Abstract

Breast cancer in women is a prevalent disease that takes over 680,000 lives each year worldwide. Early detection of breast cancer through screening has played a significant role in reducing the mortality rates. The current screening paradigm has shown the difficulties in detecting cancers for patients with dense breasts, small and deep tumors, and cancer types that are difficult to visualize. Infrared imaging (IRI) aided by advanced thermal analysis of the breast has shown great promise in detecting cancer using surface temperatures effected by a metabolically active and highly perfused tumor region. We previously developed an inverse heat transfer approach to detect the presence and absence of breast cancer using IRI, called the IRI-Numerical Engine. It was validated with 23 biopsy-proven breast cancer patients irrespective of breast density and cancer type at various tumor depths (0.95 cm–5.45 cm from the breast surface). The current work is aimed to obtain the detectability limit of the IRI-Numerical Engine by testing the capability of detecting 10–20 mm tumors at various depths in patient-specific digital breast models (DBMs). In addition, a study on the effect of tumor size, tumor location, breast shape, and breast size on the surface temperatures of patient-specific models was conducted to verify that an IR camera could capture these surface temperature distributions. The algorithm was able to detect the presence of a tumor at various depths, and deep tumors are detectable given the appropriate thermal sensitive IR camera.

References

1.
Hong
,
R.
, and
Xu
,
B.
,
2022
, “
Breast Cancer: An Up-to-Date Review and Future Perspectives
,”
Cancer Commun.
,
42
(
10
), pp.
913
936
.10.1002/cac2.12358
2.
Sung
,
H.
,
Ferlay
,
J.
,
Siegel
,
R. L.
,
Laversanne
,
M.
,
Soerjomataram
,
I.
,
Jemal
,
A.
, and
Bray
,
F.
,
2021
, “
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
,”
CA: Cancer J. Clin.
,
71
(
3
), pp.
209
249
.10.3322/caac.21660
3.
Łukasiewicz
,
S.
,
Czeczelewski
,
M.
,
Forma
,
A.
,
Baj
,
J.
,
Sitarz
,
R.
, and
Stanisławek
,
A.
,
2021
, “
Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review
,”
Cancers
,
13
(
17
), p.
4287
.10.3390/cancers13174287
4.
Watkins
,
E. J.
,
2019
, “
Overview of Breast Cancer
,”
Jaapa
,
32
(
10
), pp.
13
17
.10.1097/01.JAA.0000580524.95733.3d
5.
Ahmad
,
A.
, ed.,
2019
,
Breast Cancer Metastasis and Drug Resistance: Challenges and Progress
,
Springer
,
Cham, Switzerland
.
6.
Monticciolo
,
D. L.
,
Newell
,
M. S.
,
Hendrick
,
R. E.
,
Helvie
,
M. A.
,
Moy
,
L.
,
Monsees
,
B.
,
Kopans
,
D. B.
,
Eby
,
P. R.
, and
Sickles
,
E. A.
,
2017
, “
Breast Cancer Screening for Average-Risk Women: Recommendations From the ACR Commission on Breast Imaging
,”
J. Am. Coll. Radiol.
,
14
(
9
), pp.
1137
1143
.10.1016/j.jacr.2017.06.001
7.
Narayan
,
A. K.
,
Lee
,
C. I.
, and
Lehman
,
C. D.
,
2020
, “
Screening for Breast Cancer
,”
Med. Clin.
,
104
(
6
), pp.
1007
1021
.10.1016/j.mcna.2020.08.003
8.
Dibden
,
A.
,
Offman
,
J.
,
Duffy
,
S. W.
, and
Gabe
,
R.
,
2020
, “
Worldwide Review and Meta-Analysis of Cohort Studies Measuring the Effect of Mammography Screening Programmes on Incidence-Based Breast Cancer Mortality
,”
Cancers
,
12
(
4
), p.
976
.10.3390/cancers12040976
9.
Mammography
,
2023
, “
National Institute of Biomedical Imaging and Bioengineering
,” accessed Dec. 16, 2023, https://www.nibib.nih.gov/science-education/science-topics/mammography
10.
Lian
,
J.
, and
Li
,
K.
,
2020
, “
A Review of Breast Density Implications and Breast Cancer Screening
,”
Clin. Breast Cancer
,
20
(
4
), pp.
283
290
.10.1016/j.clbc.2020.03.004
11.
Hussein
,
H.
,
Abbas
,
E.
,
Keshavarzi
,
S.
,
Fazelzad
,
R.
,
Bukhanov
,
K.
,
Kulkarni
,
S.
,
Au
,
F.
,
Ghai
,
S.
,
Alabousi
,
A.
, and
Freitas
,
V.
,
2023
, “
Supplemental Breast Cancer Screening in Women With Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis
,”
Radiology
,
306
(
3
), p.
e221785
.10.1148/radiol.221785
12.
Foxcroft
,
L. M.
,
Evans
,
E. B.
,
Joshua
,
H. K.
, and
Hirst
,
C.
,
2000
, “
Breast Cancers Invisible on Mammography
,”
Aust. N. Z. J. Surg.
,
70
(
3
), pp.
162
167
.10.1046/j.1440-1622.2000.01763.x
13.
Rajentheran
,
R.
,
Rao
,
C. M.
,
Lim
,
E.
, and
Lennard
,
T. W. J.
,
2001
, “
Palpable Breast Cancer Which Is Mammographically Invisible
,”
Breast
,
10
(
5
), pp.
416
420
.10.1054/brst.2000.0270
14.
Gruber
,
I. V.
,
Rueckert
,
M.
,
Kagan
,
K. O.
,
Staebler
,
A.
,
Siegmann
,
K. C.
,
Hartkopf
,
A.
,
Wallwiener
,
D.
, and
Hahn
,
M.
,
2013
, “
Measurement of Tumour Size With Mammography, Sonography and Magnetic Resonance Imaging as Compared to Histological Tumour Size in Primary Breast Cancer
,”
BMC Cancer
,
13
(
1
), p.
328
.10.1186/1471-2407-13-328
15.
Wadhwa
,
A.
,
Sullivan
,
J. R.
, and
Gonyo
,
M. B.
,
2016
, “
Missed Breast Cancer: What Can We Learn?
,”
Curr. Probl. Diagn. Radiol.
,
45
(
6
), pp.
402
419
.10.1067/j.cpradiol.2016.03.001
16.
Welch
,
H. G.
,
Prorok
,
P. C.
,
O'Malley
,
A. J.
, and
Kramer
,
B. S.
,
2016
, “
Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening Effectiveness
,”
N. Engl. J. Med.
,
375
(
15
), pp.
1438
1447
.10.1056/NEJMoa1600249
17.
Eichler
,
C.
,
Abrar
,
S.
,
Puppe
,
J.
,
Arndt
,
M.
,
Ohlinger
,
R.
,
Hahn
,
M.
, and
Warm
,
M.
,
2017
, “
Detection of Ductal Carcinoma In Situ by Ultrasound and Mammography: Size-Dependent Inaccuracy
,”
Anticancer Res.
,
37
(
9
), pp.
5065
5070
.10.21873/anticanres.11923
18.
Hadjipanteli
,
A.
,
Elangovan
,
P.
,
Mackenzie
,
A.
,
Wells
,
K.
,
Dance
,
D. R.
, and
Young
,
K. C.
,
2019
, “
The Threshold Detectable Mass Diameter for 2D-Mammography and Digital Breast Tomosynthesis
,”
Phys. Med.
,
57
, pp.
25
32
.10.1016/j.ejmp.2018.11.014
19.
Lee
,
S. H.
,
Jang
,
M. J.
,
Kim
,
S. M.
,
Yun
,
B. L.
,
Rim
,
J.
,
Chang
,
J. M.
,
Kim
,
B.
, and
Choi
,
H. Y.
,
2019
, “
Factors Affecting Breast Cancer Detectability on Digital Breast Tomosynthesis and Two-Dimensional Digital Mammography in Patients With Dense Breasts
,”
Korean J. Radiol.
,
20
(
1
), pp.
58
68
.10.3348/kjr.2018.0012
20.
Wang
,
J.
,
Gottschal
,
P.
,
Ding
,
L.
,
Veldhuizen
,
D. A.
,
Lu
,
W.
,
Houssami
,
N.
,
Greuter
,
M. J. W.
, and
de Bock
,
G. H.
,
2020
, “
Mammographic Sensitivity as a Function of Tumor Size: A Novel Estimation Based on Population-Based Screening Data
,”
Breast
,
55
, pp.
69
74
.10.1016/j.breast.2020.12.003
21.
Sogunro
,
O.
,
Cashen
,
C.
,
Fakir
,
S.
,
Stausmire
,
J.
, and
Buderer
,
N.
,
2021
, “
Detecting Accurate Tumor Size Across Imaging Modalities in Breast Cancer
,”
Breast Dis.
,
40
(
3
), pp.
177
182
.10.3233/BD-201021
22.
Nickson
,
C.
, and
Kavanagh
,
A. M.
,
2009
, “
Tumour Size at Detection According to Different Measures of Mammographic Breast Density
,”
J. Med. Screening
,
16
(
3
), pp.
140
146
.10.1258/jms.2009.009054
23.
Isheden
,
G.
, and
Humphreys
,
K.
,
2019
, “
Modelling Breast Cancer Tumour Growth for a Stable Disease Population
,”
Stat. Methods Med. Res.
,
28
(
3
), pp.
681
702
.10.1177/0962280217734583
24.
Abrahamsson
,
L.
,
Isheden
,
G.
,
Czene
,
K.
, and
Humphreys
,
K.
,
2020
, “
Continuous Tumour Growth Models, Lead Time Estimation and Length Bias in Breast Cancer Screening Studies
,”
Stat. Methods Med. Res.
,
29
(
2
), pp.
374
395
.10.1177/0962280219832901
25.
Howell
,
K.
,
Dudek
,
K.
, and
Soroko
,
M.
,
2020
, “
Thermal Camera Performance and Image Analysis Repeatability in Equine Thermography
,”
Infrared Phys. Technol.
,
110
, p.
103447
.10.1016/j.infrared.2020.103447
26.
Vollmer
,
M.
, and
Möllmann
,
K.-P.
,
2018
,
Infrared Thermal Imaging: Fundamentals, Research and Applications
,
Wiley-VCH Verlag GmbH & Co. KGaA
,
Weinheim, Germany
.
27.
Gautherie
,
M.
,
1980
, “
Thermopathology of Breast Cancer: Measurement and Analysis of In Vivo Temperature and Blood Flow
,”
Ann. N. Y. Acad. Sci.
,
335
(
1
), pp.
383
415
.10.1111/j.1749-6632.1980.tb50764.x
28.
Kandlikar
,
S. G.
,
Perez-Raya
,
I.
,
Raghupathi
,
P. A.
,
Gonzalez-Hernandez
,
J.-L.
,
Dabydeen
,
D.
,
Medeiros
,
L.
, and
Phatak
,
P.
,
2017
, “
Infrared Imaging Technology for Breast Cancer Detection – Current Status, Protocols and New Directions
,”
Int. J. Heat Mass Transfer
,
108
, pp.
2303
2320
.10.1016/j.ijheatmasstransfer.2017.01.086
29.
Gonzalez-Hernandez
,
J.-L.
,
Recinella
,
A. N.
,
Kandlikar
,
S. G.
,
Dabydeen
,
D.
,
Medeiros
,
L.
, and
Phatak
,
P.
,
2019
, “
Technology, Application and Potential of Dynamic Breast Thermography for the Detection of Breast Cancer
,”
Int. J. Heat Mass Transfer
,
131
, pp.
558
573
.10.1016/j.ijheatmasstransfer.2018.11.089
30.
Lozano
,
A.
, and
Hassanipour
,
F.
,
2019
, “
Infrared Imaging for Breast Cancer Detection: An Objective Review of Foundational Studies and Its Proper Role in Breast Cancer Screening
,”
Infrared Phys. Technol.
,
97
, pp.
244
257
.10.1016/j.infrared.2018.12.017
31.
Hakim
,
A.
, and
Awale
,
R. N.
,
2020
, “
Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review
,”
J. Med. Syst.
,
44
(
8
), p.
136
.10.1007/s10916-020-01581-y
32.
Roslidar
,
R.
,
Rahman
,
A.
,
Muharar
,
R.
,
Syahputra
,
M. R.
,
Arnia
,
F.
,
Syukri
,
M.
,
Pradhan
,
B.
, and
Munadi
,
K.
,
2020
, “
A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection
,”
IEEE Access
,
8
, pp.
116176
116194
.10.1109/ACCESS.2020.3004056
33.
Owens
,
A.
,
Kandlikar
,
S. G.
, and
Phatak
,
P.
,
2021
, “
Potential of Infrared Imaging for Breast Cancer Detection: A Critical Evaluation
,”
ASME J. Eng. Sci. Med. Diagn. Ther.
,
4
(
4
), p.
041005
.10.1115/1.4051800
34.
Mashekova
,
A.
,
Zhao
,
Y.
,
Ng
,
E. Y. K.
,
Zarikas
,
V.
,
Fok
,
S. C.
, and
Mukhmetov
,
O.
,
2022
, “
Early Detection of the Breast Cancer Using Infrared Technology – A Comprehensive Review
,”
Therm. Sci. Eng. Prog.
,
27
, p.
101142
.10.1016/j.tsep.2021.101142
35.
Sarigoz
,
T.
,
Ertan
,
T.
,
Topuz
,
O.
,
Sevim
,
Y.
, and
Cihan
,
Y.
,
2018
, “
Role of Digital Infrared Thermal Imaging in the Diagnosis of Breast Mass: A Pilot Study
,”
Infrared Phys. Technol.
,
91
, pp.
214
219
.10.1016/j.infrared.2018.04.019
36.
Pennes
,
H. H.
,
1948
, “
Analysis of Tissue and Arterial Blood Temperatures in the Resting Human Forearm
,”
J. Appl. Physiol.
,
1
(
2
), pp.
93
122
.10.1152/jappl.1948.1.2.93
37.
Shrivastava
,
D.
,
2018
,
Theory and Applications of Heat Transfer in Humans
,
Wiley, Incorporated
,
Newark, UK
.
38.
Etehadtavakol
,
M.
, and
Ng
,
E. Y. K.
,
2020
, “
Survey of Numerical Bioheat Transfer Modelling for Accurate Skin Surface Measurements
,”
Therm. Sci. Eng. Prog.
,
20
, p.
100681
.10.1016/j.tsep.2020.100681
39.
Figueiredo
,
A. A. A.
,
Menegaz
,
G. L.
,
Fernandes
,
H. C.
, and
Guimaraes
,
G.
,
2018
, “
Thermographic Computational Analyses of a 3D Model of a Scanned Breast
,”
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
,
A. F.
Frangi
,
J. A.
Schnabel
,
C.
Davatzikos
,
C.
Alberola-López
, and
G.
Fichtinger
, eds.,
Springer International Publishing
,
Cham
, pp.
885
892
.
40.
Lozano
,
A.
,
Hayes
,
J. C.
,
Compton
,
L. M.
,
Azarnoosh
,
J.
, and
Hassanipour
,
F.
,
2020
, “
Determining the Thermal Characteristics of Breast Cancer Based on High-Resolution Infrared Imaging, 3D Breast Scans, and Magnetic Resonance Imaging
,”
Sci. Rep.
,
10
(
1
), p.
10105
.10.1038/s41598-020-66926-6
41.
Mukhmetov
,
O.
,
Igali
,
D.
,
Mashekova
,
A.
,
Zhao
,
Y.
,
Ng
,
E. Y. K.
,
Fok
,
S. C.
, and
Teh
,
S. L.
,
2021
, “
Thermal Modeling for Breast Tumor Detection Using Thermography
,”
Int. J. Therm. Sci.
,
161
, p.
106712
.10.1016/j.ijthermalsci.2020.106712
42.
Mukhmetov
,
O.
,
Zhao
,
Y.
,
Mashekova
,
A.
,
Zarikas
,
V.
,
Ng
,
E. Y. K.
, and
Aidossov
,
N.
,
2023
, “
Physics-Informed Neural Network for Fast Prediction of Temperature Distributions in Cancerous Breasts as a Potential Efficient Portable AI-Based Diagnostic Tool
,”
Comput. Methods Prog. Biomed.
,
242
, p.
107834
.10.1016/j.cmpb.2023.107834
43.
Figueiredo
,
A. A. A.
,
do Nascimento
,
J. G.
,
Malheiros
,
F. C.
,
da Silva Ignacio
,
L. H.
,
Fernandes
,
H. C.
, and
Guimaraes
,
G.
,
2019
, “
Breast Tumor Localization Using Skin Surface Temperatures From a 2D Anatomic Model Without Knowledge of the Thermophysical Properties
,”
Comput. Methods Prog. Biomed.
,
172
, pp.
65
77
.10.1016/j.cmpb.2019.02.004
44.
Gonzalez-Hernandez
,
J.-L.
,
Kandlikar
,
S. G.
,
Dabydeen
,
D.
,
Medeiros
,
L.
, and
Phatak
,
P.
,
2018
, “
Generation and Thermal Simulation of a Digital Model of the Female Breast in Prone Position
,”
ASME J. Eng. Sci. Med. Diagn. Ther.
,
1
(
4
), p.
041006
.10.1115/1.4041421
45.
Gonzalez-Hernandez
,
J.-L.
,
Recinella
,
A. N.
,
Kandlikar
,
S. G.
,
Dabydeen
,
D.
,
Medeiros
,
L.
, and
Phatak
,
P.
,
2020
, “
An Inverse Heat Transfer Approach for Patient-Specific Breast Cancer Detection and Tumor Localization Using Surface Thermal Images in the Prone Position
,”
Infrared Phys. Technol.
,
105
, p.
103202
.10.1016/j.infrared.2020.103202
46.
Said Camilleri
,
J.
,
Farrugia
,
L.
,
Curto
,
S.
,
Rodrigues
,
D. B.
,
Farina
,
L.
,
Caruana Dingli
,
G.
,
Bonello
,
J.
,
Farhat
,
I.
, and
Sammut
,
C. V.
,
2022
, “
Review of Thermal and Physiological Properties of Human Breast Tissue
,”
Sensors
,
22
(
10
), p.
3894
.10.3390/s22103894
47.
Singh
,
M.
,
2022
, “
Incorporating Vascular-Stasis Based Blood Perfusion to Evaluate the Thermal Signatures of Cell-Death Using Modified Arrhenius Equation With Regeneration of Living Tissues During Nanoparticle-Assisted Thermal Therapy
,”
Int. Commun. Heat Mass Transfer
,
135
, p.
106046
.10.1016/j.icheatmasstransfer.2022.106046
48.
Singh
,
M.
,
2024
, “
Modified Pennes Bioheat Equation With Heterogeneous Blood Perfusion: A Newer Perspective
,”
Int. J. Heat Mass Transfer
,
218
, p.
124698
.10.1016/j.ijheatmasstransfer.2023.124698
49.
Singh
,
M.
,
Ma
,
R.
, and
Zhu
,
L.
,
2021
, “
Quantitative Evaluation of Effects of Coupled Temperature Elevation, Thermal Damage, and Enlarged Porosity on Nanoparticle Migration in Tumors During Magnetic Nanoparticle Hyperthermia
,”
Int. Commun. Heat Mass Transfer
, 126, p. 105393.10.1016/j.icheatmasstransfer.2021.105393
50.
Recinella
,
A. N.
,
Gonzalez-Hernandez
,
J.-L.
,
Kandlikar
,
S. G.
,
Dabydeen
,
D.
,
Medeiros
,
L.
, and
Phatak
,
P.
,
2020
, “
Clinical Infrared Imaging in the Prone Position for Breast Cancer Screening—Initial Screening and Digital Model Validation
,”
ASME J. Eng. Sci. Med. Diagn. Ther.
,
3
(
1
), p.
011005
.10.1115/1.4045319
51.
Gutierrez
,
C.
,
Owens
,
A.
,
Medeiros
,
L.
,
Dabydeen
,
D.
,
Sritharan
,
N.
,
Phatak
,
P.
, and
Kandlikar
,
S. G.
,
2024
, “
Breast Cancer Detection Using Enhanced IRI-Numerical Engine and Inverse Heat Transfer Modeling: Model Description and Clinical Validation
,”
Sci. Rep.
,
14
(
1
), p.
3316
.10.1038/s41598-024-53856-w
52.
Ozisik
,
M. N.
,
2020
,
Inverse Heat Transfer: Fundamentals and Applications
,
Routledge
,
New York
.
53.
Hossain
,
S.
, and
Mohammadi
,
F. A.
,
2016
, “
Tumor Parameter Estimation Considering the Body Geometry by Thermography
,”
Comput. Biol. Med.
,
76
, pp.
80
93
.10.1016/j.compbiomed.2016.06.023
54.
Saniei
,
E.
,
Setayeshi
,
S.
,
Akbari
,
M. E.
, and
Navid
,
M.
,
2016
, “
Parameter Estimation of Breast Tumour Using Dynamic Neural Network From Thermal Pattern
,”
J. Adv. Res.
,
7
(
6
), pp.
1045
1055
.10.1016/j.jare.2016.05.005
55.
Perez-Raya
,
I.
, and
Kandlikar
,
S. G.
,
2023
, “
Thermal Modeling of Patient-Specific Breast Cancer With Physics-Based Artificial Intelligence
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
145
(
3
), p.
031201
.10.1115/1.4055347
56.
Perez-Raya
,
I.
,
Gutierrez
,
C.
, and
Kandlikar
,
S. G.
,
2024
, “
A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
146
(
10
), pp.
1
24
.10.1115/1.4065673
57.
Kennedy
,
D. A.
,
Lee
,
T.
, and
Seely
,
D.
,
2009
, “
A Comparative Review of Thermography as a Breast Cancer Screening Technique
,”
Integr. Cancer Ther.
, 8(1), pp. 9–16.10.1177/1534735408326171
58.
Sritharan
,
N.
,
Gutierrez
,
C.
,
Perez-Raya
,
I.
,
Gonzalez-Hernandez
,
J.-L.
,
Owens
,
A.
,
Dabydeen
,
D.
,
Medeiros
,
L.
,
Kandlikar
,
S.
, and
Phatak
,
P.
,
2024
, “
Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging
,”
Cancers
, 16(12), p. 2264.10.3390/cancers16122264
59.
Swanson
,
E.
,
2017
, “
A Measurement System and Ideal Breast Shape
,”
Evidence-Based Cosmetic Breast Surgery
,
Springer International Publishing
,
Cham
, pp.
19
31
.
60.
Hsia
,
H. C.
, and
Thomson
,
J. G.
,
2003
, “
Differences in Breast Shape Preferences Between Plastic Surgeons and Patients Seeking Breast Augmentation
,”
Plast. Reconstr. Surg.
,
112
(
1
), pp.
312
320
.10.1097/01.PRS.0000066365.12348.A7
61.
Singh
,
M.
,
Singh
,
T.
, and
Soni
,
S.
,
2021
, “
Pre-Operative Assessment of Ablation Margins for Variable Blood Perfusion Metrics in a Magnetic Resonance Imaging Based Complex Breast Tumour Anatomy: Simulation Paradigms in Thermal Therapies
,”
Comput. Methods Prog. Biomed.
,
198
, p.
105781
.10.1016/j.cmpb.2020.105781
62.
Barros
,
T. C.
, and
Figueiredo
,
A. A. A.
,
2023
, “
Three-Dimensional Numerical Evaluation of Skin Surface Thermal Contrast by Application of Hypothermia at Different Depths and Sizes of the Breast Tumor
,”
Comput. Methods Prog. Biomed.
,
236
, p.
107562
.10.1016/j.cmpb.2023.107562
63.
Watmough
,
D. J.
, and
Oliver
,
R.
,
1968
, “
Emissivity of Human Skin in the Waveband Between 2μ and 6μ
,”
Nature
,
219
(
5154
), pp.
622
624
.10.1038/219622a0
64.
Steketee
,
J.
,
1973
, “
Spectral Emissivity of Skin and Pericardium
,”
Phys. Med. Biol.
,
18
(
5
), pp.
686
694
.10.1088/0031-9155/18/5/307
65.
Charlton
,
M.
,
Stanley
,
S. A.
,
Whitman
,
Z.
,
Wenn
,
V.
,
Coats
,
T. J.
,
Sims
,
M.
, and
Thompson
,
J. P.
,
2020
, “
The Effect of Constitutive Pigmentation on the Measured Emissivity of Human Skin
,”
PLoS One
,
15
(
11
), p.
e0241843
.10.1371/journal.pone.0241843
66.
Figueiredo
,
A. A. A.
,
Fernandes
,
H. C.
,
Malheiros
,
F. C.
, and
Guimaraes
,
G.
,
2020
, “
Influence Analysis of Thermophysical Properties on Temperature Profiles on the Breast Skin Surface
,”
Int. Commun. Heat Mass Transfer
,
111
, p.
104453
.10.1016/j.icheatmasstransfer.2019.104453
67.
Wang
,
L.
,
Xu
,
Z.
,
Xu
,
J.
,
Dong
,
F.
,
Wang
,
F.
,
Bai
,
Z.
,
Zhou
,
Y.
, et al.,
2020
, “
Fabrication and Characterization of InAs/GaSb Type-II Superlattice Long-Wavelength Infrared Detectors Aiming High Temperature Sensitivity
,”
J. Lightwave Technol.
,
38
(
21
), pp.
6129
6134
.10.1109/JLT.2020.3005974
68.
He
,
Z.-Z.
, and
Liu
,
J.
,
2017
, “
A Coupled Continuum-Discrete Bioheat Transfer Model for Vascularized Tissue
,”
Int. J. Heat Mass Transfer
,
107
, pp.
544
556
.10.1016/j.ijheatmasstransfer.2016.11.053
You do not currently have access to this content.