Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease
Abstract
:1. Introduction
2. Materials and Methods
3. Statistical Analysis
4. Results
4.1. Patient Demographics
4.2. Intra-Observer Reproducibility
4.3. Skeletal Muscle Native Peak T1 Mapping
4.4. Skeletal Muscle ECV
4.5. Native Peak T1 and ECV
4.6. Associations of ECV and Native Peak T1 Mapping with Clinical Measures of PAD
5. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Mozaffarian, D.; Benjamin, E.J.; Go, A.S.; Arnett, D.K.; Blaha, M.J.; Cushman, M.; de Ferranti, S.; Despres, J.P.; Fullerton, H.J.; Howard, V.J.; et al. Heart disease and stroke statistics-2015 update: A report from the american heart association. Circulation 2015, 131, e29-322. [Google Scholar] [CrossRef]
- Sigvant, B.; Lundin, F.; Wahlberg, E. The Risk of Disease Progression in Peripheral Arterial Disease is Higher than Expected: A Meta-Analysis of Mortality and Disease Progression in Peripheral Arterial Disease. Eur. J. Vasc. Endovasc. Surg. 2016, 51, 395–403. [Google Scholar] [CrossRef]
- Mueller, T.; Hinterreiter, F.; Luft, C.; Poelz, W.; Haltmayer, M.; Dieplinger, B. Mortality rates and mortality predictors in patients with symptomatic peripheral artery disease stratified according to age and diabetes. J. Vasc. Surg. 2014, 59, 1291–1299. [Google Scholar] [CrossRef]
- Olin, J.; Sealove, B. Peripheral artery disease: Current insight into the disease and its diagnosis and management. Mayo Clin. Proc. 2010, 85, 678–692. [Google Scholar] [CrossRef]
- Ross, E.G.; Shah, N.H.; Dalman, R.L.; Nead, K.T.; Cooke, J.P.; Leeper, N.J. The use of machine learning for the identification of peripheral artery disease and future mortality risk. J. Vasc. Surg. 2016, 64, 1515–1522. [Google Scholar] [CrossRef]
- Sanchez-Martinez, S.; Duchateau, N.; Erdei, T.; Kunszt, G.; Aakhus, S.; Degiovanni, A.; Marino, P.; Carluccio, E.; Piella, G.; Fraser, A.G. Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction. Circ. Cardiovasc. Imaging 2018, 11, e007138. [Google Scholar] [CrossRef]
- Martelli, E.; Enea, I.; Zamboni, M.; Federici, M.; Bracale, U.M.; Sangiorgi, G.; Martelli, A.R.; Messina, T.; Settembrini, A.M. Focus on the Most Common Paucisymptomatic Vasculopathic Population, from Diagnosis to Secondary Prevention of Complications. Diagnostics 2023, 13, 2356. [Google Scholar] [CrossRef]
- Brunner, G.; Bismuth, J.; Nambi, V.; Ballantyne, C.M.; Taylor, A.; Lumsden, A.B.; Morrisett, J.D.; Shah, D.J. Calf Muscle Perfusion As Measured With Magnetic Resonance Imaging To Assess Peripheral Arterial Disease. Med. Biol. Eng. Comput. 2016, 54, 1667–1681. [Google Scholar] [CrossRef]
- Isbell, D.C.; Epstein, F.H.; Zhong, X.; DiMaria, J.M.; Berr, S.S.; Meyer, C.H.; Rogers, W.J.; Harthun, N.L.; Hagspiel, K.D.; Weltman, A.; et al. Calf muscle perfusion at peak exercise in peripheral arterial disease: Measurement by first-pass contrast-enhanced magnetic resonance imaging. J. Magn. Reson. Imaging JMRI 2007, 25, 1013–1020. [Google Scholar] [CrossRef]
- Gimnich, O.A.; Singh, J.; Bismuth, J.; Shah, D.J.; Brunner, G. Magnetic resonance imaging based modeling of microvascular perfusion in patients with peripheral artery disease. J. Biomech. 2019, 93, 147–158. [Google Scholar] [CrossRef]
- Baessler, B.; Luecke, C.; Lurz, J.; Klingel, K.; Das, A.; von Roeder, M.; de Waha-Thiele, S.; Besler, C.; Rommel, K.-P.; Maintz, D. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology 2019, 292, 608–617. [Google Scholar] [CrossRef]
- Jellis, C.L.; Kwon, D.H. Myocardial T1 mapping: Modalities and clinical applications. Cardiovasc. Diagn. Ther. 2014, 4, 126. [Google Scholar]
- Than, M.P.; Pickering, J.W.; Sandoval, Y.; Shah, A.S.V.; Tsanas, A.; Apple, F.S.; Blankenberg, S.; Cullen, L.; Mueller, C.; Neumann, J.T. Machine learning to predict the likelihood of acute myocardial infarction. Circulation 2019, 140, 899–909. [Google Scholar] [CrossRef]
- Xiong, G.; Kola, D.; Heo, R.; Elmore, K.; Cho, I.; Min, J.K. Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med. Image Anal. 2015, 24, 77–89. [Google Scholar] [CrossRef]
- Yang, E.Y.; Ghosn, M.G.; Khan, M.A.; Gramze, N.L.; Brunner, G.; Nabi, F.; Nambi, V.; Nagueh, S.F.; Nguyen, D.T.; Graviss, E.A. Myocardial extracellular volume fraction adds prognostic information beyond myocardial replacement fibrosis. Circ. Cardiovasc. Imaging 2019, 12, e009535. [Google Scholar] [CrossRef]
- Shieh, M.C.; Belousova, T.; Taylor, A.A.; Nambi, V.; Morrisett, J.D.; Ballantyne, C.M.; Bismuth, J.; Shah, D.J.; Brunner, G. Contrast-Enhanced Magnetic Resonance Imaging Based T1-Mapping and Extracellular Volume Fractions are Associated with Peripheral Artery Disease. Circulation 2017, 136, A17267. [Google Scholar]
- Gimnich, O.A.; Holbrook, J.; Belousova, T.; Short, C.M.; Taylor, A.A.; Nambi, V.; Morrisett, J.D.; Ballantyne, C.M.; Bismuth, J.; Shah, D.J.; et al. Relation of Magnetic Resonance Imaging Based Arterial Signal Enhancement to Markers of Peripheral Artery Disease. Am. J. Cardiol. 2021, 140, 140–147. [Google Scholar] [CrossRef]
- Messroghli, D.R.; Greiser, A.; Frohlich, M.; Dietz, R.; Schulz-Menger, J. Optimization and validation of a fully-integrated pulse sequence for modified look-locker inversion-recovery (MOLLI) T1 mapping of the heart. J. Magn. Reson. Imaging JMRI 2007, 26, 1081–1086. [Google Scholar] [CrossRef]
- Messroghli, D.R.; Plein, S.; Higgins, D.M.; Walters, K.; Jones, T.R.; Ridgway, J.P.; Sivananthan, M.U. Human myocardium: Single-breath-hold MR T1 mapping with high spatial resolution—Reproducibility study. Radiology 2006, 238, 1004–1012. [Google Scholar] [CrossRef]
- Messroghli, D.R.; Rudolph, A.; Abdel-Aty, H.; Wassmuth, R.; Kuhne, T.; Dietz, R.; Schulz-Menger, J. An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging. BMC Med. Imaging 2010, 10, 16. [Google Scholar] [CrossRef]
- Carod-Artal, F.J.; Ferreira Coral, L.; Stieven Trizotto, D.; Menezes Moreira, C. Self- and proxy-report agreement on the Stroke Impact Scale. Stroke 2009, 40, 3308–3314. [Google Scholar] [CrossRef]
- Etherington, J.; Innes, G.; Christenson, J.; Berkowitz, J.; Chamberlain, R.; Berringer, R.; Leung, C. Development, implementation and reliability assessment of an emergency physician performance evaluation tool. CJEM 2000, 2, 237–245. [Google Scholar] [CrossRef]
- Nunnally, J.C.; Bernstein, I.H. Psychometric Theory; McGraw-Hill Inc.: New York, NY, USA, 1994. [Google Scholar]
- Shrout, P.E.; Fleiss, J.L. Intraclass correlations: Uses in assessing rater reliability. Psychol. Bull. 1979, 86, 420–428. [Google Scholar] [CrossRef]
- Libby, P. Inflammation in atherosclerosis. Nature 2002, 420, 868–874. [Google Scholar] [CrossRef]
- Lucia, M.; Roman, P.; Martin, P.; Luz, M.M.; Tomáš, H.; Vladimír, K.; Lenka, J.; Jan, M.; Lukáš, O.; Věra, F. Myocardial native T(1) mapping and extracellular volume quantification in asymptomatic female carriers of Duchenne muscular dystrophy gene mutations. Orphanet J. Rare Dis. 2023, 18, 283. [Google Scholar]
- Chen, H.; Erley, J.; Muellerleile, K.; Saering, D.; Jahnke, C.; Cavus, E.; Schneider, J.N.; Blankenberg, S.; Lund, G.K.; Adam, G.; et al. Contrast-enhanced cardiac MRI is superior to non-contrast mapping to predict left ventricular remodeling at 6 months after acute myocardial infarction. Eur. Radiol. 2023, 34, 1863–1874. [Google Scholar] [CrossRef]
- Wu, W.C.; Wang, J.; Detre, J.A.; Wehrli, F.W.; Mohler, E., 3rd; Ratcliffe, S.J.; Floyd, T.F. Hyperemic flow heterogeneity within the calf, foot, and forearm measured with continuous arterial spin labeling MRI. Am. J. Physiol. Heart Circ. Physiol. 2008, 294, H2129–H2136. [Google Scholar] [CrossRef]
- Puntmann, V.O.; Peker, E.; Chandrashekhar, Y.; Nagel, E. T1 Mapping in Characterizing Myocardial Disease: A Comprehensive Review. Circ. Res. 2016, 119, 277–299. [Google Scholar] [CrossRef]
- Lin, Y.C.; Fu, T.C.; Lin, G.; Ng, S.H.; Yeh, C.H.; Ng, S.C.; Chang, T.C.; Juan, Y.H. Using T1 mapping indices to evaluate muscle function and predict conservative treatment outcomes in diabetic patients with peripheral arterial disease. Eur. Radiol. 2023, 33, 4927–4937. [Google Scholar] [CrossRef] [PubMed]
- Banerjee, R.; Pavlides, M.; Tunnicliffe, E.M.; Piechnik, S.K.; Sarania, N.; Philips, R.; Collier, J.D.; Booth, J.C.; Schneider, J.E.; Wang, L.M.; et al. Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J. Hepatol. 2014, 60, 69–77. [Google Scholar] [CrossRef] [PubMed]
- Haimerl, M.; Verloh, N.; Fellner, C.; Zeman, F.; Teufel, A.; Fichtner-Feigl, S.; Schreyer, A.G.; Stroszczynski, C.; Wiggermann, P. MRI-based estimation of liver function: Gd-EOB-DTPA-enhanced T1 relaxometry of 3T vs. the MELD score. Sci. Rep. 2014, 4, 5621. [Google Scholar] [CrossRef] [PubMed]
- Kim, K.A.; Park, M.S.; Kim, I.S.; Kiefer, B.; Chung, W.S.; Kim, M.J.; Kim, K.W. Quantitative evaluation of liver cirrhosis using T1 relaxation time with 3 tesla MRI before and after oxygen inhalation. J. Magn. Reson. Imaging JMRI 2012, 36, 405–410. [Google Scholar] [CrossRef] [PubMed]
- Yoon, J.H.; Lee, J.M.; Paek, M.; Han, J.K.; Choi, B.I. Quantitative assessment of hepatic function: Modified look-locker inversion recovery (MOLLI) sequence for T1 mapping on Gd-EOB-DTPA-enhanced liver MR imaging. Eur. Radiol. 2016, 26, 1775–1782. [Google Scholar] [CrossRef] [PubMed]
- Pavlides, M.; Banerjee, R.; Sellwood, J.; Kelly, C.J.; Robson, M.D.; Booth, J.C.; Collier, J.; Neubauer, S.; Barnes, E. Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease. J. Hepatol. 2016, 64, 308–315. [Google Scholar] [CrossRef]
- Lin, Y.C.; Chuang, W.Y.; Wei, F.C.; Yeh, C.H.; Tinhofer, I.; Al Deek, N.F.; Fu, T.C.; Ng, S.C.; Chang, T.C.; Cheung, Y.C.; et al. Peripheral arterial disease: The role of extracellular volume measurements in lower limb muscles with MRI. Eur. Radiol. 2020, 30, 3943–3950. [Google Scholar] [CrossRef]
Variables | PAD Patients (n = 18) | Controls (n = 19) | p-Value |
---|---|---|---|
Age (years) | 67.6 ± 9.10 | 65.4 ± 7.19 | 0.43 |
Males, n (%) | 11 (61.1) | 11 (57.9) | 0.84 |
Black race, n (%) | 4 (22.2) | 4 (21.1) | 0.86 |
Body mass index (kg/m2) | 27.1 ± 3.85 | 28.2 ± 5.20 | 0.48 |
Resting ABI | 0.734 ± 0.22 | 1.16 ± 0.087 | <0.0001 |
Delta ABI | 0.179 ± 0.18 | 0.110 ± 0.097 | 0.16 |
History of smoking, n (%) | 16 (88.9) | 8 (42.1) | 0.006 |
Diabetes, n (%) | 7 (38.9) | 3 (15.8) | 0.11 |
Hypertension, n (%) | 17 (94.4) | 11 (57.9) | 0.010 |
Hyperlipidemia, n (%) | 17 (94.4) | 11 (57.9) | 0.010 |
Heart rate (bpm) | 68.6 ± 15.9 | 63.7 ± 6.89 | 0.24 |
Hematocrit (%) | 38.5 ± 5.73 | 41.2 ± 3.68 | 0.14 |
eGFR (mL/min/1.73 m2) | 71.8 ± 21.4 | 78.4 ± 18.1 | 0.36 |
Anticoagulation, n (%) | 8 (44.4) | 3 (15.8) | 0.06 |
ACE inhibitor, n (%) | 8 44.4) | 5 (26.3) | 0.25 |
Beta blocker, n (%) | 9 (50.0) | 7 (36.8) | 0.42 |
Claudication onset time (s) | 112.6 ± 102.8 | N/A | <0.0001 |
Peak walking time (s) | 298.2 ± 88.0 | 355.3 ± 20.6 | 0.009 |
Completed 6 min treadmill walking, n (%) | 10 (58.8) | 18 (94.7) | 0.010 |
Cholesterol-lowering drug use, n (%) | 15 (83.3) | 8 (42.1) | 0.010 |
Coronary artery disease, n (%) | 8 (44.4) | 4 (21.1) | 0.13 |
Lower extremity revascularization history, n (%) | 12 (66.7) | 0 (0.0) | <0.0001 |
Family history of coronary heart disease, n (%) | 11 (61.1) | 8 (42.1) | 0.25 |
Intra-Observer ICC for the Anterior Muscle Group (Right Side) | Intra-Observer ICC for the Lateral Muscle Group (Right Side) | Intra-Observer ICC for the Deep Posterior Muscle Group (Right Side) | Intra-Observer ICC for the Soleus Muscle (Right Side) | Intra-Observer ICC for the Gastrocnemius Muscle (Right Side) | |
Average ICC (95% CI) | 0.946 (0.885–0.975) | 0.883 (0.746–0.946) | 0.728 (0.430–0.871) | 0.903 (0.794–0.954) | 0.919 (0.828–0.962) |
Intra-observer ICC for the anterior muscle group (left side) | Intra-observer ICC for the lateral muscle group (left side) | Intra-observer ICC for the deep posterior muscle group (left side) | Intra-observer ICC for the soleus muscle (left side) | Intra-observer ICC for the gastrocnemius muscle (left side) | |
Average ICC (95% CI) | 0.955 (0.904–0.979) | 0.933 (0.857–0.969) | 0.828 (0.633–0.920) | 0.961 (0.914–0.982) | 0.892 (0.772–0.949) |
Intra-observer ICC for the anterior muscle group (bilateral average) | Intra-observer ICC for the lateral muscle group (bilateral average) | Intra-observer ICC for the deep posterior muscle group (bilateral average) | Intra-observer ICC for the soleus muscle (bilateral average) | Intra-observer ICC for the gastrocnemius muscle (bilateral average) | |
Average ICC (95% CI) | 0.954 (0.902–0.978) | 0.942 (0.877–0.973) | 0.786 (0.548–0.899) | 0.948 (0.889–0.975) | 0.914 (0.819–0.960) |
Intra-observer ICC for the cross-sectional leg area (bilateral average) | Intra-observer ICC for the anterior tibialis artery (right side) | Intra-observer ICC for the anterior tibialis artery (left side) | Intra-observer ICC for the posterior tibialis artery (right side) | Intra-observer ICC for the posterior tibialis artery (left side) | |
Average ICC (95% CI) | 0.961 (0.917–0.982) | 0.992 (0.992–0.992) | 0.988 (0.987–0.989) | 0.959 (0.957–0.960) | 0.986 (0.985–0.987) |
Intra-observer ICC for the peroneal artery (right side) | Intra-observer ICC for the peroneal artery (left side) | ||||
Average ICC (95% CI) | 0.934 (0.924–0.942) | 0.955 (0.953–0.957) |
Variables | PAD Patients (n = 18) | Controls (n = 19) | p-Value |
---|---|---|---|
Native peak T1 of composite arteries (ms) | 1729 (1679–1810) | 1688 (1637–1756) | 0.17 |
Cross-sectional area, anterior muscle group (mm2) | 795 (678–980) | 971 (802–1309) | 0.045 |
Cross-sectional area, lateral muscle group (mm2) | 454 (385–554) | 597 (483–803) | 0.007 |
Cross-sectional area, deep posterior muscle group (mm2) | 677 (531–803) | 624 (491–947) | 1.00 |
Cross-sectional area, soleus muscle (mm2) | 1459 (1235–1882) | 1759 (1333–2322) | 0.18 |
Cross-sectional area, gastrocnemius muscle (mm2) | 1326 (1099–1800) | 1649 (1204–2146) | 0.11 |
Average cross-sectional area (mm2) | 954 (828–1175) | 1175 (954–1546) | 0.055 |
Native peak T1, anterior muscle group (ms) | 1835.5 (188) | 1746 (163) | 0.027 |
Native peak T1, lateral muscle group (ms) | 1907 (65) | 1755 (279) | 0.002 |
Native peak T1, deep posterior muscle group (ms) | 1917.5 (106) | 1782 (296) | 0.012 |
Native peak T1, soleus muscle (ms) | 1930 (143) | 1899 (164) | 0.29 |
Native peak T1, gastrocnemius muscle (ms) | 1945.5 (50) | 1934 (160) | 0.74 |
Minimum T1, anterior muscle group (ms) | 878 (777–963) | 878 (777–963) | 0.53 |
Minimum T1, lateral muscle group (ms) | 897 (818–955) | 840 (718–926) | 0.45 |
Minimum T1, deep posterior muscle group (ms) | 729 (637–802) | 712 (595–889) | 0.87 |
Minimum T1, soleus muscle (ms) | 728 (643–813) | 823 (797–912) | 0.024 |
Minimum T1, gastrocnemius muscle (ms) | 730 (524–900) | 651 (0–809) | 0.55 |
Average cross-sectional native peak T1 (ms) | 1902 (1877–1924) | 1823 (1709–1883) | 0.005 |
Average cross-sectional mean T1 (ms) | 1218 (1143–1263) | 1190 (1140–1258) | 0.45 |
ECV, anterior muscle group (%) | 26.4 (21.2–31.5) | 17.3 (10.2–25.1) | 0.046 |
ECV, lateral muscle group (%) | 21.7 (15.1–31.2) | 24.7 (19.6–38.5) | 0.43 |
ECV, deep posterior muscle group (%) | 29.0 (22.5–36.1) | 24.1 (16.5–31.0) | 0.19 |
ECV, soleus muscle (%) | 22.7 (19.5–27.8) | 13.8 (10.2–19.1) | 0.020 |
ECV, gastrocnemius muscle (%) | 21.8 (15.1–26.5) | 16.8 (13.3–23.6) | 0.38 |
ECV, averaged over 5 muscle compartments (%) | 22.9 (21.0–27.5) | 24.5 (20.7–28.0) | 0.68 |
Independent Variables | n | β | Standard Error | R2 | Adjusted r2 | p-Value | |
---|---|---|---|---|---|---|---|
ECV, AM (%) | Resting ABI | 28 | 0.243 | 9.086 | 0.06 | 0.02 | 0.21 |
Δ ABI | 27 | −0.213 | −23.030 | 0.05 | 0.05 | 0.28 | |
Claudication onset time (s) | 27 | 0.150 | 0.018 | 0.02 | −0.02 | 0.46 | |
Peak walking time (s) | 27 | −0.201 | −0.035 | 0.04 | 0.002 | 0.31 | |
Body mass index (kg/m2) | 27 | −0.216 | −0.501 | 0.05 | 0.01 | 0.27 | |
eGFR (mL/min/1.73 m2) | 27 | 0.079 | 0.041 | 0.006 | −0.04 | 0.72 | |
ECV, LM (%) | Resting ABI | 28 | 0.244 | 9.086 | 0.06 | 0.02 | 0.21 |
Δ ABI | 27 | −0.213 | −23.034 | 0.05 | 0.01 | 0.28 | |
Claudication onset time (s) | 27 | −0.081 | −0.009 | 0.01 | −0.03 | 0.69 | |
Peak walking time (s) | 27 | 0.146 | 0.023 | 0.02 | −0.02 | 0.47 | |
Body mass index (kg/m2) | 28 | −0.052 | −0.116 | 0.003 | −0.04 | 0.79 | |
eGFR (mL/min/1.73 m2) | 23 | −0.122 | −0.064 | 0.02 | −0.03 | 0.58 | |
ECV, DM (%) | Resting ABI | 28 | 0.389 | 3.197 | 0.15 | 0.12 | 0.041 |
Δ ABI | 27 | 0.080 | 4.055 | 0.01 | −0.03 | 0.69 | |
Claudication onset time (s) | 27 | 0.135 | 0.013 | 0.02 | −0.02 | 0.50 | |
Peak walking time (s) | 27 | −0.192 | −0.027 | 0.04 | −0.002 | 0.34 | |
Body mass index (kg/m2) | 28 | −0.580 | −1.120 | 0.34 | 0.31 | 0.001 | |
eGFR (mL/min/1.73 m2) | 23 | 0.373 | 0.176 | 0.14 | 0.10 | 0.08 | |
ECV, SM (%) | Resting ABI | 28 | 0.403 | 1.580 | 0.16 | 0.13 | 0.034 |
Δ ABI | 27 | −0.093 | −9.920 | 0.01 | −0.03 | 0.64 | |
Claudication onset time (s) | 27 | 0.226 | 0.024 | 0.05 | 0.01 | 0.26 | |
Peak walking time (s) | 27 | 0.029 | 0.004 | 0.001 | −0.04 | 0.89 | |
Body mass index (kg/m2) | 28 | −0.247 | 0.394 | 0.06 | 0.02 | 0.21 | |
eGFR (mL/min/1.73 m2) | 23 | 0.244 | 0.128 | 0.06 | 0.01 | 0.26 | |
ECV, GM (%) | Resting ABI | 28 | 0.104 | 0.669 | 0.01 | −0.03 | 0.60 |
Δ ABI | 27 | 0.140 | 5.410 | 0.02 | −0.02 | 0.49 | |
Claudication onset time (s) | 27 | 0.112 | 0.015 | 0.01 | −0.03 | 0.58 | |
Peak walking time (s) | 27 | −0.022 | −0.002 | 0.001 | −0.04 | 0.91 | |
Body mass index (kg/m2) | 28 | −0.247 | −0.511 | 0.06 | 0.02 | 0.21 | |
eGFR (mL/min/1.73 m2) | 23 | −0.020 | 0.007 | 0.00 | −0.05 | 0.93 |
Independent Variables | n | β | Standard Error | R2 | Adjusted r2 | p-Value | |
---|---|---|---|---|---|---|---|
Native peak T1 of AM (ms) | Resting ABI | 37 | −0.276 | −73.6 | 0.08 | 0.05 | 0.10 |
Δ ABI | 36 | 0.201 | 237.4 | 0.04 | 0.01 | 0.24 | |
Claudication onset time (s) | 36 | 0.083 | 0.159 | 0.01 | −0.02 | 0.63 | |
Peak walking time (s) | 36 | −0.354 | −0.893 | 0.13 | 0.10 | 0.03 | |
Body mass index (kg/m2) | 37 | 0.153 | 5.79 | 0.02 | −0.01 | 0.37 | |
eGFR (mL/min/1.73 m2) | 31 | −0.152 | −1.33 | 0.02 | −0.01 | 0.42 | |
Native peak T1 of LM (ms) | Resting ABI | 37 | −0.359 | −120.2 | 0.13 | 0.10 | 0.029 |
Δ ABI | 36 | 0.277 | 258.7 | 0.08 | 0.05 | 0.10 | |
Claudication onset time (s) | 36 | 0.344 | 0.517 | 0.12 | 0.09 | 0.040 | |
Peak walking time (s) | 36 | −0.141 | −0.281 | 0.02 | −0.01 | 0.41 | |
Body mass index (kg/m2) | 37 | 0.166 | 4.87 | 0.03 | 0.00 | 0.33 | |
eGFR (mL/min/1.73 m2) | 31 | 0.049 | 0.347 | 0.002 | −0.03 | 0.79 | |
Native peak T1 of DM (ms) | Resting ABI | 37 | −0.102 | −26.2 | 0.01 | −0.02 | 0.55 |
Δ ABI | 36 | 0.216 | 240.8 | 0.05 | 0.02 | 0.21 | |
Claudication onset time (s) | 36 | −0.026 | −0.046 | 0.00 | −0.03 | 0.88 | |
Peak walking time (s) | 36 | −0.199 | −0.472 | 0.04 | 0.01 | 0.25 | |
Body mass index (kg/m2) | 37 | 0.110 | 3.85 | 0.01 | −0.02 | 0.52 | |
eGFR (mL/min/1.73 m2) | 31 | 0.011 | 0.093 | 0.00 | −0.03 | 0.95 | |
Native peak T1 of SM (ms) | Resting ABI | 37 | −0.141 | −59.9 | 0.02 | −0.01 | 0.41 |
Δ ABI | 36 | 0.192 | 153.0 | 0.04 | 0.01 | 0.26 | |
Claudication onset time (s) | 36 | 0.122 | 0.156 | 0.02 | −0.01 | 0.48 | |
Peak walking time (s) | 36 | −0.233 | −0.395 | 0.05 | 0.03 | 0.17 | |
Body mass index (kg/m2) | 37 | −0.071 | −1.78 | 0.01 | −0.02 | 0.68 | |
eGFR (mL/min/1.73 m2) | 31 | −0.144 | 0.902 | 0.02 | −0.01 | 0.44 | |
Native peak T1 of GM (ms) | Resting ABI | 37 | −0.199 | −75.8 | 0.04 | 0.01 | 0.24 |
Δ ABI | 36 | 0.185 | 131.7 | 0.03 | 0.01 | 0.28 | |
Claudication onset time (s) | 36 | 0.024 | 0.027 | 0.001 | −0.03 | 0.89 | |
Peak walking time (s) | 36 | −0.091 | −0.139 | 0.01 | −0.02 | 0.60 | |
Body mass index (kg/m2) | 37 | 0.353 | 7.94 | 0.12 | 0.10 | 0.032 | |
eGFR (mL/min/1.73 m2) | 31 | 0.131 | 0.672 | 0.02 | −0.02 | 0.48 |
Independent Variables | n | β | Standard Error | R2 | Adjusted r2 | p-Value | |
---|---|---|---|---|---|---|---|
Native peak T1 averaged over all calf muscle compartments (ms) | Resting ABI | 37 | −0.379 | −145.1 | 0.14 | 0.12 | 0.021 |
Δ ABI | 36 | 0.289 | 204.3 | 0.08 | 0.06 | 0.09 | |
Claudication onset time (s) | 36 | 0.143 | 0.163 | 0.02 | −0.01 | 0.40 | |
Peak walking time (s) | 36 | −0.289 | −0.436 | 0.08 | 0.06 | 0.09 | |
Body mass index (kg/m2) | 37 | 0.183 | 4.14 | 0.03 | 0.01 | 0.28 | |
eGFR (mL/min/1.73 m2) |
Independent Variables | n | β | Standard Error | R2 | Adjusted r2 | p-Value | |
---|---|---|---|---|---|---|---|
Mean ECV (averaged over all calf muscle compartments) (%) | Resting ABI | 28 | 0.016 | 0.324 | <0.001 | −0.04 | 0.93 |
Δ ABI | 27 | 0.019 | 1.06 | <0.001 | −0.04 | 0.93 | |
Claudication onset time (s) | 27 | 0.117 | 0.007 | 0.01 | −0.03 | 0.56 | |
Peak walking time (s) | 27 | 0.014 | 0.001 | <0.001 | −0.04 | 0.95 | |
Body mass index (kg/m2) | 28 | −0.091 | −0.103 | 0.008 | −0.03 | 0.65 | |
eGFR (mL/min/1.73 m2) | 23 | −0.058 | −0.016 | 0.003 | −0.04 | 0.79 |
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Fitian, A.I.; Shieh, M.C.; Gimnich, O.A.; Belousova, T.; Taylor, A.A.; Ballantyne, C.M.; Bismuth, J.; Shah, D.J.; Brunner, G. Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease. J. Cardiovasc. Dev. Dis. 2024, 11, 181. https://doi.org/10.3390/jcdd11060181
Fitian AI, Shieh MC, Gimnich OA, Belousova T, Taylor AA, Ballantyne CM, Bismuth J, Shah DJ, Brunner G. Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease. Journal of Cardiovascular Development and Disease. 2024; 11(6):181. https://doi.org/10.3390/jcdd11060181
Chicago/Turabian StyleFitian, Asem I., Michael C. Shieh, Olga A. Gimnich, Tatiana Belousova, Addison A. Taylor, Christie M. Ballantyne, Jean Bismuth, Dipan J. Shah, and Gerd Brunner. 2024. "Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease" Journal of Cardiovascular Development and Disease 11, no. 6: 181. https://doi.org/10.3390/jcdd11060181