Quantitative Dynamic Contrast-Enhanced MRI (DCE-MRI) of Tumor Angiogenesis

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1 Quantitative Dynamic Contrast-Enhanced MRI (DCE-MRI) of Tumor Angiogenesis Hai-Ling Margaret Cheng, Ph.D. Medical Biophysics, University of Toronto The Hospital for Sick Children June 20, 2011

2 Outline What and why of DCE-MRI DCE-MRI classifications Quantitative DCE-MRI Steps Scientific issues

3 Tumor angiogenesis Tumor blood vessels differ from normal vasculature Tortuous, dilated, AV shunts, rapidly proliferating endothelium Leakier vessel walls, poor blood flow, decreased vessel stability Normal vessels Tumor vessels Capillary Venule Arteriole With permission from MacDonald DM, Choyke PL. Nat Med 9, , 2003.

4 Tumor angiogenesis Function and morphology of tumor vasculature determines tumor growth, metastatic potential, and therapeutic response New anti-cancer therapies selectively target tumor vasculature (e.g. prune immature vessels reduced vascular permeability) Current need for specific quantitative biomarkers of tumor vascular function and morphology to guide selection of treatment strategy, understand biological effects, and monitor response

5 Imaging biomarkers of vascular function PET CT ultrasound optical MRI

6 DCE-MRI paradigm Gd T1 (DCE-MRI) T2* (DSC-MRI) tumor Pre-injection

7 DCE-MRI dynamic data Gd tumor Concentration tumor normal Time

8 DCE-MRI physiological parameters Gd BF tumor PS Capillary blood flow (BF) Capillary blood volume (BV) Capillary permeability (PS) Interstitial volume (EES)

9 DCE-MRI analysis Visual curve score Kuhl CK et al. Radiology 211, , 1999.

10 DCE-MRI analysis Parametric descriptive Signal enhancement TTP AUC MAX slope Time Schlossbauer T et al. Acad Radiol 17, , 2010.

11 DCE-MRI analysis Parametric quantitative BF Dynamic series Plasma flow (ml/min/g) 10 PS 0 Winter JD, St. Lawrence KS, Cheng HL. J Magn Reson Imaging (in press)

12 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation

13 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Motion correction Segmentation AIF (essential for quantitative analysis)

14 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Signal intensity S(t) Time Tracer concentration C(t) Time

15 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Step 1: S(t) R 1 (t) S(t) = Ω M o c exp(-t E R 2 *) f (R 1 (t); T R, ) constant T1 effects from contrast agent S(t) = S f (R 1 (t))

16 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Step 1: S(t) R 1 (t) Need a measurement of pre-injection R 1o R 1 (t) = f -1 { f (R 1o ) S(t)/S o } (non-linear) R 1 (t) = R 1o S(t)/S o (linear)

17 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Step 2: R 1 (t) C(t) R 1 (t) = R 1o + r 1 C(t) (fast water exchange assumption) C(t) = (R 1 (t) -R 1o ) / r 1

18 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Tissue concentration measured C(t) = IRF(t) C A (t) AIF measured Impulse response function unknown Deconvolution F = max { IRF(t) } V = IRF(t) dt

19 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Deconvolution Model-free, constrained, model-based

20 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Deconvolution Model-free, constrained, model-based

21 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Model-based deconvolution IRF(t) = v p δ(t) + K trans exp(-k trans t / v e ) (modified Tofts model) IRF(t) = F p, t < T c EF p exp(-ef p (t-t c ) / v e ), t > T c (AATH model)

22 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Model-based deconvolution # parameters Two-compartment models Parameters estimated 2 K trans = EF p Patlak v p 3 K trans = EF p modified Tofts v p v e 4 E F p AATH v p v e

23 Quantitative DCE-MRI analysis Pre-processing Signal to concentration Parameter estimation Model-based deconvolution Winter JD, St. Lawrence KS, Cheng HL. J Magn Reson Imaging (in press)

24 Inject contrast agent (CA) DCE-MRI an illustrative walk-through

25 DCE-MRI an illustrative walk-through Inject contrast agent (CA) Acquire data Blood Tissue Time

26 DCE-MRI an illustrative walk-through Inject contrast agent (CA) Acquire data Blood Baseline T1 C p (t) - AIF Time Tissue C t (t) Time Cheng HL, Wright GA. Magn Reson Med 55, , 2006.

27 DCE-MRI an illustrative walk-through Inject contrast agent (CA) Acquire data Baseline T1 Time Tissue C t (t) Time Cheng HL. J Magn Reson Imaging 25, , Tofts PS et al. Magn Reson Med 33, , Di Giovanni P et al. Phys Med Biol 55, , 2010.

28 DCE-MRI an illustrative walk-through Inject contrast agent (CA) Acquire data Blood Baseline T1 C p (t) - AIF Cheng HL. J Magn Reson Imaging 28, , 2008.

29 DCE-MRI an illustrative walk-through Measuring the AIF dual bolus injection Prebolus AIF for predicting full bolus AIF Rapid AIF sampling Kershaw LE, Cheng HL. Magn Reson Imaging 29, , 2011.

30 DCE-MRI an illustrative walk-through Inject contrast agent (CA) Acquire data Analyse data Blood Baseline T1 C p (t) - AIF Time Tissue C t (t) Time Plasma volume v p EES volume v e Transfer constant K trans Pharmacokinetic model permeability flow

31 DCE-MRI an illustrative walk-through Requirements for more specific pharmacokinetic models Kershaw LE, Cheng HL. Magn Reson Med 64, , 2010.

32 DCE-MRI in practice VX2 rabbit tumor model 23 days post-implantation Winter JD, Akens MK, Cheng HL. Phys Med Biol 56, , 2011.

33 DCE-MRI in practice Blood flow in normal rabbit kidney cortex DCE-MRI ASL DCE-MRI ASL MRI Microspheres 298 ± ± ± 123 Flow in ml/100g/min Winter JD, St. Lawrence KS, Cheng HL. J Magn Reson Imaging (in press)

34 Summary DCE-MRI can play a critical role in non-invasive measurement of vascular function for disease assessment and treatment planning and monitoring Unsolved scientific problems Optimal DCE-MRI measurement and analysis approaches for improved sensitivity and specificity remain an area of research Unmet clinical needs International consensus on standardized DCE-MRI methods Automated post-processing methods Translate into clinical workup

35 Thank you!