Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms

Jiménez-Carretero, Daniel; Estépar, Raúl San Jośe; Cacio, Mario Diaz; Ledesma-Carbayo, Maria J.

doi:10.1101/022871

Cited by 4 publications

(6 citation statements)

References 34 publications

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“…One of the fundamental limitations for the development of subvoxel sizing methods is the ability to generate realistic and high-resolution representations of 3D bronchial and vascular trees. Realistic anthropomorphic phantoms are complex to generate as several parameters need to be adjusted in order to properly define the topology of the structures of interest and their relationship in the 3D space [41]. Moreover, these methods are not ideal for morphology assessment, as an exact size of the structure is difficult to obtain.…”

Section: Discussionmentioning

confidence: 99%

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

Nardelli

Ross

Estépar

2020

Medical Image Analysis

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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 PREPRINT -MARCH 16, 2020 Additionally, several studies have demonstrated that small pulmonary arteries become smaller and shrink at subsegmental levels in patients with COPD [7,8], and endothelial dysfunction may be caused by both pulmonary and extra-pulmonary vascular alterations in COPD [2,3]. Therefore, having an automated method for airway and vessel morphology assessment will help precise measurements of the geometrical properties of bronchial and venous trees which, in turn, may lead to improved diagnosis and open the door to new studies on lung disorders.While in the past several methods have been proposed with the aim of helping physicians accurately locate small pulmonary airways and veins on chest CT images [9, 10], up to date not much work has been proposed for sub-voxel morphology assessment. Traditional approaches for airway wall thickness detection are based on edge-detection methods, that, although limited by the Nyquist theorem, use the reconstructed CT signal to analyze properties of the structure directly. Among them, the full width at half max (FWHM) [11], for which the true edge of an ideal step function undergoing low-pass filtering is located at the FWHM location, is one of the most typical algorithms. In [1...

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Section: Discussionmentioning

confidence: 99%

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

Nardelli

Ross

Estépar

2020

Medical Image Analysis

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“…Numerous morphometry studies [14, 18, 20, 33, 35] have investigated the number of generations, branch lengths and diameters, and airway wall thicknesses in conducting airways and pulmonary vessel trees to the level of terminal branches enabling mathematical 3D models of human lung airways [12, 23, 24, 45, 47] and vasculature networks [25, 48]. Some of these models have been successfully used to investigate some lung functions [26–30, 43].…”

Section: Discussionmentioning

confidence: 99%

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

Abadi

Segars

Sturgeon

et al. 2018

IEEE Trans. Med. Imaging

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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.

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“…The distribution of the labeled synthetic database (both in NRRD and DICOM formats) [ 44 , 45 ] will enable the community to improve evaluation schemes, to train or test different methods for processing pulmonary structures in CT images, as well as support the development of new image processing algorithms.…”

Section: Discussionmentioning

confidence: 99%

Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms

et al. 2016

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The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery–vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically. Starting from biological and image-based knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. A dataset of 24 labeled anthropomorphic pulmonary CT phantoms were synthesized with the proposed system. Visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems show good correspondence between real and synthetic lungs (p > 0.05 with low Cohen’s d effect size and AUC values), supporting the potentiality of the tool and the usefulness of the generated phantoms in the biomedical image processing field.

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Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms

Cited by 4 publications

References 34 publications

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

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

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

Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms

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