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DOI:10.2214/AJR.05.0135
AJR 2007; 188:48-56
© American Roentgen Ray Society

Clinical Observations

Primary Pulmonary Hypertension: 3D Dynamic Perfusion MRI for Quantitative Analysis of Regional Pulmonary Perfusion

Yoshiharu Ohno1, Hiroto Hatabu2, Kenya Murase3, Takanori Higashino1, Munenobu Nogami1, Takeshi Yoshikawa1 and Kazuro Sugimura1

1 Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan.
2 Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA 02215.
3 Department of Medical Engineering, Division of Allied Health Sciences, Osaka University Medical School, Osaka, Japan.

Received January 27, 2005; accepted after revision July 25, 2005.

 
This work was partially supported by Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology (JSTS.KAKENHI no. 16790729).

Y. Ohno, K. Murase, and K. Sugimura partially supported by Schering Japan.

Address correspondence to Y. Ohno (yosirad{at}kobe-u.ac.jp).


Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
OBJECTIVE. The purpose of this study was to determine whether quantitative pulmonary perfusion parameters obtained from 3D dynamic contrast-enhanced MR perfusion data can be used to assess the severity of primary pulmonary hypertension (PPH) as indicated by pulmonary vascular resistance (PVR) and mean pulmonary artery pressure (MPAP).

CONCLUSION. Three-dimensional dynamic contrast-enhanced MRI has potential for assessment of disease severity as indicated by PVR and MPAP in patients with PPH.

Keywords: chest • lung • MRI • perfusion • pulmonary hypertension


Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
Primary pulmonary hypertension (PPH) is a progressive disorder characterized by increased pulmonary arterial pressure and pathologic changes in precapillary pulmonary arteries. In the U.S. National Institutes of Health registry, PPH is defined as mean pulmonary artery pressure (MPAP) greater than 25 mm Hg at rest (30 mm Hg with exertion) in the absence of heart disease, chronic thromboembolic disease, underlying pulmonary disorder, or secondary causes [1]. An increase in pulmonary vascular resistance (PVR) and subsequent compensatory right ventricular hypertrophy leads to an increase in pulmonary pressure that often results in increased right ventricular afterload and failure. The disorder progresses, leading to right heart failure and death a median of 2.8 years after diagnosis [2, 3].

In several studies in the late 1970s and early 1980s investigators measured the diameter of central pulmonary arteries using CT findings to predict the presence of pulmonary hypertension [4-6]. Results of a few studies in the late 1990s suggested the utility of velocity-encoded MRI with the phase-contrast technique as a noninvasive method of assessment of MPAP and PVR [7, 8]. Although these radiologic methods make it possible to evaluate the severity of pulmonary hypertension, Doppler echocardiography has been most frequently used for assessment of pulmonary hypertension in clinical situations. Inefficient ventilation due to pulmonary perfusion abnormalities in PPH patients cannot be evaluated with the aforementioned techniques. The only radiologic method of quantitative and qualitative assessment of pulmonary perfusion abnormalities in patients with PPH has been perfusion scintigraphy [9-11].

Advances in gradient systems and improved MR sequences have made it possible for us to develop a 3D gradient-recalled echo sequence with ultrashort TR/TE of less than 3.0 ms/1.0 ms. With this sequence, we can evaluate qualitative regional perfusion differences in the entire lung with high temporal resolution [12, 13]. Contrast-enhanced, first-pass dynamic MRI in quantitative assessment of the absolute value of cerebral blood flow according to the indicator dilution theory has been described [14-16]. Other investigators [17-19] have found that 2D contrast-enhanced, first-pass dynamic perfusion MRI of the lung can be used to evaluate regional quantitative pulmonary perfusion parameters. The findings were validated in a pig model of pulmonary embolism. In addition, we [20] have found excellent correlation and small limits of agreement between calculated semiquantitative pulmonary perfusion from 3D contrast-enhanced, first-pass dynamic MR perfusion data and findings of nuclear medicine studies of patients with lung cancer. Consequently, using indicator dilution theory and fuzzy cluster analysis, we have developed software for calculation of quantitative pulmonary perfusion parameters—that is, pulmonary blood flow (PBF), mean transit time (MTT), and pulmonary blood volume (PBV)— from 3D contrast-enhanced, first-pass dynamic perfusion MRI data. We have used this software to assess regional pulmonary perfusion and the differences between healthy volunteers and patients with primary and secondary pulmonary hypertension [21].


Figure 1
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  		Fig. 1A —34-year-old healthy female volunteer. PC display of regions of interest. Square = right pulmonary parenchyma, circle = main pulmonary artery trunk, triangle = left pulmonary parenchyma.

 


Figure 2
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  		Fig. 1B —34-year-old healthy female volunteer. Graph shows signal intensity-time course curves. Arterial input function was obtained from signal intensity-time course curve of main trunk of pulmonary artery (circles). Squares = right pulmonary parenchyma, triangles = left pulmonary parenchyma, AU = arbitrary units.

 


Figure 3
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  		Fig. 1C —34-year-old healthy female volunteer. Graph shows signal intensity-time course curves of right pulmonary parenchyma (squares) and left pulmonary parenchyma (triangles) at lower signal intensity than B. AU = arbitrary units.

 
In this study, we hypothesized that 3D ultrafast dynamic contrast-enhanced MRI performed with indicator dilution technique may enable quantitative assessment of regional pulmonary perfusion abnormalities and allow correlation with disease severity indicated by increased PVR and MPAP in PPH patients. The purpose of our study was to determine the usefulness of quantitative pulmonary perfusion parameters—that is, PBF, MTT, and PBV—evaluated from 3D dynamic contrast-enhanced perfusion MRI data in the assessment of disease severity in PPH patients.


Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Three-dimensional dynamic contrast-enhanced MRI was performed on 14 healthy volunteers (11 men, three women; age range, 28-48 years; mean age, 34 years) and 14 patients with PPH (five men, nine women; age range, 20-65 years; mean age, 41 years). For all patients PPH was defined as MPAP greater than 25 mm Hg at rest with a normal pulmonary artery wedge pressure (< 12 mm Hg) measured with catheterization of the right side of the heart. These procedures were performed within 96 hours of each other (mean, 53 hours). No change in drug treatment and no clinically important evolution of disease occurred in the interval between the two examinations. Our institutional review board approved this study, and informed consent was obtained from each subject before the study.

Dynamic Contrast-Enhanced MRI
All MR studies were performed with a 1.5-T superconducting magnet (Gyroscan Intera, Philips Medical Systems) and a body coil as transmitter and receiver. Dynamic MR images (TR/TE, 2.7 ms/0.6 ms; flip angle, 40°; matrix size, 128 x 96; 256 x 192 reconstructed matrix; rectangular field of view, 450-530 x 315-371 mm) were acquired with a 3D radiofrequency spoiled gradient-recalled echo sequence.

For examinations of healthy volunteers, 3D slab thickness of 120-240 mm was used with 10-12 partitions, an overcontiguous slice technique in the coronal plane, and left to right phase encoding, resulting in an effective partition thickness of 10-20 mm and real phase encoding in the slice direction of five or six steps. Temporal resolution was 1.1 seconds for each 3D data set. For patient examinations, a 3D slab thickness of 100-120 mm was used with 10-12 partitions, an overlapping slice in the coronal plane, and left to right phase encoding, resulting in an effective partition thickness of 10 mm and real phase encoding in the slice direction of five or six steps. Temporal resolution was 1 second for each 3D data set.

All subjects were given a 2-mL bolus of gadopentetate dimeglumine (Magnevist, Schering) through a cubital vein. An automatic infusion system (Sonic shot, Nemoto) was used to administer the bolus at a rate of 5 mL/s, which was followed by 20 mL of saline solution at the same rate. The basic theory and application of contrast-enhanced dynamic perfusion MRI have been documented in previous reports [12, 13, 17-21]. After careful instruction, patients practiced the breath-holding technique to reproduce a consistent degree of inspiration for each scan series before the MRI studies. For each scan, 20 images were obtained during a breath-hold at end inspiration.

Data Analysis of Dynamic Contrast-Enhanced Perfusion MRI
Dynamic MRI data were analyzed with proprietary software developed with MATLAB (Math-works) running on a PC (FMV-7000TX, Fujitsu) (Figs. 1A, 1B, and 1C). Signal intensity was measured for each voxel in each slice of dynamic MRI data. According to the indicator dilution theory [22] for intravascular contrast agents, when the arterial input function of the contrast agent entering the volume of interest is known, pulmonary blood flow (PBF) is implicitly represented by equation 1:

Formula 1(1)
where CVOI(t) and CAIF(t) are the time-dependent concentrations of contrast agent in the volume of interest and the arterial input function, respectively. R(t) is the residue function, which is the relative amount of contrast agent in the volume of interest in an idealized perfusion experiment, in which a unit area bolus is instantaneously injected [R(0) = 1] and subsequently washed out by perfusion [R({infty}) = 0]. It was known from equation 1 that the initial height of the deconvolved time-concentration curve equals PBF.

There were several approaches to calculating R(t) from equation 1 by deconvolution. In this study, we adopted an algebraic approach based on singular value decomposition, which was robust against statistical noise. The details of this approach have been described elsewhere [23-25]. We generated PBF maps by applying this approach pixel by pixel. Arterial input function was obtained from the main trunk of the pulmonary artery by use of fuzzy clustering [15].

Pulmonary blood volume (PBV) was represented by equation 2:

Formula 2(2)

According to the central volume principle, mean transit time (MTT) was obtained with the equation 3:

Formula 3(3)

The details of data analysis of dynamic contrast-enhanced perfusion MRI are described in an earlier report [21].

Right-Heart Catheterization
In each patient, a Swan-Ganz catheter (Baxter Healthcare) was advanced into the main pulmonary artery after puncture of the brachial or jugular vein, and MPAP, pulmonary capillary wedge pressure, cardiac output, and PVR (derived from the difference between MPAP and pulmonary capillary wedge pressure divided by cardiac output) were measured. Cardiac output was measured with standard methods for 4 minutes on three to five occasions, and the results were averaged.

Statistical Analysis
To compare the regional PBF, PBV, and MTT between healthy subjects and patients with PPH, a chest radiologist with 10 years of experience generated regions of interest on every slice of calculated quantitative PBF, PBV, and MTT maps. These regions of interest measured 10 mm in diameter and were located in the right upper, right middle, right lower, left upper, left middle, and left lower areas of the lung, excluding large vessels. Because there was no lung area on slice number 12 in all healthy volunteers, slice number 12 was excluded in quantitative pulmonary perfusion parameter graphs. To compare the difference in regional pulmonary perfusion parameters between healthy and PPH subjects, mean regional PBF, PBV, and MTT were statistically compared by Student's t test. To determine the usefulness of quantitative pulmonary perfusion parameters in assessment of disease severity in PPH patients, PVR and MPAP were correlated with pulmonary perfusion parameters. A p value less than 0.05 was considered significant in all statistical analyses.


Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
All 3D dynamic contrast-enhanced perfusion MRI examinations were successfully completed. No adverse effects were detected. Pulmonary perfusion parameters and subject characteristics are shown in Table 1. PBF, PBV, and MTT maps of representative cases of healthy and PPH subjects are shown in Figures 2A, 2B, 2C, 3A, 3B, and 3C. Comparisons of mean regional PBF, PBV, and MTT between healthy volunteers and PPH patients are shown in Table 2. The mean regional PBF of healthy volunteers was significantly different from that of PPH patients (p < 0.0001). The mean regional MTT of healthy volunteers also was significantly different from that of PPH patient (p < 0.0001).


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  		TABLE 1: Pulmonary Perfusion Parameters and Subject Characteristics

 

Figure 4
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  		Fig. 2A —33-year-old healthy man. Quantitative pulmonary perfusion parameter maps on one of 10 slices. Pulmonary blood flow (PBF) map shows regional changes in PBF in both lungs. Mean regional PBF in this slice is 128.3 ± 3.3 mL/100 mL/min.

 

Figure 5
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  		Fig. 2B —33-year-old healthy man. Quantitative pulmonary perfusion parameter maps on one of 10 slices. Pulmonary blood volume (PBV) map shows regional changes in PBV in both lungs. Mean regional PBV in this slice is 11.7 ± 0.7 mL/100 mL.

 

Figure 6
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  		Fig. 2C —33-year-old healthy man. Quantitative pulmonary perfusion parameter maps on one of 10 slices. Mean transit time (MTT) map shows regional changes in MTT in both lungs. Mean regional MTT in this slice is 4.3 ± 0.7 seconds.

 

Figure 7
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  		Fig. 3A —42-year-old woman with primary pulmonary hypertension. Quantitative pulmonary perfusion parameter maps on one of 10 slices. Pulmonary blood flow (PBF) map clearly shows decreased PBF in both lungs. Mean regional PBF in this slice is 43.9 ± 4.9 mL/100 mL/min.

 

Figure 8
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  		Fig. 3B —42-year-old woman with primary pulmonary hypertension. Quantitative pulmonary perfusion parameter maps on one of 10 slices. Pulmonary blood volume (PBV) map clearly shows decreased PBV in both lungs. Mean regional PBF in this slice is 5.8 ± 2.5 mL/100 mL.

 

Figure 9
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  		Fig. 3C —42-year-old woman with primary pulmonary hypertension. Quantitative pulmonary perfusion parameter maps on one of 10 slices. Mean transit time (MTT) map clearly shows prolonged MTT in both lungs. Mean regional MTT in this slice is 6.9 ± 1.2 seconds.

 

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  		TABLE 2: Comparison of Quantitative Pulmonary Perfusion Parameters Between Healthy and Primary Pulmonary Hypertension (PPH) Subjects

 

Correlation between PBF and PVR is shown in Figure 4. PBF had good negative correlation with PVR. Correlation between MTT and PVR is shown in Figure 5. MTT and PVR had moderate positive correlation. Correlation between PBF and MPAP is shown in Figure 6. PBF had moderate negative correlation with MPAP. Correlation between MTT and MPAP is shown in Figure 7. MTT and MPAP had fair positive correlation.


Figure 10
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  		Fig. 4 —Graph shows correlation between pulmonary blood flow (PBF) and pulmonary vascular resistance (PVR) in patients with primary pulmonary hypertension. Good negative correlation was observed between PBF and PVR (r = -0.79, r2 = 0.62, p < 0.001).

 

Figure 11
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  		Fig. 5 —Graph shows correlation between mean transit time (MTT) and pulmonary vascular resistance (PVR) in patients with primary pulmonary hypertension. Moderate positive correlation was observed between MTT and PVR (r = 0.60, r2 = 0.36, p = 0.022).

 

Figure 12
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  		Fig. 6 —Graph shows correlation between pulmonary blood flow (PBF) and mean pulmonary arterial pressure (MPAP) in patients with primary pulmonary hypertension. Moderate negative correlation was observed between PBF and MPAP (r = -0.70, r2 = 0.49, p = 0.005).

 

Figure 13
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  		Fig. 7 —Graph shows correlation between mean transit time (MTT) and mean pulmonary arterial pressure (MPAP) in patients with primary pulmonary hypertension. Fair positive correlation was observed between MTT and MPAP (r = 0.54, r2 = 0.29, p = 0.048).

 

Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Our results show 3D dynamic contrast-enhanced perfusion MRI performed with the indicator dilution technique may enable quantitative assessment of regional pulmonary perfusion abnormalities and disease severity identified according to PVR and MPAP in patients with PPH. To our knowledge, this report is the first of the use of 3D dynamic contrast-enhanced MRI for noninvasive assessment of severity of PPH in quantitative analysis of changes in regional pulmonary perfusion parameters caused by underlying pathophysiologic conditions. We believe the results are the first to show the relation between quantitatively analyzed changes in regional pulmonary perfusion parameters and the findings at cardiac catheterization studies.

In our study, quantitative assessment of regional pulmonary perfusion parameters with 3D dynamic contrast-enhanced perfusion MRI showed significant differences in PBF and MTT between healthy and PPH subjects. PBV was not significantly different between the two groups. These results are compatible with underlying physiopathologic and pathobiologic findings in PPH patients [26-33].

Past pathobiologic analyses [28, 30] have shown that vasoconstriction, vascular wall remodeling, and thrombosis in situ progress to smooth-muscle hypertrophy, intimal hyperplasia, and in situ thrombosis. These three elements also increase PVR in PPH patients. Therefore the pathophysiologic hallmark of PPH has been defined as increases in PVR that increase pressure in the pulmonary circulation. In general, PVR has been defined in equation 4 as follows:

Formula 4(4)
where PCWP is pulmonary capillary wedge pressure, CO is cardiac output, and BP is blood pressure. Therefore significant differences between healthy and PPH subjects were found for mean PBF because PBF was directly affected by increased PVR and pressure within the pulmonary circulation.

Although the mean PBV of PPH patients was lower than that of healthy subjects, our results showed that there was no significant difference in mean PBV between healthy and PPH subjects. In PPH patients, proliferation of intimal and adventitial tissue follows vasoconstriction, and PVR increases as the disease progresses. Thrombosis may result from injury to the endothelium, abnormal fibrinolysis, enhanced procoagulant activity, and platelet abnormalities [33]. As a result of these changes, luminal diameter decreases markedly, and PVR may increase. Ultimately, the lumen may be completely obliterated, the overall number of small vessels greatly diminished, and the volume of the pulmonary capillary bed decreased. Therefore decreased pulmonary capillary bed volume is less sensitive to disease severity than is PVR, and mean PBV may be a less sensitive pulmonary perfusion parameter than PBF in the diagnosis of PPH.

The mean MTT of PPH patients was significantly different from that of healthy subjects. MTT was determined with equation 3 on the basis of the indicator dilution theory. Therefore, for equations 3 and 4, the significant difference in mean MTT between healthy and PPH subjects may be influenced mainly by the significant difference in PBF.

In usefulness in assessment of disease severity in PPH patients, PBF showed good and moderate negative correlation with PVR (r = -0.79) and MPAP (r = -0.70), and MTT showed moderate positive correlation with PVR (r = 0.60) and fair positive correlation with MPAP (r = 0.54). In regard to the pathophysiologic features of PPH, progressively increased PVR in the lung parenchyma is the main cause of PPH, and increases in MPAP and right ventricular hypertrophy due to increased PVR cause respiratory abnormalities in PPH patients. Therefore correlation coefficients with PVR were better than those with MPAP for both quantitative regional pulmonary perfusion parameters. Moreover, in this study, PBF and PBV were directly calculated from the signal intensity-time course curve of dynamic contrast-enhanced perfusion MRI data. MTT, however, was determined with equation 3 on the basis of the indicator dilution theory and was considered to reflect calculated PBF and PBV. Therefore our preliminary results suggest that for assessment of disease severity indicated by PVR and MPAP in PPH patients, PBF may be considered more sensitive and less affected by other parameters than is MTT.

There were some limitations to the study. First, although all dynamic perfusion MRI examinations were successfully completed without adverse effects and we completely calculated regional perfusion from signal intensity-time course curves, one PPH patient needed to breathe shallowly for data acquisition. Thus the image quality was slightly degraded 17.6-22 seconds after bolus injection of contrast medium. In patients with severe PPH in which pulmonary function is low, poor breath-hold capability may result in underestimation or overestimation of regional perfusion and regional pulmonary function due to motion misregistration artifact. However, 3D dynamic perfusion MRI is a new technique for assessment of quantitative regional pulmonary perfusion, and the advances in fast scanning time and phase-encoding schemes for reduction of motion artifact may make this technique feasible.

Second, some investigators [17-21, 34, 35] have suggested that direct application of the central volume principle and indicator dilution theory to dynamic contrast-enhanced perfusion MRI is difficult for determination of perfusion parameters, although these methods have been frequently used in various contrast-enhanced perfusion MRI techniques. In the aforementioned techniques, regional pulmonary perfusion parameters were directly calculated from the area under the curve of the observed tissue. However, calculated regional perfusion parameters were affected by changes in signal intensity for specific tissue regions of interest, which reflected the tracer concentration within the tissue rather than that leaving the tissue. Considering the strict mathematic modeling of the microvasculature, Weisskoff et al. [34] suggested as semiquantitative parameters the pulmonary perfusion parameters calculated from the aforementioned theories. In addition, the indicator dilution theory is primarily applied to IV contrast agents. This theory is suitable for extracellular gadolinium agents in brain perfusion because of the blood-brain barrier. Therefore, when this theory is used for calculation of pulmonary perfusion, the effect of extravasation of this contrast agent may be greater on pulmonary perfusion parameters in terms of different permeability of water spins between normal and diseased pulmonary arteries and between young and old patients. We interpreted the data with the knowledge of this limitation, even though our software provided quantitative pulmonary perfusion parameters.

Third, it was necessary for us to prove the linear relation between observed signal intensity and concentration of contrast medium within the blood for direct calculation of regional perfusion parameters from dynamic contrast-enhanced perfusion MR images [17-21, 34, 35]. In the study, we used small amounts of contrast medium. Within a limited range of contrast concentration, the linear relation between signal intensity and contrast concentration was maintained [35]. Therefore, it was very important for us to use low-dose boluses of contrast medium to avoid saturation effects due to high concentration during the bolus peak that would compromise quantification of the perfusion parameters.

Fourth, distribution volume of contrast agent reflects blood volume within the tissue, if the contrast agent behaves as an intravascular agent. Gadolinium contrast medium leaves the vascular space and distributes within interstitial space in the lungs. Therefore distribution volume is not equal to blood volume, and the signal intensity-time course curve does not fully return to equilibrium level over the measurement time.

The fifth limitation was that our study population was small. In addition, although we found significant differences in pulmonary perfusion parameters between healthy and PPH patients and significant correlation among PBF, MTT, and disease severity indicated by increased PVR and MPAP, we did not directly compare these changes in perfusion parameters among 3D dynamic contrast-enhanced MRI perfusion data, other MRI techniques or analyses, or nuclear medicine methods such as SPECT and PET. We also did not show reproducibility; inherent error; or interobserver, intrasubject, or interpopulation variabilities of this technique in healthy subjects and PPH patients. To show the real utility of 3D dynamic contrast-enhanced perfusion MRI in noninvasive assessment of disease severity in PPH patients, we must perform a large prospective trial using quantitative analyzed regional pulmonary perfusion parameters with comparison among 3D dynamic contrast-enhanced MR perfusion data, other MR techniques and analyses, and nuclear medicine techniques as warranted.

In conclusion, 3D dynamic contrast-enhanced perfusion MRI has potential for assessment of disease severity in PPH patients. Moreover, quantitative assessment of regional pulmonary perfusion parameters from 3D dynamic contrast-enhanced perfusion MR images of patients with PPH may offer the opportunity for noninvasive physiologic and pathophysiologic evaluation of the lungs with relatively high spatial resolution and without radiation exposure.


Acknowledgments
 
We thank Sumiaki Matsumoto, Department of Radiology, Kobe University Graduate School of Medicine, Daisuke Takenaka, Department of Radiology, Kasai Municipal Hospital, and Masahiko Fujii, Division of Radiology, Kobe University Hospital, for their contributions to this manuscript.


References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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