Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging

Visual analysis of pulmonary blood flow in pulmonary circulation assessment: differences between two variant algorithms for processing dynamic images

Background

In the quantitative assessment of pulmonary blood flow, two different processing algorithms [cross-correlation calculation processing (CCC-pro) and reference frame subtraction processing (RFS-pro)] within dynamic imaging systems have been reported to exhibit high correlations with conventional measurement methods. However, reports still need to evaluate these two processing algorithms regarding the different aspects of pulmonary blood flow. This study aimed to analyze the differences in pulmonary circulation.

Methods

We conducted a cross-sectional study to evaluate patients with lung cancer who underwent radical surgery, simultaneous dynamic chest radiography (DCR), and pulmonary perfusion scintigraphy (PPS). We assessed the correlation between PPS and two algorithms (CCC-pro and RFS-pro) regarding calculated blood flow ratio (BFR) using Pearson's correlation and linear regression analysis. Additionally, we evaluated consistency using the Bland-Altman analysis. We compared the pulmonary blood flow distributions across six-division lung fields and evaluated each method's blood flow images and histograms of pixel values.

Results

From May 2018 to December 2020, we consecutively enrolled 46 patients with lung cancer who met the inclusion criteria (40 male patients, with a mean age of 72.91 years). In these patients, CCC-pro and RFS-pro were correlated (R=0.718, P<0.01); however, CCC-pro was more strongly correlated with PPS than RFS-pro (R=0.859, P<0.01 vs. R=0.549, P<0.01). The Bland-Altman analysis showed high agreement, although systematic errors were observed in relationships other than RFS-pro to PPS. CCC-pro and RFS-pro showed similar blood flow distributions in the upper and lower lung fields, with RFS-pro being dominant in the middle. RFS-pro showed higher pixel values in the hilar region and a histogram shape similar to PPS; however, posture affected the right upper lung field gradient. RFS-pro showed no difference in the BFR when the pulmonary artery region was symmetric; however, potential inaccuracies existed when it overlapped with the cardiovascular shadow.

Conclusions

The CCC-pro algorithm was useful for quantifying BFRs, whereas the RFS-pro algorithm accurately evaluated blood flow distribution in lung fields. Further algorithm development is required to enable versatile pulmonary blood flow analysis.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Construction of a hybrid lung model by combining a real geometry of the upper airways and an idealized geometry of the lower airways

BACKGROUND AND OBJECTIVE

Health care costs represent a substantial an increasing percentage of global expenditures. One key component is treatment of respiratory diseases, which account for one in twelve deaths in Europe. Computational simulations of lung airflow have potential to provide considerable cost reduction and improved outcomes. Such simulations require accurate in silico modeling of the lung airway. The geometry of the lung is extremely complex and for this reason very simple morphologies have primarily been used to date. The objective of this work is to develop an effective methodology for the creation of hybrid pulmonary geometries combining patient-specific models obtained from CT images and idealized pulmonary models, for the purpose of carrying out experimental and numerical studies on aerosol/particle transport and deposition in inhaled drug delivery.

METHODS

For the construction of the hybrid numerical model, lung images obtained from computed tomography were exported to the DICOM format to be treated with a commercial software to build the patient-specific part of the model. At the distal terminus of each airway of this portion of the model, an idealization of a single airway path is connected, extending to the sixteenth generation. Because these two parts have different endings, it is necessary to create an intermediate solid to link them together. Physically realistic treatment of truncated airway boundaries in the model was accomplished by mapping of the flow velocity distribution from corresponding conducting airway segments.

RESULTS

The model was verified using two sets of simulations, steady inspiration/expiration and transient simulation of forced spirometry. The results showed that the hybrid model is capable of providing a realistic description of air flow dynamics in the lung while substantially reducing computational costs relative to models of the full airway tree.

CONCLUSIONS

The model development outlined here represents an important step toward computational simulation of lung dynamics for patient-specific applications. Further research work may consist of investigating specific diseases, such as chronic bronchitis and pulmonary emphysema, as well as the study of the deposition of pollutants or drugs in the airways.