Using X-ray velocimetry (XV) is possible to measure the dynamics of airflow and detect cystic fibrosis lung disease


World-first research led by Monash University could hold the key to better monitoring and treatment of lung disease associated with cystic fibrosis (CF) – a condition thousands of people are diagnosed with each year.

Using X-ray velocimetry (XV), the multi-disciplinary collaboration of physicists, engineers, biomedical engineers and clinicians was able to measure the dynamics of airflow through the lungs during the course of a natural breathing cycle, and measures the presentations of CF lung disease.

The research team, led by Dr. Freda Werdiger from Monash University’s Department of Mechanical and Aerospace Engineering, was able to pinpoint the exact locations of abnormal airflow within lungs with CF-like disease, and better quantify the level of disease present.

The successful trial opens up avenues for a range of respiratory diseases to be diagnosed, treated and managed earlier than current technology allows and at a lower radiation dose than current CT scanning.

The study was published in the prestigious journal Scientific Reports.

“In this study we present two developments in XV analysis. Firstly, we show the ability of laboratory-based XV to detect the patchy nature of CF-like disease in affected mice. Secondly, we present a technique for numerical quantification of that disease, which can delineate between two major modes of disease symptoms,” Dr. Werdiger said.

“This analytical model provides a simple, easy-to-interpret approach, and one capable of being readily applied to large quantities of data generated in XV imaging. Together these advances show the power of XV for assessing local airflow changes.

“We propose that XV should be considered as a novel lung function measurement tool for lung therapeutics development in small animal models, for CF and for other muco-obstructive diseases.”

Cystic fibrosis is a progressive, chronic and debilitating genetic disease caused by mutations in the CF Transmembrane-conductance Regulator gene. Unrelenting CF airway disease begins early in infancy and produces a steady deterioration in quality of life, ultimately leading to premature death.

Effective lung health assessment tools must capture the patchy nature of muco-obstructive lung diseases such as CF, which is important during the early stages of the disease when local treatments could be applied to prevent disease progression.

But, current lung function assessment methods have many limitations, including the inability to accurately localize the origin of globally-measured changes in lung health.

Assessments of overall lung health in humans and animals are made using lung function tests that screen for abnormalities by measuring the flow of gas in the airways.

Spirometry is the most common tool for assessing lung function, however it measures global airflow at the mouth.

Despite the availability of techniques that assess either lung function or lung structure, none of these are able to simultaneously quantify function and identify the origin of those functional changes. Abnormal lung motion during breathing has been demonstrated to be an indicator of disease.

X-ray velocimetry provides non-invasive, high-definition and sensitive real-time images of airflow through the lungs in live organisms.

The technology was designed and commercialized by Australian-based med-tech company 4DMedical, led by CEO and former Monash University researcher Professor Andreas Fouras.

The technology has since been upscaled for clinical use, and in the USA was recently given FDA approval for all respiratory indications in adults.

“To effectively diagnose, monitor and treat respiratory disease clinicians should be able to accurately assess the spatial distribution of airflow across the fine structure of the lung.

This capability would enable any decline or improvement in health to be located and measured, allowing improved treatment options to be designed,” Dr. Werdiger said.

“The success of XV lies in its ability to draw reliable and meaningful quantitative measures, and this study shows how this can be accomplished.

“In the future, these techniques can be expected to be applied to the numerical characterisation of CF lung disease in larger cohorts and other CF animal models.

“These methods allow analyses to be applied in a straightforward fashion and with minimal manual processing, to enable ongoing study and development of the treatment of CF and other respiratory diseases.”

Imaging the soft tissues of the body, and in particular the lungs and airways, has traditionally been difficult in the x-ray regime due to the similarities in the x-ray absorption properties of muscle, tissues and liquids.

This has made high-resolution respiratory imaging difficult, particularly in clinical diagnosis and treatment monitoring. Current techniques for imaging the lungs, such as magnetic resonance imaging (MRI), x-ray computed tomography (CT) or positron emission tomography (PET), lack either the spatial resolution to resolve airway and alveolar structures or the temporal resolution to image throughout ventilation, or both, and often require the use of contrast agents, radioisotopes or relatively high radiation doses [1].

Additionally, non-imaging methods used to provide quantitative measurements for lung function, including spirometry and plethysmography, lack the ability to measure regional lung function [2,3].

As such, our understanding of the progression and intricacies of chronic lung diseases such as asthma, emphysema and cystic fibrosis, and our ability to diagnose and treat these conditions earlier, can be significantly improved with the ability to image the structure of the lungs and airways at high resolution and the ability to perform regional lung function testing.

Propagation-based phase contrast x-ray imaging (PB-PCXI) has proven to be a forefront imaging modality in the advancement of high-resolution lung imaging [4–7]. Recent applications have combined PB-PCXI with a common engineering technique used to study fluid flows, namely velocimetry.

These studies have shown that velocimetry can also be used to study the motion of the lungs during ventilation [3,8], and have shown that altered lung motion, as detected by this combined technique, can be a sensitive indicator of regional lung disease [2]. This technique, combining PB-PCXI and velocimetry, has come to be known as x-ray velocimetry (XV).

Lung XV shows potential in reaching clinical application, and so work must be done to optimise laboratory x-ray source imaging systems that can provide the necessary flux and spatial coherence required.

Whilst synchrotron facilities have been fundamental to the development of phase contrast lung imaging, they do not possess the financial practicality or convenience for widespread clinical medical imaging.

To make such a technique widely available, it is crucial that a more compact, less expensive imaging setup be developed, with the ultimate goal of clinical implementation.

In this paper we present an optimisation of imaging rates for XV of the lungs on both a synchrotron, where XV research began and will continue to develop, and also on a laboratory x-ray system, with the translation of XV from the synchrotron to the laboratory being inevitable.

We give a brief summary of both phase contrast x-ray imaging and x-ray velocimetry in §2.1 and §2.2 respectively, followed by the important imaging parameters to consider in combining these techniques (§2.3).

We detail the methods used in this paper, describing imaging system parameters and how the simulated lung speckle was created (§3.1). We then outline the XV analysis methods (§3.2).

The results of optimising the exposure time ratio and, separately, exposure time are discussed in §4.1 and §4.2 respectively, with important implications to future users to obtain the best quality XV from their systems, whether they be synchrotron- or laboratory-based. We conclude the results of this investigation in §5.

Phase contrast x-ray imaging
Phase contrast x-ray imaging utilises the refractive properties of materials to produce high-resolution soft-tissue sensitive images. As an x-ray passes through a sample, changes to both the amplitude and phase of the wavefield are introduced based on the refractive indices, n, of the materials within the sample and the sample thickness [9].

The variations in density and atomic make-up of different biological tissues create differences in their refractive abilities, and it is these refractive properties that create the contrast seen in phase contrast imaging. In such a way, PCXI can prove to be significantly more sensitive than conventional x-ray imaging methods [10].

There are numerous ways to convert these phase variations to observable intensity variations, the simplest of which is propagation-based PCXI. This technique differs from the conventional x-ray imaging setup only by the increased distance from the sample to the detector, with no additional optical elements required.

In PB-PCXI, x-rays produced by a sufficiently coherent source pass through the sample and are refracted according to the refractive indices of the materials present within the object, as can be seen in Fig. 1.

As the distorted wavefront propagates towards the detector the rays diverge and interfere, producing a Fresnel diffraction pattern at the imaging plane, resulting in intensity variations in the form of bright-dark fringes. These fringes enhance the visibility of material boundaries, thus allowing subtle features within the sample to have their visibility enhanced.

With an increased sample-to-detector propagation distance, R2, the fringes of the Fresnel diffraction pattern initially increase in both width and relative magnitude, thus creating tuneable increased edge contrast [11].

An external file that holds a picture, illustration, etc.
Object name is boe-7-1-79-g001.jpg
Fig. 1 – A schematic of a propagation-based phase contrast x-ray imaging set-up of the lungs. The x-rays propagate over the distance R1 from the source through the sample, where they undergo refraction based on the refractive indices of the material interfaces. The refracted and diffracted x-rays then propagate over distance R2 to the detector, where a Fresnel diffraction pattern is recorded.

X-ray velocimetry
X-ray velocimetry is used to study the flow of air through the lungs and is a technique based on particle image velocimetry (PIV). PIV is a well-established technique used in fluid mechanics to measure the flow and movement of fluids [12,13].

Conventionally used to image optically transparent systems with lasers, PIV has been extended in recent years to optically opaque systems, such as biological objects, with the use of x-rays [1,14].

The advancement of ultra-fast detector systems and the development of phase-contrast imaging has increased the applicability of this technique to live imaging in vivo, namely to the speckle patterns created from the blood [15,16] and the lungs [2,3]

Thus, the application of PIV to biological systems using x-rays has been dubbed x-ray velocimetry (XV).

In PIV and XV, movement or flow of the sample is determined by statistically analysing two images separated by a known time interval. In phase-contrast XV of the lungs, the speckle pattern created from phase contrast imaging of the lungs provides the pattern through which the movement of the lungs can be traced.

The image pairs are divided into many sampling windows, commonly known as ‘interrogation windows’, and analysed by cross-correlation to produce correlation peaks. In each cross-correlation, the distance from the centre to the maximum peak shows the inter-frame displacement for that interrogation window, as can be seen in Fig. 2.

By iteratively applying this process from larger interrogation widows to progressively smaller windows, the accuracy and resolution of the speckle displacement between frames can be refined. This displacement can then be displayed as a velocity vector given a known time-lapse between image frames. Post-processing can then allow the user to map local lung expansion and airflow through the lungs [2,3].

An external file that holds a picture, illustration, etc.
Object name is boe-7-1-79-g002.jpg
Fig. 2
Schematic diagram illustrating the technique of x-ray velocimetry, where the speckle images taken at two different time points are discretised into interrogation windows, the cross correlation calculated for each window, and the resultant vector field produced to show the motion of the sample.

Important XV imaging parameters
When optimising an imaging system for XV, there are multiple parameters to consider. Optimisation of magnification, source size and pixel size for XV have previously been investigated by Ng et al. [17], and thus the present investigation focuses on optimising imaging rates, in particular exposure time and the time between exposures.

Exposure time is a fundamental parameter to optimise when imaging dynamic samples. If the exposure time is too short then the image will be noisy. Whilst from a phase-contrast image quality point-of-view this is undesirable, noise is particularly problematic for XV analysis of the lungs.

When noise distorts the already spatially fine lung speckle the correlation peak will have a small signal-to-noise ratio (SNR), resulting in noise in the motion vectors. However, motion blur becomes an issue when the exposure time is too long, relative to the speed of the lung motion.

In lung XV, motion blur will broaden and decrease the visibility of the lung speckle, reducing the correlation peak height, and hence the ability of XV to be able to accurately track the speckle movement in the presence of any noise.

To better understand this, each smeared speckle can be approximated via convolution with a top-hat function. The cross-correlation peak of two top-hat functions is triangular, with the area under the triangle being fixed.

The longer the exposure time and the higher the velocity, the more the speckle will suffer from motion blur and thus the shorter and wider the top hat function becomes. This in turn drives the cross correlation peak lower, thus reducing the SNR of the peak and reducing the accuracy of the XV.

This concept is explored in more detail in Fouras et al. [18], along with the importance of the time between exposures in XV imaging. In their study of a particle-laced fluid moving through a cylinder, Fouras et al. found that in an image with multiple velocities present during a single exposure, motion blur affected the higher-velocity particles, and thus reduced the cross-correlation peak height for higher velocities.

The iterative XV analysis method therefore biased results towards the lower displacement components of the field that had higher correlation peaks, leading to an underestimation of the velocity in the faster moving areas of the image.

It was found that optimising the dead-time between exposures relative to the exposure time, henceforth described as the exposure time ratio (exposure time/dead-time between exposures), εt, is crucial in preventing this underestimation of velocity. In this study, we investigate the optimal exposure time ratio for XV imaging of the lungs of small animals.

The optimal imaging rates for performing x-ray velocimetry on live small animal lungs, in vivo, were determined via simulation for a given laboratory and synchrotron imaging setup. This optimisation focused on the balance required between short exposure times for non-blurred high-speed live animal imaging and longer exposure times for higher SNR.

We investigated this relationship using a simulated lung speckle model undergoing rotational motion, allowing us to explore a range of velocities up to 1 px ms−1, a range comparable to lung velocities observed in vivo in mice.

The use of px ms−1 allows the results presented to be expanded beyond the specific pixel and speckle sizes investigated here. It was found that, independent of experimental setup or lung velocity, the exposure time ratio that achieved optimal XV analysis was consistently 1:2, meaning that the dead-time in between exposures should be twice as long as the exposure time.

The optimal exposure time for an Excillum source laboratory setup for the range of speckle displacements/velocities tested was 35 ms, with a range of 20-45 ms still giving results with <20% error for most of the velocities analysed.

For a 3 GeV synchrotron imaging setup at 30 keV, the optimal exposure times for the range of velocities can be found from 15 to 40 ms, however a range of 5-55 ms will still give an XV accuracy of <15% for most velocities up to 1 px ms−1. However, the average grey level counts can be used to guide appropriate exposure times for other imaging systems.

It is important to reiterate that the velocity of the object itself is not the limiting factor in XV, but it is the displacement between frames that should guide the selection of imaging speeds. This cannot be understated, and it is this fact that truly allows our results to be expanded and utilised beyond the current experimental parameter ranges presented within this study.

References and links

1. Fouras A., Kitchen M. J., Dubsky S., Lewis R. A., Hooper S. B., Hourigan K., “The past, present, and future of x-ray technology for in vivo imaging of function and form,” J. Appl. Phys. 105(10), 102009 (2009).10.1063/1.3115643 [CrossRef] [Google Scholar]

2. Fouras A., Allison B. J., Kitchen M. J., Dubsky S., Nguyen J., Hourigan K., Siu K. K. W., Lewis R. A., Wallace M. J., Hooper S. B., “Altered lung motion is a sensitive indicator of regional lung disease,” Ann. Biomed. Eng. 40(5), 1160–1169 (2012).10.1007/s10439-011-0493-0 [PubMed] [CrossRef] [Google Scholar]

3. Dubsky S., Hooper S. B., Siu K. K. W., Fouras A., “Synchrotron-based dynamic computed tomography of tissue motion for regional lung function measurement,” J. R. Soc. Interface 9(74), 2213–2224 (2012).10.1098/rsif.2012.0116 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

4. Lewis R. A., Yagi N., Kitchen M. J., Morgan M. J., Paganin D., Siu K. K. W., Pavlov K., Williams I., Uesugi K., Wallace M. J., Hall C. J., Whitley J., Hooper S. B., “Dynamic imaging of the lungs using x-ray phase contrast,” Phys. Med. Biol. 50(21), 5031–5040 (2005).10.1088/0031-9155/50/21/006 [PubMed] [CrossRef] [Google Scholar]

5. Kitchen M. J., Lewis R. A., Morgan M. J., Wallace M. J., Siew M. L., Siu K. K. W., Habib A., Fouras A., Yagi N., Uesugi K., Hooper S. B., “Dynamic measures of regional lung air volume using phase contrast x-ray imaging,” Phys. Med. Biol. 53(21), 6065–6077 (2008).10.1088/0031-9155/53/21/012 [PubMed] [CrossRef] [Google Scholar]

6. Hooper S. B., Kitchen M. J., Siew M. L., Lewis R. A., Fouras A., te Pas A. B., Siu K. K., Yagi N., Uesugi K., Wallace M. J., “Imaging lung aeration and lung liquid clearance at birth using phase contrast X-ray imaging,” Clin. Exp. Pharmacol. Physiol. 36(1), 117–125 (2009).10.1111/j.1440-1681.2008.05109.x [PubMed] [CrossRef] [Google Scholar]

7. Siu K. K. W., Morgan K. S., Paganin D. M., Boucher R., Uesugi K., Yagi N., Parsons D. W., “Phase contrast X-ray imaging for the non-invasive detection of airway surfaces and lumen characteristics in mouse models of airway disease,” Eur. J. Radiol. 68(3 Suppl), S22–S26 (2008).10.1016/j.ejrad.2008.04.029 [PubMed] [CrossRef] [Google Scholar]

8. S. Dubsky, S. B. Hooper, K. K. W. Siu, and A. Fouras, “Dynamic four-dimensional X-ray PIV of the lung”, presented at the 9th International Symposium on Particle Image Velocimetry, Kobe, Japan, 21–23 July 2011. [Google Scholar]

9. Bravin A., Coan P., Suortti P., “X-ray phase-contrast imaging: from pre-clinical applications towards clinics,” Phys. Med. Biol. 58(1), R1–R35 (2013).10.1088/0031-9155/58/1/R1 [PubMed] [CrossRef] [Google Scholar]

10. Takeda T., “Phase-contrast and fluorescent X-ray imaging for biomedical researches,” Nucl. Instrum. Methods Phys. Res. A 548(1–2), 38–46 (2005).10.1016/j.nima.2005.03.063 [CrossRef] [Google Scholar]

11. Morgan K. S., Siu K. K. W., Paganin D. M., “The projection approximation versus an exact solution for X-ray phase contrast imaging, with a plane wave scattered by a dielectric cylinder,” Opt. Commun. 283(23), 4601–4608 (2010).10.1016/j.optcom.2010.07.012 [CrossRef] [Google Scholar]

12. Dudderar T. D., Simpkins P. G., “Laser speckle photography in a fluid medium,” Nature 270(5632), 45–47 (1977).10.1038/270045a0 [CrossRef] [Google Scholar]

13. Adrian R. J., “Twenty years of particle image velocimetry,” Exp. Fluids 39(2), 159–169 (2005).10.1007/s00348-005-0991-7 [CrossRef] [Google Scholar]

14. Fouras A., Dusting J., Sheridan J., Kawahashi M., Hirahara H., Hourigan K., “Engineering imaging: using particle image velocimetry to see physiology in a new light,” Clin. Exp. Pharmacol. Physiol. 36(2), 238–247 (2009).10.1111/j.1440-1681.2008.05102.x [PubMed] [CrossRef] [Google Scholar]

15. Irvine S. C., Paganin D. M., Dubsky S., Lewis R. A., Fouras A., “Phase retrieval for improved three-dimensional velocimetry of dynamic x-ray blood speckle,” Appl. Phys. Lett. 93(15), 153901 (2008).10.1063/1.3001592 [CrossRef] [Google Scholar]

16. Jamison R. A., Dubsky S., Siu K. K. W., Hourigan K., Fouras A., “X-ray velocimetry and haemodynamic forces within a stenosed femoral model at physiological flow rates,” Ann. Biomed. Eng. 39(6), 1643–1653 (2011).10.1007/s10439-011-0260-2 [PubMed] [CrossRef] [Google Scholar]

17. Ng I., Paganin D. M., Fouras A., “Optimization of in-line phase contrast particle image velocimetry using a laboratory x-ray source,” J. Appl. Phys. 112(7), 074701 (2012).10.1063/1.4757407 [CrossRef] [Google Scholar]

18. Fouras A., Dusting J., Lewis R., Hourigan K., “Three-dimensional synchrotron x-ray particle image velocimetry,” J. Appl. Phys. 102(6), 064916 (2007).10.1063/1.2783978 [CrossRef] [Google Scholar]

More information: Freda Werdiger et al, Quantification of muco-obstructive lung disease variability in mice via laboratory X-ray velocimetry, Scientific Reports (2020). DOI: 10.1038/s41598-020-67633-y


Please enter your comment!
Please enter your name here

Questo sito usa Akismet per ridurre lo spam. Scopri come i tuoi dati vengono elaborati.