Home Multibreath Hyperpolarized3 He Imaging Scheme to Measure Alveolar Oxygen Tension and Apparent Diffusion Coefficient
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Multibreath Hyperpolarized3 He Imaging Scheme to Measure Alveolar Oxygen Tension and Apparent Diffusion Coefficient

Rationale and Objectives

In this study, we compared a newly developed multibreath simultaneous alveolar oxygen tension and apparent diffusion coefficient ( P A O 2 -ADC) imaging sequence to a single-breath acquisition, with the aim of mitigating the compromising effects of intervoxel flow and slow-filling regions on single-breath measurements, especially in chronic obstructive pulmonary disease (COPD) subjects.

Materials and Methods

Both single-breath and multibreath simultaneous P A O 2 -ADC imaging schemes were performed on a total of 10 human subjects (five asymptomatic smokers and five COPD subjects). Estimated P A O 2 and ADC values derived from the different sequences were compared both globally and regionally. The distribution of voxels with nonphysiological values was also compared between the two schemes.

Results

The multibreath protocol decreased the ventilation defect volumes by an average of 12.9 ± 6.6%. The multibreath sequence generated nonphysiological P A O 2 values in 11.0 ± 8.5% fewer voxels than the single-breath sequence. Single-breath P A O 2 maps also showed more regions with gas-flow artifacts and general signal heterogeneity. On average, the standard deviation of the P A O 2 distribution was 16.5 ± 7.0% lower using multibreath P A O 2 -ADC imaging, suggesting a more homogeneous gas distribution. Both mean and standard deviation of the ADC increased significantly from single- to multibreath imaging ( p = 0.048 and p = 0.070, respectively), suggesting more emphysematous regions in the slow-filling lung.

Conclusion

Multibreath P A O 2 -ADC imaging provides superior accuracy and efficiency compared to previous imaging protocols. P A O 2 and ADC maps generated by multibreath imaging allowed for the qualification of various regions as emphysematous or obstructed, which single-breath P A O 2 maps can only identify as defects. The simultaneous P A O 2 and ADC measurements generated by the presented multibreath method were also more physiologically realistic, and allowed for more detailed analysis of the slow-filling regions characteristic of COPD subjects.

Introduction

Hyperpolarized (HP) gas magnetic resonance imaging (MRI) has recently emerged as a technique capable of safely detecting a variety of changes in the lungs such as air flow restriction, airspace enlargement, decreased tissue perfusion, alveolar membrane thickening, and air trapping ( ), thereby expanding upon the parameters available from conventional modalities like computed tomography and x-ray imaging, and enabling the accurate, regional evaluation of gas flow and uptake ( ).

Two parameters that HP gas MRI is uniquely capable of assessing are regional alveolar oxygen tension ( P A O 2 ), which reflects oxygen delivery to the parenchyma and uptake into the blood, and apparent diffusion coefficient (ADC), which describes the diffusion of gas within various regions of the lung. Because P A O 2 reflects the balance between gas replacement through ventilation and uptake into the blood, it is sensitive to early manifestations of chronic obstructive pulmonary disease (COPD), an inflammatory lung condition causing chronic obstruction of airflow. ADC, on the other hand, is primarily indicative of structural changes in alveolar size and connectivity that are expected later in disease progression ( ). Often acquired simultaneously and coregistered, P A O 2 and ADC can serve as important indicators of pathophysiological changes in the lungs ( ), and together provide a more comprehensive portrait of lung function and structure than is available with other methods—increasing sensitivity to early subclinical alterations and, thereby, diagnostic power ( ).

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Materials and Methods

Demographics

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Table 1

Demographics and a Subset of Clinical Test Results

Subject Demographics PFTs Age (Years) TLC (L) Height (in) Weight (lb) BMI (kg/m 2 ) FEV 1 (L) FEV 1 /FVC (%) RV/TLC (%) DL CO (mL/min/mmHg) Pred. FEV 1 (%) Pred. DL CO (%) Life Impact SGRQ Overall (%) AS 1 58 6.01 64.5 141.7 23.9 2.97 78 34 19.29 117 91 15.9 AS 2 64 5.95 70.5 167 23.6 2.88 71 32 19.75 93 88 6.51 AS 3 67 6.91 66.5 198 31.5 2.81 72 46 23.73 110 93 29 AS 4 62 6.55 71.5 176 23.9 3.01 77 36 33.46 80 127 10.39 AS 5 31 7.87 75 207 25.9 5.19 80 16 34.2 101 92 2.64 Mean ± SD 56.4 ± 13.03 6.66 ± 0.79 69.6 ± 3.72 177.9 ± 23.2 25.76 ± 2.98 3.37 ± 0.91 75.6 ± 3.50 32.8 ± 9.68 26.09 ± 6.51 100.2 ± 12.95 98.2 ± 14.50 12.89 ± 9.17 COPD 1 70 6.57 69 184 27.2 0.88 35 59 8.7 28 37 51.5 COPD 2 68 5.04 63.5 158.5 27.2 1.54 58 46 12.44 83 73 18.88 COPD 3 60 9.24 67 178 27.9 1.26 40 62 14.66 39 59 29.79 COPD 4 55 5.14 67 180 28.2 2.29 71 41 16.07 92 68 57.16 COPD 5 61 3.71 63 134 23.7 0.67 49 62 8.95 27 51 7.727 Mean ± SD 62.8 ± 6.14 5.94 ± 2.10 65.9 ± 2.56 166.9 ± 20.9 26.84 ± 1.81 1.33 ± 0.63 51.8 ± 16.6 54.0 ± 9.82 12.16 ± 3.31 53.8 ± 31.28 57.6 ± 14.28 33.01 ± 21.06 P (T ≤ t) 0.406 0.542 0.268 0.587 0.548 0.036 0.036 0.038 0.014 0.080 0.006 0.088

BMI, body mass index; COPD, chronic obstructive pulmonary disease; SD, standard deviation; SGRQ, St. George’s Respiratory Questionnaire.

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Measurement of P A O 2

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1T1=0.45(299T)0.42[O2] 1

T

1

=

0.45

(

299

T

)

0.42

[

O

2

]

where T 1 , the longitudinal relaxation time, T , body temperature, and [O 2 ], oxygen concentration, are measured in s, K, and Amagat, respectively. At body temperature, Equation 1 yields:

T1=EPAO2 T

1

=

E

P

A

O

2

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Mn=M0cosn*NPE(α)×exp[−PAO2*tn(k)ξ] M

n

=

M

0

co

s

n

*

N

P

E

(

α

)

×

exp

[

P

A

O

2

*

t

n

(

k

)

ξ

]

where M 0 is the initial magnetization level of 3 He, N PE is the number of phase-encode gradients for each imaged slice, and α represents the flip angle. t n ( k ) is the start time of the n th acquisition for the k th slice.

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MA(j)=FV*MS+(1−FV).MA(j−1).exp(DA),MA(0)=0 M

A

(

j

)

=

FV

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M

S

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1

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Simultaneous Measurement of P A O 2 and ADC

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ADC=1b0(logS0S1) ADC

=

1

b

0

(

log

S

0

S

1

)

in which S 0 / S 1 corresponds to the measured signal intensity without/with diffusion encoding. The parameter b 0 combines the strength and duration of the gradients, as is standard for liquid and gas diffusion measurements by MRI. Although the numerical value of ADC can vary with gradient duration even as b 0 is held constant, we utilize the commonly chosen standard diffusion pulse timing parameters with no interpulse delay ( ).

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Imaging Parameters

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Figure 1, Newly presented multibreath sequence designed to generate measurements of alveolar O 2 tension and gas diffusion. Each slice uses a set of ADC with two interleaved b values as well as one set of slice-interleaved GRE in a “AABB … EEFFAABB … EEFF” pattern. The ADC image with b 0 = 0 for each slice and the two last back-to-back interleaved GRE images are sufficient for P A O 2 calculations (three time points to decouple flip angle depolarization and oxygen relaxation). ADC, apparent diffusion coefficient; GRE, gradient echo. (Color version of figure is available online.)

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Imaging Studies

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Gas Delivery Device/System

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Figure 2, ( a ) Gas delivery device piping and instrumentation diagram. ( b ) Gas delivery prototype and Biopac MP150 instrumentation setup used in the study. (Color version of figure is available online.)

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HP 3 He Production

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Data Analysis

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Homogeneity=∑i,jp(i,j)1+|i−j| Homogeneity

=

i

,

j

p

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i

,

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)

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Statistical Analyses

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Results/Discussion

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Figure 3, Spin density maps obtained from representative COPD subjects: COPD 1: age 70 years, BMI 27.2, FEV 1FVC 0.35, DL CO 37% Pred., RV/TLC 62% ( a ); and COPD 5: age 61 years, BMI 23.7, FEV 1FVC 0.49, DL CO , 51% Pred., RV/TLC 59% ( b ), including both single-breath images (top) and last time-point (seventh breath) multibreath images (bottom). Yellow arrows indicate slow-filling regions of the lung where signal intensity is greater in the hybrid multibreath imaging than in single-breathing imaging; orange arrows indicate regions of overinflation; green arrows indicate regions where signal homogeneity is improved in the multibreath imaging scheme. BMI, body mass index; COPD, chronic obstructive pulmonary disease. (Color version of figure is available online.)

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Figure 4, Maps for subject COPD 3: age 60 years, BMI 27.9, FEV 1FVC 40%, DL CO , 59% Pred. Top: spin density maps of single- and multibreath schemes in representative subject COPD3. Middle: P A O 2 maps and histogram distributions of P A O 2 value versus percent incidence in the lungs. Bottom: ADC maps and histogram distribution of ADC value versus percent incidence in the lungs in representative subject COPD 3. ADC, apparent diffusion coefficient; COPD: chronic obstructive pulmonary disease.

Table 2

Imaging Markers

P A O 2 ADC Single-breath Multibreath Single-breath Multibreath Subject Global (Torr) Heterogeneity \* (Torr) <40 or >160 Nonphysiol. † Global (Torr) Heterogeneity \* (Torr) <40 or >160 Nonphysiol. † Global (cm 2 /s) Heterogeneity \* (cm 2 /s) Global (cm 2 /s) Heterogeneity \* (cm 2 /s) AS 1 116 43 61 69 35 52 0.356 0.118 0.391 0.097 AS 2 ‡ - - - - - - 0.315 0.106 0.317 0.124 AS 3 73 42 102 85 36 89 0.320 0.109 0.328 0.108 AS 4 101 35 48 74 34 48 0.263 0.072 0.272 0.070 AS 5 ‡ - - - - - - 0.204 0.099 0.205 0.111 Mean ± SD 97 ± 22 40 ± 4 70 ± 28 76 ± 8 35 ± 1 63 ± 23 0.292 ± 0.059 0.101 ± 0.017 0.303 ± 0.069 0.102 ± 0.02 COPD 1 106 49 117 73 36 103 0.547 0.143 0.598 0.157 COPD 2 105 42 69 86 34 44 0.396 0.109 0.414 0.119 COPD 3 114 39 33 90 33 33 0.328 0.107 0.329 0.124 COPD 4 85 38 50 76 30 32 0.396 0.111 0.402 0.135 COPD 5 145 48 101 106 42 92 0.269 0.072 0.264 0.119 Mean ± SD 111 ± 22 43 ± 5 67 ± 44 86 ± 13 35 ± 4 56 ± 41 0.387 ± 0.104 0.108 ± 0.025 0.401 ± 0.125 0.131 ± 0.016 P (T ≤ t) 0.416 0.367 0.877 0.225 0.384 0.916 0.122 0.609 0.1720.037

ADC, apparent diffusion coefficient; COPD, chronic obstructive pulmonary disease; SD, standard deviation.

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Figure 5, P A O 2 maps (top) and ADC maps (bottom) in four representative subjects (clockwise from top left: AS3: age 67 years, FEV 1FVC 72%; AS4: age 62 years, FEV 1FVC 77; COPD1: age 70 years, FEV 1FVC 35%; COPD5: age: 61 years, FEV 1FVC 49%). In the P A O 2 maps, the color scale differentiates regions of abnormally high P A O 2 values, which may correlate with slow-filling regions, from those with abnormally low P A O 2 values, which may correlate with overinflation and subsequent loss of signal intensity through resolution of pressure gradients. In the ADC maps, the color scale highlights regions of extremely high diffusion, which could be indicative of emphysema.

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Figure 6, Image (spin density) homogeneity, as calculated from the homogeneity marker (refer to methods), plotted against P A O 2 heterogeneity ( a , left). Image homogeneity plotted against the number of nonphysiological values ( a , right). Bar graphs comparing image homogeneity ( b , left), nonphysiological values ( b , middle), and P A O 2 heterogeneity ( b , right) in single-breath versus multibreath imaging. (Color version of figure is available online.)

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Table 3

Imaging Markers

Slow-Filling Voxels ADC_P_ A O 2 Subject VDV Decrease (%) Mean (Torr) SD (Torr) Mean (cm 2 /s) SD (cm 2 /s) AS 1 15.8 0.372 0.267 59.8 22.0 AS 2 5.7 0.323 0.054 - - AS 3 13.6 0.274 0.093 73.6 26.4 AS 4 11.2 0.296 0.083 66.4 19.2 AS 5 3.5 0.206 0.033 - - Mean ± SD 10 ± 6.2 0.314 ± 0.052 0.147 ± 0.103 66.6 ± 6.9 22.5 ± 3.6 COPD 1 24.7 0.610 0.237 88.0 30.8 COPD 2 8.2 0.426 0.163 59.7 29.2 COPD 3 14.5 0.337 0.076 109.6 28.6 COPD 4 10.3 0.419 0.047 86.1 21.5 Mean ± SD 15.7 ± 7 0.455 ± 0.14 0.12 ± 0.103 88.4 ± 18.6 27 ± 4.9p ( Tt ) 0.180 0.144 0.922 0.060 0.144

COPD, chronic obstructive pulmonary disease; SD, standard deviation; VDV, ventilation defect volume.

Figure 7, Spin density maps from representative subjects: COPD 1: age 70 years, BMI 27.2, FEV 1FVC 0.35, DL CO 37% Pred., RV/TLC 62% ( a ); and COPD 5: age 61 years, BMI 23.7, FEV 1FVC 49%, DL CO 51% Pred., RV/TLC 59% ( b ). ADC and P A O 2 color maps are overlaid on the regions identified as slow-filling—regions that were not filled with 3 He gas in the single-breath protocol, but are filled under the multibreath method. Histograms are also included for each slice, including ADC value versus incidence (top) and P A O 2 value versus incidence (bottom). ADC, apparent diffusion coefficient; COPD: chronic obstructive pulmonary disease.

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Figure 8, Top: Histogram distributions of P A O 2 values from single-breath imaging (top) and multibreath imaging (bottom) from all subjects. Bottom: Histogram distributions of ADC values from single-breath imaging (top) and multibreath imaging (bottom). (Color version of figure is available online.)

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Figure 9, ( a ) Spin density maps for the case of a representative subject who exhaled in the middle of the breath-hold. ( b ) Gas delivery monitoring system plots indicating (from top to bottom) the flow of inhaled gas ( black graph ), flow of exhaled gas ( blue graph ), end-tidal O 2 (ETO 2 ; green graph ), and end-tidal CO 2 (ETCO 2 ; red graph ) from different representative subjects to indicate the cases in which the breathing protocol was not followed properly. The red arrows in the top plot indicate intermittent inhalation attempts during the breath-hold, while the orange arrows in the second from top graph show the exhalation during the breath-hold. (Color version of figure is available online.)

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Figure 10, Scatter plots of expired O 2 (ETO 2 ; left) and expired CO 2 (ECO 2 ; right) collected from both COPD and AS cohorts against their global P A O 2 values. COPD, chronic obstructive pulmonary disease. (Color version of figure is available online.)

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Limitations

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Conclusion

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References

  • 1. Galbán C.J., Han M.K., Boes J.L., et. al.: Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med 2012; 18: pp. 1711-1715.

  • 2. Thurlbeck W.M., Simon G.: Radiographic appearance of the chest in emphysema. AJR Am J Roentgenol 1978; 130: pp. 429-440.

  • 3. Kumar S, Liney G, Rai R, et al. Magnetic resonance imaging in lung: a review of its potential for radiotherapy. Br J Radiol 2016; 89. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4846194/ . Accessed July 6, 2018.

  • 4. Mathews J.D., Forsythe A.V., Brady Z., et. al.: Cancer risk in 680 000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians. BMJ 2013; 346: pp. f2360.

  • 5. Inaba Y., Chida K., Kobayashi R., et. al.: A cross-sectional study of the radiation dose and image quality of X-ray equipment used in IVR. J Appl Clin Med Phys 2016; 17: pp. 391-401.

  • 6. Currie GP. ABC of COPD. 3rd ed Wiley-Blackwell, 2017 Available at: https://www.wiley.com/en-us/ABC+of+COPD%2C+3rd+Edition-p-9781119212850 . Accessed February 28, 2018.

  • 7. Punturieri A., Croxton T.L., Weinmann G.G., et. al.: Chronic obstructive pulmonary disease: a view from the NHLBI. Am J Respir Crit Care Med 2008; 178: pp. 441-443.

  • 8. Hamedani H., Kadlecek S.J., Ishii M., et. al.: Alterations of regional alveolar oxygen tension in asymptomatic current smokers: assessment with hyperpolarized (3)He MR imaging. Radiology 2015; 274: pp. 585-596.

  • 9. Hamedani H., Kadlecek S., Xin Y., et. al.: A hybrid multibreath wash-in wash-out lung function quantification scheme in human subjects using hyperpolarized 3He MRI for simultaneous assessment of specific ventilation, alveolar oxygen tension, oxygen uptake, and air trapping. Magn Reson Med 2017; 78: pp. 611-624.

  • 10. Hamedani H., Clapp J.T., Kadlecek S.J., et. al.: Regional fractional ventilation by using multibreath wash-in (3)He MR imaging. Radiology 2016; 279: pp. 917-924.

  • 11. Marshall H., Deppe M.H., Parra-Robles J., et. al.: Direct visualisation of collateral ventilation in COPD with hyperpolarised gas MRI. Thorax 2012; 67: pp. 613-617.

  • 12. Horn F.C., Rao M., Stewart N.J., et. al.: Multiple breath washout of hyperpolarized 129Xe and 3He in human lungs with three-dimensional balanced steady-state free-precession imaging. Magn Reson Med 2017; 77: pp. 2288-2295.

  • 13. Kruger S.J., Nagle S.K., Couch M.J., et. al.: Functional imaging of the lungs with gas agents. J Magn Reson Imaging 2016; 43: pp. 295-315.

  • 14. Saam B, Happer W, Middleton H: Nuclear relaxation of 3He in the presence of O 2 . Phys Rev At Mol Opt Phys 1995; 52: pp. 862-865.

  • 15. Cereda M., Xin Y., Hamedani H., et. al.: Mild loss of lung aeration augments stretch in healthy lung regions. J Appl Physiol 2016; 120: pp. 444-454.

  • 16. Clapp J., Hamedani H., Kadlecek S., et. al.: Multibreath alveolar oxygen tension imaging. Magn Reson Med 2016; 76: pp. 1092-1101.

  • 17. Emami K., Hamedani H., Han B., et. al.: Automatic respiratory gas delivery device for noninvasive administration of hyperpolarized gaseous contrast agents to consciously breathing subjects. Am Thorac Soc 2012; pp. A2045. http://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2012.185.1_MeetingAbstracts.A2045 Accessed July 6, 2018

  • 18. Effect of T1 relaxation on ventilation mapping using hyperpolarized 129 Xe multiple breath wash-out imaging. https://www.readbyqxmd.com/read/30009427/effect-of-t-1-relaxation-on-ventilation-mapping-using-hyperpolarized-129-xe-multiple-breath-wash-out-imaging . Accessed July 20, 2018.

  • 19. Hamedani H., Kadlecek S.J., Emami K., et. al.: A multislice single breath-hold scheme for imaging alveolar oxygen tension in humans. Magn Reson Med 2012; 67: pp. 1332-1345.

  • 20. Yu J., Rajaei S., Ishii M., et. al.: Measurement of pulmonary partial pressure of oxygen and oxygen depletion rate with hyperpolarized helium-3 MRI: a preliminary reproducibility study on pig model. Acad Radiol 2008; 15: pp. 702-712.

  • 21. Fischer M.C., Spector Z.Z., Ishii M., et. al.: Single-acquisition sequence for the measurement of oxygen partial pressure by hyperpolarized gas MRI. Magn Reson Med 2004; 52: pp. 766-773.

  • 22. Fischer M.C., Kadlecek S., Yu J., et. al.: Measurements of regional alveolar oxygen pressure using hyperpolarized 3 He MRI. Acad Radiol 2005; 12: pp. 1430-1439.

  • 23. Reza A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process Syst Signal Image Video Technol 2004; 38: pp. 35-44.

  • 24. Bricault I.: A fast morphology-based registration.1997.SpringerBerlin, Heidelbergpp. 417-426. https://link.springer.com/chapter/10.1007/BFb0029264 Accessed July 6, 2018

  • 25. Kirby M., Pike D., Coxson H.O., et. al.: Hyperpolarized (3)He ventilation defects used to predict pulmonary exacerbations in mild to moderate chronic obstructive pulmonary disease. Radiology 2014; 273: pp. 887-896.

  • 26. Badrul M., Akter K., Akter A.: Detection of brain cancer from MRI images using neural network. Int J Appl Inf Syst 2016; 10: pp. 6-11.

  • 27. Somwanshi D.K., Goswami A.: Detection of abnormalities in MRI images using texture analysis. J Int Acad Phys Sci 2011; 15: http://www.iaps.org.in/journal/index.php/journaliaps/article/view/212 Accessed July 20, 2018

  • 28. Fain S.B., Panth S.R., Evans M.D., et. al.: Early emphysematous changes in asymptomatic smokers: detection with 3He MR imaging. Radiology 2006; 239: pp. 875-883.

  • 29. Sheikh K., Capaldi D.P.I., Hoover D.A., et. al.: Magnetic resonance imaging biomarkers of chronic obstructive pulmonary disease prior to radiation therapy for non-small cell lung cancer. Eur J Radiol Open 2015; 2: pp. 81-89.

  • 30. Hart M.C., Orzalesi M.M., Cook C.D.: Relation between anatomic respiratory dead space and body size and lung volume. J Appl Physiol 1963; 18: pp. 519-522.

  • 31. Hamedani H., Kadlecek S.J., Ishii M., et al. A variability study of regional alveolar oxygen tension measurement in humans using hyperpolarized 3 He MRI. Magn Reson Med. 70:1557–1566.

  • 32. Goto T., Suwa K., Uezono S., et. al.: The blood-gas partition coefficient of xenon may be lower than generally accepted. Br J Anaesth 1998; 80: pp. 255-256.

  • 33. Group NETTR A randomized trial comparing lung-volume–reduction surgery with medical therapy for severe emphysema. http://dx.doi.org/10.1056/NEJMoa030287 ; 2009. https://www.nejm.org/doi/10.1056/NEJMoa030287?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dwww.ncbi.nlm.nih.gov . Accessed July 27, 2018.

  • 34. Thomen R.P., Sheshadri A., Quirk J.D., et. al.: Regional ventilation changes in severe asthma after bronchial thermoplasty with 3 He MR imaging and CT. Radiology 2015; 274: pp. 250-259.

  • 35. Han M.K., Kazerooni E.A., Lynch D.A., et. al.: Chronic obstructive pulmonary disease exacerbations in the COPD gene study: associated radiologic phenotypes. Radiology 2011; 261: pp. 274-282.

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