Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms

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

doi:10.1371/journal.pone.0146060

Cited by 6 publications

(4 citation statements)

References 32 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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“…To incorporate the remaining vasculature and airway branches, multiple groups developed physiologically based algorithms that model airways, 37 – 39 vasculature, 40 or both together. 41 , 42 These algorithms are volume-filling branching methods in which the parameters (branching angle, diameters, lengths, branching order, etc.) are informed by physiological laws (e.g., flow dynamics) and anatomical measurement studies.…”

Section: Computational Anthropomorphic Phantomsmentioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

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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.

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“…The software implementation [12], however, does not support out-of-core volume generation since it operates completely in memory. VascuSynth was also used as the basis for the generation of pulmonary CT phantoms [13]. Other works targeting volumetric vessel image generation model the tree shape based on tissue metabolism [26] or experimental data [18].…”

Section: Introductionmentioning

confidence: 99%