EP-134
ADC based thermometry
of the brain in children
Matthias W. Wagner 1, Steven E. Stern 2, Alexander
Oshmyansky 1,2, Thierry A. G. M. Huisman 1, Andrea Poretti 1
1 Section
of Pediatric Neuroradiology, Division of Pediatric Radiology, Russell H. Morgan
Department of Radiology and Radiological Science, The Johns Hopkins University
School of Medicine, Baltimore, MD, USA ² School of Mathematical Sciences, Faculty of
Science and Engineering, Queensland University of Technology, Brisbane, QLD,
Australia
ASNR 53rd Annual Meeting, Chicago, April 25-30, 2015
Disclosure
We have nothing to disclose
No relevant financial relations interfering
with the presentation
Brain temperature
MRI  ideal tool to measure brain
temperature non-invasively
Techniques:
1.
2.
3.
4.
T1 and T2 relaxation times
ADC based
Magnetization transfer
Temperature-responsive water saturation
shift referencing
5. Proton resonance frequency
ADC based thermometry in adults
Sakai, Sai, Tazoe et al:
 ↓ ventricular temperature with ↑ age ¹
 ↓ brain core temperature in mild traumatic brain injury ²
 ↓ brain core temperature in multiple sclerosis ³
 ↑ in ventricular temperature in moyamoya disease ⁴
Hasan et al:
versus
 ↑ left ventricular temperature in multiple sclerosis ⁵
¹ Sakai K, Yamada K, Mori S, Sugimoto N, Nishimura T. NMR Biomed. 2011;24(9):1063-7.
² Tazoe J, Yamada K, Sakai K, Akazawa K, Mineura K. Neuroradiology. 2014.
³ Sai A, Shimono T, Sakai K, Takeda A, Shimada H, Tsukamoto T, et al. J Magn Reson Imaging. 2013.
⁴ Yamada K, Sakai K, Akazawa K, Yuen S, Sugimoto N, Sasajima H, et al. Neuroreport. 2010;21(13):851-5.
⁵ Hasan KM, Lincoln JA, Nelson FM, Wolinsky JS, Narayana PA. Magn Reson Imaging. 2014.
ADC based thermometry: How to do?
1. Extraction¹
Trace of diffusion map
 Semi-automated extraction
of ADC values of each voxel
on Trace of Diffusion map
 Region of Interest covering
the lateral ventricles
¹ Sakai K, Yamada K, Sugimoto N. NMR Biomed. 2012;25(2):340-6.
ADC based thermometry
2. Apply equations ¹,²,³
3. “Mode method” ¹
 Generation of a histogram 
plotting the frequency of
temperature over temperature
D = Diffusion constant (mm²/s)
b = applied diffusion weighting
value (s/mm²)
S0 / S = voxel signal intensities
of reference on DWI/DTI
T = temperature (⁰C)
 Mode point of 8th order
polynomial curve fitted to
histogram = representative to
ADC based ventricular
temperature
¹ Sakai K, Yamada K, Sugimoto N. NMR Biomed. 2012;25(2):340-6. ² Kozak LR, Bango M, Szabo M, Rudas G, Vidnyanszky Z,
Nagy Z. Acta Paediatr. 2010;99(2):237-43. ³ Mills R. The Journal of Physical Chemistry. 1973;77(5):685-8.
Purpose / Possible applications
 To determine the feasibility of ADC based
thermometry to assess intraventricular
temperature in children
Monitoring of therapeutic hypothermia in:
1.
2.
3.
4.
Neonatal hypoxic-ischemic injury
Cardiac arrest
Global hypoxia after drowning
Traumatic brain injury
Inclusion criteria
A. Age at MRI < 18 years
B. Ventricles without non-physiological
material (e.g. blood, pus, tumour tissue)
C. 8 age groups covering 0-18 years to
account for age dependent change of
ventricular size
 0-1 year, 1-2 years, 2-4 years, 4-6 years, 6-8 years,
8-10 years, 10-14 years, 14-18 years
Methods: Validation
 Calculated intraventricular temperature is
correlated with estimated brain temperature
based on temporal artery temperature
measurement
 Measurements before/after each MRI scan 
calculation of a mean temperature
 Temporal artery temperature = body core
temperature = brain temperature - 0.4 ⁰C
 Estimated brain temperature = temporal artery
temperature + 0.4 ⁰C
Methods
Statistical analysis
Difference (ΔT) intraventricular temperature
(ADC based thermometry)  brain
temperature (temporal artery scan)
Spearman’s rank correlation coefficient
calculated  estimated brain temperature
Standard linear regression for the two
temperature measurements
Results 1
Inclusion of 120 children
Correlation coefficient (r) of ADC based
temperatures + estimated brain temperatures
= 0.1, r-squared (R²) = 0.01  1% of changes
in estimated brain temperature attributable
to changes in ADC based temperature
Standard linear regression: p = 0.28  no
statistically significant relationship between
the two temperature measurements
Results 2
Wide range of ΔT calculated  estimated intracranial temperature:
- 5.80 ⁰C to +2.85 ⁰C
Reasons for ↑ ΔT
1. Ventricular size:
 ↑ ventricular size with ↑ age
 Children: ↓ number of ventricular voxels available to
calculate intraventricular temperature
 ↑ proportion of voxels interfacing with adjacent
gray/white matter  ↑ partial volume effects in
children with small ventricles ↑ impact on
calculated temperature
 ↓ number of ventricular voxels  ↓ exactness of
calculated temperature
Reasons for ↑ ΔT
2. Choroid plexus:
 Impact of ependymal cells on diffusion measurement
 Size of choroid plexus stable with ↑ age  choroid
plexus ↑ impact on temperature calculations in
subjects with smaller ventricles
 Our finding: ↓ ΔT in children with larger ventricles
(>8000 voxels)
Reasons for ↑ ΔT
Absolute ΔT: 0 ⁰C - 2.6 ⁰C for ventricles >8000 voxels and 0 ⁰C - 5.8 ⁰C for <8000 voxels
Conclusion
ADC based thermometry = unreliable
method to calculate the intracranial
temperature in children
Most likely due to smaller lateral ventricles
compared to adults