimeters. The resolution of the hybrid model was [Formula: see text], i.e., substantially finer than that of the original model. Furthermore, a high resolution lung voxel model [[Formula: see text] voxel volume, slice thickness: [Formula: see text]] was developed from the specimen of a left lung lobe. This has been inserted into the hybrid model, substituting its left lung lobe and resulting in a dual-lattice geometry model. "Dual lattice" means, in this context, the combination of voxel models with different resolutions. Monte Carlo simulations of radiographic imaging were performed and the fine structure of the lung was easily recognizable.