Reproducibility and comparison of oxygen-enhanced T1 quantification in COPD and asthma patients

Simon M. F. Triphan; Bertram J. Jobst; Angela Anjorin; Oliver Sedlaczek; Ursula Wolf; Maxim Terekhov; Christian Hoffmann; Sebastian Ley; Christoph Düber; Jürgen Biederer; Hans-Ulrich Kauczor; Peter M. Jakob; Mark O. Wielpütz

doi:10.1371/journal.pone.0172479

Abstract

T₁ maps have been shown to yield useful diagnostic information on lung function in patients with chronic obstructive pulmonary disease (COPD) and asthma, both for native T₁ and ΔT₁, the relative reduction while breathing pure oxygen. As parameter quantification is particularly interesting for longitudinal studies, the purpose of this work was both to examine the reproducibility of lung T₁ mapping and to compare T₁ found in COPD and asthma patients using IRSnapShotFLASH embedded in a full MRI protocol. 12 asthma and 12 COPD patients (site 1) and further 15 COPD patients (site 2) were examined on two consecutive days. In each patient, T₁ maps were acquired in 8 single breath-hold slices, breathing first room air, then pure oxygen. Maps were partitioned into 12 regions each to calculate average values. In asthma patients, the average T_1,RA = 1206ms (room air) was reduced to T_1,O2 = 1141ms under oxygen conditions (ΔT₁ = 5.3%, p < 5⋅10⁻⁴), while in COPD patients both native T_1,RA = 1125ms was significantly shorter (p < 10⁻³) and the relative reduction to T_1,O2 = 1081ms on average ΔT₁ = 4.2%(p < 10⁻⁵). On the second day, with T_1,RA = 1186ms in asthma and T_1,RA = 1097ms in COPD, observed values were slightly shorter on average in all patient groups. ΔT₁ reduction was the least repeatable parameter and varied from day to day by up to 23% in individual asthma and 30% in COPD patients. While for both patient groups T₁ was below the values reported for healthy subjects, the T₁ and ΔT₁ found in asthmatics lies between that of the COPD group and reported values for healthy subjects, suggesting a higher blood volume fraction and better ventilation. However, it could be demonstrated that lung T₁ quantification is subject to notable inter-examination variability, which here can be attributed both to remaining contrast agent from the previous day and the increased dependency of lung T₁ on perfusion and thus current lung state.

Citation: Triphan SMF, Jobst BJ, Anjorin A, Sedlaczek O, Wolf U, Terekhov M, et al. (2017) Reproducibility and comparison of oxygen-enhanced T₁ quantification in COPD and asthma patients. PLoS ONE 12(2): e0172479. https://doi.org/10.1371/journal.pone.0172479

Editor: Philipp Latzin, University Children’s Hospital Bern, SWITZERLAND

Received: September 15, 2016; Accepted: February 6, 2017; Published: February 16, 2017

Copyright: © 2017 Triphan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work has been supported by the Competence Network on Asthma / COPD (ASCONET) through a grant from the Bundesministerium für Bildung und Forschung (BMBF) of the federal government of Germany (01GI0870, www.bmbf.de/en). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

For lung diseases like chronic obstructive pulmonary disease (COPD), cystic fibrosis and asthma, global parameters available through spirometry are of limited value to monitor disease progression and treatment response on a lobar or segmental level. Non-invasive imaging methods dedicated to collect regional information on lung structure and function are considered a prerequisite for further clinical work in the field. The current clinical standard, computed tomography (CT), requires ionizing radiation and may thus be unfavorable for repeated measurements in long-term observational or interventional studies. In contrast, functional proton magnetic resonance imaging (MRI) can be repeated arbitrarily due to lack of radiation exposure [1, 2]. For instance, contrast agent-based perfusion measurements have been shown to be useful for visualizing lung function in the form of perfusion defects, exploiting the mechanism of hypoxic vasoconstriction [3, 4].

Alternatively, MRI allows for the measurement of a number of physical parameters of the investigated tissue, among which the T₁ relaxation time appears particularly interesting in the lungs [5–8]: T₁ depends on a number of morphological and functional parameters, including tissue composition and blood volume content. Importantly, since molecular oxygen (O₂) is paramagnetic, it reduces T₁, connecting this reduction to local ventilation. While most published work on COPD and asthma patients is based on the visual detection of T₁-weighted signal intensity changes induced by inhalation of pure oxygen [9, 10], others employed quantification of T₁ itself to produce potentially useful diagnostic information on regional lung ventilation and state [11–14]: T₁ and the oxygen-induced T₁ reduction were found to be significantly different in diseased areas of the lung and correlate with the GOLD stage in COPD patients. Since these approaches utilize only pure oxygen as an endogenic contrast agent or, when considering room air T₁ maps alone, no agent at all, they appear very well suited for imaging that accompanies therapy in a clinical setting. The fast quantification methods developed for T₁ mapping in the lungs can also be completed in very short breath-holds (≈ 6s) suitable for dyspnoeic patients.

For all forms of functional MR imaging, a primary goal of parameter quantification is to gain absolute values reflecting the physical characteristics of the tissue independent of the scanner environment being used. Such parameter mapping can be repeated at regular intervals and the measured values can be compared both between subjects and the same subject at different timepoints, which would be an advantage for longitudinal monitoring. Given the epidemiologic and economic importance of asthma and COPD, a suitable tool for functional lung imaging in these patients would be highly appreciated as biomarker for current and future research. However, MRI-based parametrization of lung tissue in COPD patients is particularly challenging due to the inherently lower lung signal in emphysematous lungs. Accordingly, the aims of this study were to confirm previously reported data on the characteristic lung T₁ values found in patients with COPD, provide comparable data for asthmatics in contrast to healthy subjects, and to investigate the intra-individual reproducibility of these values in repeated measurements.

Materials and methods

Patient selection

The study was carried out as part of a prospective trial (German Clinical Trials Register number DRKS00005072) approved by the institutional ethics committee, and conducted according to the recommendations of the review board. The study was approved by the Institutional Review Board of the Medical Faculty of the University of Heidelberg, Germany. All subjects gave written informed consent for examination and data evaluation. The work was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Patients were included based on clinical diagnoses as indicated by spirometry and [15] effectively divided into three groups: 12 patients with asthma and 12 patients with COPD were examined at site 1, further 15 COPD patients at site 2. The COPD patient group included GOLD [16] stages I to IV. Exclusion criteria were recent exacerbations, inability to hold breath for 10s and any contraindication for MR imaging.

MRI measurements

All measurements were performed on clinical 1.5T scanners (Magnetom Avanto, Siemens Medical Solutions, Erlangen, Germany) equipped with identical hard- and software at both study sites. An Inversion Recovery (IR) SnapShot FLASH sequence [6] was used in 8 coronal slices in the lungs of each patient, during one 6s expiratory breath-hold for each slice. Each measurement consists of 32 individual images, acquired with a short echo time of TE = 750 μs using an asymmetric readout to compensate for the short in lung tissue. At TR = 3ms, the time resolution for each image was 192ms with a matrix size of 64 × 128 in a 50 × 50cm² field of view and 15mm slices. The flip angle was chosen as α = 8°, corresponding with the effective Ernst angle in the lungs for an expected T₁ ≈ 1100ms.

This T₁ quantification experiment was repeated four times: First, under normoxic conditions, i.e. at 21% oxygen (O₂) in the breathing gas. Then, using a standard clinical oxygen mask, 100% O₂ was supplied at 15l/min to introduce hyperoxic conditions. After 3min to allow for a complete wash-in of oxygen [8], the entire experiment was repeated. The entire procedure was repeated on the following day, on average (22.9 ± 0.9)h later. This T₁ quantification experiment was embedded in a full protocol of morphological MRI sequences and followed by a functional DCE perfusion measurement that included the injection of an MRI contrast agent as reported previously (0.1 mmol/kg body weight Gd-DTPA, Magnevist, Bayer Schering Pharma AG, Berlin, Germany) [14, 17]. The total duration of the protocol was about 30min for each patient.

Prior to the patient measurements, T₁ mapping was performed using a specifically designed phantom and the same healthy volunteer on both used scanners [18] to ensure no discrepancies were introduced by the MRI equipment.

Data analysis and statistics

T₁ maps were calculated using an exponential parameter fit implemented to run directly on the scanner, producing parameter maps alongside the MR images according to [19]. To find T₁ values for the lungs, masks were drawn manually by a single observer to select the lungs on the acquired slices. To account for the large inhomogeneity of T₁ in diseased lungs (as shown below), these masks were then separated by software into 12 regions for each patient, providing upper, middle and lower regions for both lungs, each divided into an anterior and posterior volume. This masking is illustrated for one patient in Fig 1. Software to assist with masking and to perform the regional separation was written in MATLAB (Matworks, Natick, MA). Both median T₁ values for entire lung volumes and the median T₁ in these regions were calculated. As a measure of T₁ inhomogeneity, an intra-patient standard deviation σ_T1 for both room air and oxygen measurements was determined, from the median values within all regions.

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Fig 1. Anterior regions of interest in an asthma patient, generated from a manually drawn mask.

ROIs are shown in different colours.

https://doi.org/10.1371/journal.pone.0172479.g001

, the relative reduction of T₁ due to O₂ was also determined from T₁ at normoxic and hyperoxic conditions. For the reduction of T₁ due to oxygen in each group as well as the combined group of COPD patients from both sites, p-values were calculated according to a Wilcoxon signed-rank, as a normal distribution of values could not be assumed in this small sample size. For the difference in baseline T₁ in the asthma and COPD groups, a Wilcoxon rank-sum test was used to calculate p-values. Finally, T₁ measured on day 1 and day 2 was compared using the method of Bland and Altman [20]. Values of p < 0.05 taken from these tests were considered statistically significant.

Results

Fig 2 shows T₁ maps acquired in three different patients. The instances of fairly homogenous T₁ distribution in the lungs, as in Fig 2i–2l ut also highly inhomogenous cases such as in Fig 2a–2d, were the motivation for the masks drawn over the entire lungs to be split into regions as described above. To illustrate the comparability of slice and breathing position for the reproducibility measurements, parameter maps on concurrent days are shown as well. While notable inhomogeneity was also present in many of the COPD patient T₁ maps, on average significantly shorter T₁ values were prevalent compared to the asthmatics.

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Fig 2. Example T₁ maps, acquired in one asthma (a-d) and two COPD (g-k) patients from different GOLD stages.

The maps in the upper row were measured at 21%O₂ in the breathing gas, the lower row during administration of 100%O₂. For each patient, T₁ maps from equivalent slices on both measurement days are shown to illustrate reproducibility.

https://doi.org/10.1371/journal.pone.0172479.g002

Table 1 shows median T₁ values for in the entire lungs sorted by pathologies and sites. Considering the switch from room air to pure oxygen, statistically significant reductions were observed in all groups. Note that the standard deviations given in Table 1 are for the inter-patient variance of T₁. Due to the large inhomogeneity of T₁ in the examined pathologies, the average intra-patient standard deviation was σ_T1,RA = 141ms among the regions in each COPD patient’s lung and σ_T1,RA = 102ms in asthma patients. Comparing these average T₁ for all patients in a Wilcoxon rank-sum test gives p < 10⁻³ for the statistical difference between asthma and all COPD patients (with p < 10⁻² for the COPD measurements from single sites alone) but at p = 0.79 no statistical significance for the difference between the COPD measurements at different sites.

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Table 1. Median T₁ values over the entire lungs of patients.

https://doi.org/10.1371/journal.pone.0172479.t001

Fig 3 contains Bland-Altman plots [21] relating the difference between the T₁ measurements on consecutive days to the mean of both measurements, calculated from the median T₁ values taken from lung regions. As seen in Table 1, T₁ measured at the second day was found to be shorter on average in all patient groups. Notably, this systematic discrepancy is more prononounced at site 2. However, with a 95% confidence interval of 90ms, the inter-patient variance of this difference in the repeat measurements is large compared to the average of −26ms. This is equal to average relative differences of 2.2% for asthma and 2.1% for COPD patients at site 1 and 3.6% among COPD patients at site 2.

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Fig 3. Bland-Altman plots comparing the difference in T₁ under room air conditions measured at day 1 and day 2 to the average of both days.

Data from asthma and COPD patients examined at both sites is shown. The average difference is shown as a solid line and 95% confidence intervals (1.96σ) as dashed lines.

https://doi.org/10.1371/journal.pone.0172479.g003

The observed effect on T₁ of switching the breathing gas from room air (21% O₂) to 100% O₂ is displayed in Fig 4. The absolute difference in T₁ due to O₂ is shown with per-region median values for both days. While the reduction is larger in the asthma patients, it appears very similar in both groups of COPD patients.

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Fig 4. Bland-Altman plots relating the T₁-reduction due to pure oxygen to the mean of both T₁ values.

The lower row shows the reproducibility of T₁ under hyperoxic conditions on both days, analogous to Fig 3.

https://doi.org/10.1371/journal.pone.0172479.g004

Finally, the repeat measurements of T₁ under hyperoxic conditions also shown in Fig 4 again display a systematic tendency to shorter T₁ on the second day. With a 95% confidence interval of 85ms and average difference of −30ms, the errors in the hyperoxic T₁ measurements are similar to those of the base T₁, even though they include an additional imprecision introduced by the difficulty of achieving the same oxygen concentration in the breathing gas on both days. The absolute relative variation of the oxygen-induced ΔT₁ from day to day was found to be 23% for asthma and 30% for COPD. As all measurement errors contribute to this value, it is the least precise parameter produced in this study.

Discussion

The average T₁ values measured in the lungs of asthma and COPD patients in this study were found to be significantly shorter than values previously reported in healthy subjects, which tend to be between 1170ms and 1300ms [6, 8, 22]. Notably, average T₁ in asthma lies between the values for COPD and healthy subjects. This can be attributed to the effective lung T₁ being comprised of the longer T₁ provided by the blood fraction and a shorter T₁ component by the alveolar structure [23]. Accordingly, the reduced T₁ in diseased tissue likely reflects both reduced perfusion and pathological changes in the pulmonary tissue. This is also apparent in that the average standard deviation of T₁ within the lungs as a measure of inhomogeneity is higher in COPD than in asthma, even though T₁ itself is shorter. This further confirms the necessity for regional assessments of lung disease with quantitative imaging methods.

The precision of T₁ quantification in diseased lungs appears to suffer from reduced SNR due to low proton density. The large inhomogeneity T₁ within the lung volume caused by the varying degrees of emphysemal destruction itself also increases the need to ensure identical ROI and breathing state positions. However, even though it is considerably weaker than in healthy volunteers, the T₁ reduction due to pure oxygen is almost always visible and statistically highly significant over the study participants. Notably, T₁ quantification under hyperoxic conditions additionally loses accuracy since the administration of O₂ gas with conventional clinical masks is somewhat unreliable: The actual oxygen concentration will likely be less than 100% and vary slightly due to the adjustment of the breathing mask [24]. Nevertheless, statistically significant reductions of T₁ due to oxygen administration were found in all patient groups in this study and with 54ms and 56ms for COPD at sites 1 and 2 and 59ms for asthma patients, the 95% confidence intervals for the distribution of the reduction shown in Fig 4 are very similar. Interestingly, this reduction was on average lower than the 8% to 12% previously reported in healthy subjects [6, 8], confirming the diagnostic relevance of oxygen enhancement in chronic obstructive lung diseases [14].

As the lung masks were drawn manually, they may be inherently biased by observer input, limiting the parameter quantification. A fully automatic segmentation of images would be desirable to ensure unbiased reproducible measurements. Apart from the imprecision of T₁ quantification itself, a notable, though at the small patient numbers investigated here not statistically significant, lowering of T₁ from the first experiment day to the second was observed in this study. Several sources for this discrepancy can be proposed: Patients may become accustomed to the breathing commands given during the study protocol, leading to deeper exhalation on the second day. However, while on average smaller lung volumes were found on the second day, no correlation between volume and T₁ reduction was found (this is illustrated in S1 Fig). While in earlier publications, an effect of respiratory state on lung T₁ had been found [12], in more recent work no such dependency was found [25, 26]. In contrast, when observing oxygen-induced signal enhancement as a measure of ventilation, the change in proton density due to respiratory state may be an issue.

A very probable source for T₁ reduction is remaining contrast agent that was injected during the later steps in the study protocol during the first day: The mean serum elimination half-life of the employed 0.1mmol/kg Gd-DTPA dose is 1.6±0.1h [27] for healthy subjects with normal renal function. At this rate, less than 0.005% of the original dose would remain after 23h and no effect on lung T₁ should be measurable. However, chronic renal impairment commonly occurs in patients with COPD [28] and even though the study subjects were screened to have glomerular filtration rates (GFR) of at least 40ml/min, the elimination half-life of Gd-DTPA has been shown to increase to 4.2±2.0h at clearance rates between 30ml/min and 60ml/min [27]. Thus, assuming a relaxivity of 4.1l/s/mmol of Gd-DTPA in blood [29], the T₁ shortening from 1124ms to 1086ms observed in COPD patients at site 2 could be fully explained by remaining contrast agent if the mean elimination half-life is as long as 3.65h. Notably, this both requires significant renal impairment to be prevalent within the patient collective and provides no explanation for the larger reduction on site 2 in comparison to site 1, as contrast agent application was identical on both sites to ensure comparability of perfusion measurements and thus likely accounts for only part of the discrepancy in T₁. A change in the experiment setup can be discounted entirely, since measurements were distributed over a long period with repeat on consecutive days. As such, changes in circumstances would at best affect different patients but not intra-patient repeat measurements.

Finally, as stated above, T₁ in the lungs, especially in patients, is strongly affected by perfusion which is in turn influenced by ventilation through hypoxic vasoconstriction [2–4, 30]. This means that even short-term influences on lung function may have an effect on the observable T₁, which is after all what the measurement is intended for. Though changes of therapeutic treatments between the two MRI sessions were avoided, the study protocol includes both a large number of very short breath-holds and an administration of pure oxygen, which comes up to physical therapy for the patients. As such, the observed site-dependent lowering of T₁ on consecutive days both highlights the difficulty to achieve repeatable measurements and the sensitivity of the T₁ quantification to changes in lung vital status.

In its entirety, this study emphasizes multiple difficulties in T₁ mapping in COPD and asthma patients: The IRSnapShot FLASH sequence as employed here requires only very short breath-holds, but suffers from the low proton density in emphysematous tissue and unsteady depth of repeated breath-holds. The short measurement times determined by T₁ relaxation also limit the amount of signal that can be acquired. To adress these challenges, MRI sequences that improve on the basic IRSnapShot FLASH by employing ultrashort echo times during free breathing [31, 32] or a balanced steady-state fast precession (bSSFP)-based readout [26] have been demonstrated and applied to COPD [25], though the reproducibility of these methods in patients remain to be tested.

Conclusion

In this work, the characteristically short lung T₁ values previously reported in COPD were confirmed along with smaller ΔT₁ induced by the administration of pure O₂ than commonly observed in healthy subjects. In addition, an average T₁ in asthma patients was found to lie between values typical for healthy volunteers and COPD patients. The lung T₁ values found display both large inter-patient and intra-patient variations, with inhomogenous T₁ within the lungs also being distinctive for diseased lungs. In the reproducibility measurements, relevant variability within data from day 1 was found, for the first time presenting a range of measurement variation for T₁ values in the diseased lung in the short term, but also underlining the sensitivity of T₁ mapping to physiological conditions. The possible influence of remaining contrast agent on the repeated T₁ measurements also highlights the need to consider time intervals for quantitative measurements within longitudinal studies with the specific pathology in mind: An interval that is reasonable for healthy volunteers may be insufficient for patients with impaired renal function.

Supporting information

S1 Fig. Correlation of ROI areas and T₁ differences.

a: Bland-Altman plot of the number of voxels n_v in each ROI at both measurement days. b: The relative change in n_v compared to the relative change in T₁ from day 1 to day 2.

https://doi.org/10.1371/journal.pone.0172479.s001

(EPS)

Author Contributions

Conceptualization: ST BJ OS SL CD JB HK PJ MW.
Data curation: ST BJ MW.
Formal analysis: ST BJ MW.
Funding acquisition: ST BJ OS UW SL CD JB HK PJ MW.
Investigation: ST BJ AA OS UW MT CH SL CD JB HK PJ MW.
Methodology: ST BJ AA OS UW MT CH SL CD JB HK PJ MW.
Project administration: ST BJ OS UW JB HK PJ MW.
Resources: SL CD JB HK PJ.
Software: ST.
Supervision: SL CD JB HK PJ MW.
Validation: ST BJ HK MW.
Visualization: ST BJ.
Writing – original draft: ST BJ AA OS UW MT CH SL JB HK PJ MW.
Writing – review & editing: ST BJ AA OS UW MT CH SL CD JB HK PJ MW.

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Figures

Abstract

Introduction

Materials and methods

Patient selection

MRI measurements

Data analysis and statistics

Results

Discussion

Conclusion

Supporting information

S1 Fig. Correlation of ROI areas and T1 differences.

Author Contributions

References

S1 Fig. Correlation of ROI areas and T₁ differences.