ADC based thermometry of the brain in children 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: T1 and T2 relaxation times ADC based Magnetization transfer Temperature-responsive water saturation shift referencing 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: ↑ left ventricular temperature in multiple sclerosis ⁵ versus 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? 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 Mode point of 8th order polynomial curve fitted to histogram = representative to ADC based ventricular 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) 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: Neonatal hypoxic-ischemic injury Cardiac arrest Global hypoxia after drowning Traumatic brain injury

Inclusion criteria Age at MRI < 18 years Ventricles without non-physiological material (e.g. blood, pus, tumour tissue) 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 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 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