Abstract

Thermal infrared imaging has been suggested as a non-invasive alternative to monitor physiological processes and disease. However, the use of this technique to image internal organs, such as the heart, has not yet been investigated. We sought to determine the ability of our novel thermal image-processing algorithm to detect structural and functional changes in a mouse model of hypertension and cardiac remodeling. Twelve mice were randomly assigned to receive either the pro-inflammatory, hypertensive hormone angiotensin-II (2 mg/kg/day, n = 6) or saline (n = 6) infusion for 28 days. We performed weekly blood pressure measurements, together with serial trans-thoracic echocardiography studies and histopathological evaluation of the hearts. Thermal images were captured with a commercially available thermal camera, and images were processed by our novel algorithm which analyzes relative spatial temperature variation across the animal’s thorax. We assessed cardiac inflammation by measuring inflammatory cell infiltration through flow cytometry. Angiotensin infusion increased blood pressure together with cardiac hypertrophy and fibrosis. Thermal imaging at day 28 of the experiment detected an increase in the fraction of the skin heated by the heart in angiotensin-treated mice. Thermal image findings were significantly correlated to left ventricular volume and mass parameters seen on echocardiography (r = 0.8, p < 0.01 and r = 0.6, p = 0.07). We also identified distinct changes in the spatial heat profiles of all angiotensin-treated hearts, possibly reflecting remodeling processes in the hypertensive heart. Finally, a machine learning based model using thermal imaging parameters predicted intervention status in 10 out of 11 mice similar to a model using echocardiographic measurements. Our findings suggest, for the first time, that a new thermal image-processing algorithm successfully correlates surface thermography with cardiac structural changes in mice with hypertensive heart disease.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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    [Crossref]
  2. I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.
  3. O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
    [Crossref]
  4. S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
    [Crossref]
  5. T. J. Love, “Thermography as an Indicator of Blood Perfusion,” Ann. N. Y. Acad. Sci. 335(1 Thermal Chara), 429–437 (1980).
    [Crossref]
  6. Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
    [Crossref]
  7. M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
    [Crossref]
  8. Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
    [Crossref]
  9. Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
    [Crossref]
  10. J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
    [Crossref]
  11. Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
    [Crossref]
  12. Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
    [Crossref]
  13. P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
    [Crossref]
  14. C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
    [Crossref]
  15. Y. Cho, S. J. Julier, N. Marquardt, and N. Bianchi-Berthouze, “Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging,” Biomed. Opt. Express 8(10), 4480 (2017).
    [Crossref]
  16. P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph, Image Process. 41(2), 233–260 (1988).
    [Crossref]
  17. N. Otsu, “A threshold selection method from gray level histogram,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
    [Crossref]
  18. N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
    [Crossref]
  19. C. Cortes and V. Vapnik, “Support-Vector Networks,” Int. J. Mach. Learn. Cybern. 20(3), 273–297 (1995).
    [Crossref]
  20. T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36(3), 1171–1220 (2008).
    [Crossref]
  21. S. Geisser, Predictive Inference. (Chapman and Hall, 1993).
  22. M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
    [Crossref]
  23. H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
    [Crossref]
  24. F. H. Messerli, S. F. Rimoldi, and S. Bangalore, “The Transition From Hypertension to Heart Failure,” JACC Hear. Fail. 5(8), 543–551 (2017).
    [Crossref]
  25. B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
    [Crossref]

2018 (3)

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
[Crossref]

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

2017 (5)

Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
[Crossref]

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Y. Cho, S. J. Julier, N. Marquardt, and N. Bianchi-Berthouze, “Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging,” Biomed. Opt. Express 8(10), 4480 (2017).
[Crossref]

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

F. H. Messerli, S. F. Rimoldi, and S. Bangalore, “The Transition From Hypertension to Heart Failure,” JACC Hear. Fail. 5(8), 543–551 (2017).
[Crossref]

2015 (3)

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
[Crossref]

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

2013 (1)

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

2012 (2)

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

E. F. J. Ring and K. Ammer, “Infrared thermal imaging in medicine,” Physiol. Meas. 33(3), R33–R46 (2012).
[Crossref]

2009 (1)

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

2008 (1)

T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36(3), 1171–1220 (2008).
[Crossref]

2002 (1)

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

1995 (2)

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

C. Cortes and V. Vapnik, “Support-Vector Networks,” Int. J. Mach. Learn. Cybern. 20(3), 273–297 (1995).
[Crossref]

1990 (1)

J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
[Crossref]

1988 (1)

P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph, Image Process. 41(2), 233–260 (1988).
[Crossref]

1980 (1)

T. J. Love, “Thermography as an Indicator of Blood Perfusion,” Ann. N. Y. Acad. Sci. 335(1 Thermal Chara), 429–437 (1980).
[Crossref]

1979 (1)

N. Otsu, “A threshold selection method from gray level histogram,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

Aharonson, V.

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
[Crossref]

Alezra, D.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

Amit, U.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Ammer, K.

E. F. J. Ring and K. Ammer, “Infrared thermal imaging in medicine,” Physiol. Meas. 33(3), R33–R46 (2012).
[Crossref]

Avrahami, I.

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Bagavathiappan, S.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Bangalore, S.

F. H. Messerli, S. F. Rimoldi, and S. Bangalore, “The Transition From Hypertension to Heart Failure,” JACC Hear. Fail. 5(8), 543–551 (2017).
[Crossref]

Battler, A.

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Ben-David, M.

I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.

Ben-David, M. A.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

Bianchi-Berthouze, N.

Bracht, C.

J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
[Crossref]

Casscells, W.

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Castel, D.

Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
[Crossref]

Chayen, D.

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Cho, Y.

Chouraqui, P.

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Cortes, C.

C. Cortes and V. Vapnik, “Support-Vector Networks,” Int. J. Mach. Learn. Cybern. 20(3), 273–297 (1995).
[Crossref]

Dai, H.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Dash, R.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Daugherty, A.

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

Duncker, D. J.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

Eldar, M.

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
[Crossref]

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Ferng, A.

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Fornwalt, B. K.

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

Gannot, I.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.

Gavish, B.

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
[Crossref]

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Gefen, A.

M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
[Crossref]

Geisser, S.

S. Geisser, Predictive Inference. (Chapman and Hall, 1993).

Gong, M. C.

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

Gritzman, A.

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

Haggerty, C. M.

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

Hochhauser, E.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Hoffer, O. A.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

Hofmann, T.

T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36(3), 1171–1220 (2008).
[Crossref]

Hopper, O.

I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.

Jagadeesan, K.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Jayakumar, T.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Julier, S. J.

Kain, D.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Karunanithi, R.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Katz, E.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.

Keenan, J. B.

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Kelson, I.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.

Khalpey, Z.

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Kim, S.

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Kitagawa, T.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Kopplin, K.

M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
[Crossref]

Korath, M. P.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Kornowski, R.

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Kosuge, H.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Landa, N.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Leor, J.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Levin-Kotler, L.-P.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Levy, A.

M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
[Crossref]

Levy-Aharoni, D.

Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
[Crossref]

Leyhe, A.

J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
[Crossref]

Litovsky, S.

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Love, T. J.

T. J. Love, “Thermography as an Indicator of Blood Perfusion,” Ann. N. Y. Acad. Sci. 335(1 Thermal Chara), 429–437 (1980).
[Crossref]

Lustig, M.

M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
[Crossref]

Madjid, M.

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Malik, B. A.

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Marquardt, N.

Mattingly, A. C.

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

McConnell, M. V.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Meilin, A.

Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
[Crossref]

Messerli, F. H.

F. H. Messerli, S. F. Rimoldi, and S. Bangalore, “The Transition From Hypertension to Heart Failure,” JACC Hear. Fail. 5(8), 543–551 (2017).
[Crossref]

Naftali-Shani, N.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Naghavi, M.

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Nicolay, K.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

Niesen, L. B. P.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

Otsu, N.

N. Otsu, “A threshold selection method from gray level histogram,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

Ovadia, Z.

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Ovadia-Blechman, Z.

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

M. Lustig, A. Levy, K. Kopplin, Z. Ovadia-Blechman, and A. Gefen, “Beware of the toilet: The risk for a deep tissue injury during toilet sitting,” J. Tissue Viability 27(1), 23–31 (2018).
[Crossref]

Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
[Crossref]

Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
[Crossref]

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Palevski, D.

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Panicker, T. M. R.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Philip, J.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Rabin, N.

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar, and D. Castel, “Noninvasive monitoring of peripheral microcirculatory hemodynamics under varying degrees of hypoxia,” Respir. Physiol. Neurobiol. 216, 23–27 (2015).
[Crossref]

Raj, B.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Rajab, T. K.

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Rao, P.

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

Rimoldi, S. F.

F. H. Messerli, S. F. Rimoldi, and S. Bangalore, “The Transition From Hypertension to Heart Failure,” JACC Hear. Fail. 5(8), 543–551 (2017).
[Crossref]

Ring, E. F. J.

E. F. J. Ring and K. Ammer, “Infrared thermal imaging in medicine,” Physiol. Meas. 33(3), R33–R46 (2012).
[Crossref]

Robinson, J. T.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Sahoo, P. K.

P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph, Image Process. 41(2), 233–260 (1988).
[Crossref]

Saravanan, T.

S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, B. Raj, R. Karunanithi, T. M. R. Panicker, M. P. Korath, and K. Jagadeesan, “Infrared thermal imaging for detection of peripheral vascular disorders,” J. Med. Phys. 34(1), 43–47 (2009).
[Crossref]

Schmidt, J. A.

J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
[Crossref]

Schölkopf, B.

T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36(3), 1171–1220 (2008).
[Crossref]

Sharir, T.

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Shashar, D.

Z. Ovadia-Blechman, B. Gavish, D. Levy-Aharoni, D. Shashar, and V. Aharonson, “The coupling between peripheral microcirculation and slow breathing,” Med. Eng. Phys. 39, 49–56 (2017).
[Crossref]

Sherlock, S. P.

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

Sholomov, M.

I. Gannot, M. Ben-David, O. Hopper, M. Sholomov, E. Katz, and I. Kelson, “A portable thermal imaging device as a feedback system for breast cancer treatment,” in Optical Fibers and Sensors for Medical Diagnostics and Treatment Applications XVIII, I. Gannot, ed. (SPIE, 2018), p. 27.

Shuvi, M.

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

Smola, A. J.

T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36(3), 1171–1220 (2008).
[Crossref]

Soltani, S.

P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph, Image Process. 41(2), 233–260 (1988).
[Crossref]

Strijkers, G. J.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

Su, W.

C. M. Haggerty, A. C. Mattingly, M. C. Gong, W. Su, A. Daugherty, and B. K. Fornwalt, “Telemetric Blood Pressure Assessment in Angiotensin II-Infused ApoE-/- Mice: 28 Day Natural History and Comparison to Tail-Cuff Measurements,” PLoS One 10(6), e0130723 (2015).
[Crossref]

van Assen, H. C.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

van Deel, E. D.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

van Nierop, B. J.

B. J. van Nierop, H. C. van Assen, E. D. van Deel, L. B. P. Niesen, D. J. Duncker, G. J. Strijkers, and K. Nicolay, “Phenotyping of Left and Right Ventricular Function in Mouse Models of Compensated Hypertrophy and Heart Failure with Cardiac MRI,” PLoS One 8(2), e55424 (2013).
[Crossref]

Vapnik, V.

C. Cortes and V. Vapnik, “Support-Vector Networks,” Int. J. Mach. Learn. Cybern. 20(3), 273–297 (1995).
[Crossref]

Varda-Bloom, N.

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

von Wichert, P.

J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
[Crossref]

Walden, R.

Z. Ovadia, R. Kornowski, B. Gavish, D. Chayen, R. Walden, N. Varda-Bloom, A. Battler, and M. Eldar, “Noninvasive evaluation of microcirculatory hemodynamic changes during hemorrhage followed by saline or blood transfusion,” Shock 4(2), 96–101 (1995).
[Crossref]

Weizman-Shammai, E.

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Willerson, J. T.

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Wong, A. K. C.

P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph, Image Process. 41(2), 233–260 (1988).
[Crossref]

Zimmer, Y.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

Zoltnik Kirshenabum, D.

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

Am. J. Cardiol. (1)

M. Madjid, M. Naghavi, B. A. Malik, S. Litovsky, J. T. Willerson, and W. Casscells, “Thermal detection of vulnerable plaque,” Am. J. Cardiol. 90(10), L36–L39 (2002).
[Crossref]

Angiology (1)

J. A. Schmidt, C. Bracht, A. Leyhe, and P. von Wichert, “Transcutaneous measurement of oxygen and carbon dioxide tension (TcPO2 and TcPCO2) during treadmill exercise in patients with arterial occlusive disease (AOD)–stages I and II,” Angiology 41(7), 547–552 (1990).
[Crossref]

Ann. N. Y. Acad. Sci. (1)

T. J. Love, “Thermography as an Indicator of Blood Perfusion,” Ann. N. Y. Acad. Sci. 335(1 Thermal Chara), 429–437 (1980).
[Crossref]

Ann. Stat. (1)

T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36(3), 1171–1220 (2008).
[Crossref]

Biomed. Opt. Express (1)

Biomed. Pharmacother. (1)

Z. Ovadia-Blechman, I. Avrahami, E. Weizman-Shammai, T. Sharir, M. Eldar, and P. Chouraqui, “Peripheral microcirculatory hemodynamic changes in patients with myocardial ischemia,” Biomed. Pharmacother. 74, 83–88 (2015).
[Crossref]

Circulation (1)

N. Naftali-Shani, L.-P. Levin-Kotler, D. Palevski, U. Amit, D. Kain, N. Landa, E. Hochhauser, and J. Leor, “Left Ventricular Dysfunction Switches Mesenchymal Stromal Cells Toward an Inflammatory Phenotype and Impairs Their Reparative Properties Via Toll-Like Receptor-4,” Circulation 135(23), 2271–2287 (2017).
[Crossref]

Clin. Biomech. (1)

Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson, and N. Rabin, “The response of peripheral microcirculation to gravity-induced changes,” Clin. Biomech. 57, 19–25 (2018).
[Crossref]

Comput. Vision, Graph, Image Process. (1)

P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph, Image Process. 41(2), 233–260 (1988).
[Crossref]

IEEE Trans. Syst. Man Cybern. (1)

N. Otsu, “A threshold selection method from gray level histogram,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

Int. J. Mach. Learn. Cybern. (1)

C. Cortes and V. Vapnik, “Support-Vector Networks,” Int. J. Mach. Learn. Cybern. 20(3), 273–297 (1995).
[Crossref]

J. Am. Heart Assoc. (1)

H. Kosuge, S. P. Sherlock, T. Kitagawa, R. Dash, J. T. Robinson, H. Dai, and M. V. McConnell, “Near Infrared Imaging and Photothermal Ablation of Vascular Inflammation Using Single-Walled Carbon Nanotubes,” J. Am. Heart Assoc. 1(6), e002568 (2012).
[Crossref]

J. Biomed. Opt. (1)

O. A. Hoffer, M. A. Ben-David, E. Katz, D. Zoltnik Kirshenabum, D. Alezra, Y. Zimmer, I. Kelson, and I. Gannot, “Thermal imaging as a tool for evaluating tumor treatment efficacy,” J. Biomed. Opt. 23(05), 1 (2018).
[Crossref]

J. Card. Surg. (1)

P. Rao, J. B. Keenan, T. K. Rajab, A. Ferng, S. Kim, and Z. Khalpey, “Intraoperative thermographic imaging to assess myocardial distribution of Del Nido cardioplegia,” J. Card. Surg. 32(12), 812–815 (2017).
[Crossref]

J. Med. Phys. (1)

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Figures (7)

Fig. 1.
Fig. 1. Thermal Imaging Analysis Process: Images were captured by the commercially available FLIR One thermal camera device and analyzed using the FLIR Tools software. A. The camera connects to a smartphone placed 10 cm above the heated echocardiogram platform (37° C) to which the sedated mouse is fixed. B. An optical image of the sedated mouse. C. The captured thermal image (IRON scale). D. The same image as C in HC Rainbow scale. The region of interest representing the heart is marked (white square). E. The thermal image representing the heart (white dotted square) after reducing background noise according to temperature range (see details in Methods section).
Fig. 2.
Fig. 2. Angiotensin Infusion Induces Cardiac Hypertrophy and Remodeling: We sought to evaluate the cardiac effects of continuous angiotensin infusion (2 mg/kg/day) for 28 days. A-B. We assessed cardiac structural changes over time via transthoracic echocardiography. A. Images show a parasternal long-axis view of the left ventricle (LV). Angiotensin-treated hearts (left) are dilated compared with control mice (right). B. The graphs display representative parameters indicating cardiac hypertrophy and remodeling. Data are shown as mean ± SEM. P values were calculated by a two-way repeated measure ANOVA followed by Sidak's test for multiple comparisons. C. At the end of the experiment (day 28), hearts were stained with Masson’s Trichrome staining and analyzed using planimetry software. Coronary perivascular fibrosis was assessed as the surrounding fibrotic area divided by the whole vessel wall. Angiotensin infusion significantly increased perivascular fibrosis. Data are shown as mean ± SEM with individual measurements. P value was calculated by a Mann-Whitney test. Representative pictures of the stained blood vessels are shown. Ang II- Angiotensin II
Fig. 3.
Fig. 3. Thermal Imaging Detects Cardiac Structural Changes: In the first round of the experiment (n = 3), we captured thermal images on day 28 (an average of 2 pictures per mouse). Images were processed as described in the Methods section. We measured the fraction of the skin heated by the heart (in pixels). A. Angiotensin-treated hearts were larger than saline controls. Data are shown as mean ± SEM. P value was calculated by a Mann-Whitney test. B. Interestingly, the hearts from the angiotensin group all displayed a unique triangle-like shape of heat distribution that was not seen in the saline group. Representative images are shown in B; the thermal image of the hearts is placed on top of the optical image according to the region of interest processed by our algorithm (white arrow). Ang II- Angiotensin II
Fig. 4.
Fig. 4. Thermal Imaging of Cardiac Hypertrophy over Time: To assess thermal imaging and cardiac remodeling over time, the second experiment consisted of images captured at baseline and after 28 days. The area of heat over the heart of angiotensin-treated mice was increased by 430% on day 28. These differences were not significant at baseline. Individual measurements are presented. P values were calculated by a two-way repeated measure ANOVA followed by Sidak's test for multiple comparisons. Ang II- Angiotensin II
Fig. 5.
Fig. 5. Thermal Imaging Parameters Correlate with Echocardiographic Findings: We sought to evaluate the correlation of our findings with parameters of echocardiography indicating cardiac hypertrophy. Cumulative data from both rounds (n = 6) demonstrated a significant correlation between heart size estimated by thermal imaging (measured in pixels), and echocardiographic measurements of both left ventricular volume (left) and mass (right). Data are shown as individual points with a linear trend. P values were calculated by Pearson’s test. LV- Left ventricle
Fig. 6.
Fig. 6. Angiotensin infusion does not elevate local inflammation after 28 days: We measured innate inflammatory cell infiltration to the heart by flow cytometry. We isolated cells from the hearts of mice after 28 days of angiotensin/saline infusion. Hearts of angiotensin-treated mice did not have elevated levels of monocytes (CD11+, left) or macrophages (F4/80+, right). P values were calculated by an ordinary two-way ANOVA with Sidak’s test for multiple comparisons.
Fig. 7.
Fig. 7. Summary Fig.: A graphic scheme describing our new non-invasive tool for imaging structural changes in diseased hearts.

Tables (7)

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Table 1. Confusion matrix for kernel-SVM classification based on all extracted thermal features

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Table 2. Thermal-based input data set X for kernel-SVM along with the corrected and predicted classification vector Y

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Table 3. Echocardiography-based input data set X for kernel-SVM along with the corrected and predicted classification vector Y

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Table 4. Confusion matrix for kernel SVM classification based on absolute heart and body temperature

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Table 5. Confusion matrix for kernel SVM classification based on heart size, compactness, and body to heart temperature difference and ratio

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Table 6. Confusion matrix for kernel SVM classification based on body to heart temperature difference and ratio

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Table 7. Confusion matrix for kernel SVM classification based on heart size and compactness