Identifying Tumor Vascular Permeability Heterogeneity Using Reduced Encoding Techniques

Michael Aref, Jim Xiuquan Ji, Josh D. Handbury, Keith L. Bailey, Zhi-Pei Liang & Erik C. WienerUniversity of Illinois at Urbana-Champaign, Urbana, IL

Hypothesis & Specific AimsWe test the hypothesis that in clinical dynamic contrast enhanced (DCE) magnetic resonance mammography (MRM) the loss of spatial resolution to gain temporal resolution results in partial volume effects that yield inaccurate permeability-surface area products (PS) which results in erroneous diagnostic information and we offer a potential solution using reduced encoding techniques to solve this problem. Specifically we

•Compare the PS obtained from DCE MRI at a resolution obtained with standard in vivo MRI human techniques (2500 µm resolution), to that obtained with higher resolution techniques, analogous to in vitro histopathological in plane resolutions (938 µm and 469 µm resolution)

•Determine the accuracy of PS obtained from Keyhole and Reduced-encoding Imaging by Generalized-series Reconstruction (RIGR) using high resolution baseline data (469 µm resolution) and clinical resolution dynamic data (2500 µm resolution).

•Statistically correlate PS maps obtained from 469 µm, 938 µm, and 2500 µm resolution DCE MRI to histopathologically determined tumor diagnosis based on capillary density and tumor grade.

Background• Weidner et al determined that the

range of microscope fields of view (FOV) for accurately determining tumor type and grade based on capillary density was between 0.152 mm2 (390. µm diameter) and 0.740 mm2 (860. µm diameter). These correspond with our 469 × 469 µm and 938 × 938 µm MRI resolutions (Fig. 1).

• Keyhole uses high-resolution reference data with the central k-space data replaced by dynamic low-resolution data. A fast Fourier transform (FFT) is used to form the active image.

• RIGR uses a high-resolution reference k-space data set to reconstruct images from the low-resolution dynamic k-space data. RIGR is performed in the phase encode (PE) direction and the readout (RO) direction is transformed by FFT.

• Two compartment model (Fig. 2):

• Plasma Compartment:

• Tumor Compartment:

Boreal Research Microscope courtesy of Sciencekit.com

( )[ ] ( )t2

t1p eaeaDtCA β−α− +=

CA t t( )[ ]≈ Da1 vp +ve

1− VtαKp↔t

e−αt + Da2 vp +ve

1− VtβKp↔ t

e−βt +Da1ve

1− VtαKp↔ t

+Da2ve

1− VtβKp↔ t

e−

Kp↔t

Vt

t

Materials and Methods• Female Sprague Dawley rat N-ethyl-

N-nitrosourea (ENU) induced mammary tumor model (Stoica et al).

• SISCO 4.7 T / 33 cm bore system using fast T1-weighted gradient echo multislice (GEMS) (TR = 70 ms, TE = 4.7 ms, flip angle = 80°, # slices = 7, thickness = 2 mm, coronal orient., FOV = RO 24 cm/512 × PE 6 cm/128, avg. = 4, TA = 35 s + 10 s delay).

• Data Preparation

All low-resolution (> 469 µm) data were obtained from central k-space subsets of high-resolution data.

469 µm: RO 24 cm/512 × PE 6 cm/128

938 µm: RO 24 cm/256 × PE 6 cm/64

2500 µm: RO 24 cm/96 × PE 6 cm/24

Dynamic: RO 24 cm/512 × PE 6 cm/24

• Normalize rats by fitting plasma compartment for a1, a2, α and β (Fig. 2 and Eqn 1)

• Fit for tumor extravascular extracellular space (EES) volume fraction, vp, tumor plasma volume fraction, ve, and contrast agent transfer rate constant Kp↔t/VT (Kp↔t = PS) for each voxel within tumor ROI (Fig. 2, 3 and Eqn 2)

• Filtering by dropping points that

–do not converge

–physiologically unrealistic

–were poorly fit (r2 ≤ 0.5)

Results• Effect of Resolution on PS (Fig. 4-6)

Comparison of top five Kp↔t/VT “hot spots” based on resolution.

• PS from Keyhole and RIGR (Fig. 6-8)

Comparison of top five Kp↔t/VT “hot spots” based on reduced encoding technique.

• PS-based diagnosisInfiltrating Ductal Carcinoma (IDC) (n=5)

Non-infiltrating Papillary Carcinoma (n=3)

Comparison of IDC and NPC top five Kp↔t/VT (PS) “hot spots” and their corresponding ve as a function of resolution.

Conclusions• PS estimation is resolution-limited.

• Detection of PS “hot spots” requires a spatial resolution window that includes the 469 µm to 938 µm resolutions if accurate differential tumor diagnosis is to succeed.

• Keyhole produces images of poor quality and cannot detect PS “hot spots” of similar value as FFT.

• RIGR has superior image quality to Keyhole and agrees with FFT on the magnitude of PS “hot spot”.

• The top five Kp↔t/VT “hot spots” and their corresponding ve can differentiate infiltrating ductal carcinomas from non-infiltrating papillary carcinomas at 469 µm and 938 µm resolutions but not at clinical MRI resolution (2500 µm)

Parent Resolution (µm) Compared Resolution (µm) p value469 938 0.0014469 2500 < 0.0001938 2500 < 0.0001

Parent Technique Compared Technique p valueFFT Keyhole 0.0002FFT RIGR 0.2734

469 µm 938 µm 2500 µm ve vp Kp↔ t/VT

L/kg•min ve vp Kp↔ t/VT

L/kg•min ve vp Kp↔ t/VT

L/kg•min Avg 0.035 0.001 0.03 0.031 0.0009 0.016 0.04 0.002 0.008 STD 0.009 0.001 0.01 0.007 0.0007 0.007 0.01 0.001 0.005

469 µm 938 µm 2500 µm

ve vp Kp↔t/VT L/kg•min

ve vp Kp↔t/VT L/kg•min

ve vp Kp↔t/VT L/kg•min

Avg 0.06 0.002 0.04 0.05 0.004 0.03 0.03 0.001 0.012 STD 0.02 0.003 0.03 0.02 0.004 0.02 0.02 0.001 0.008

Resolution Tumor Type Tumor Type p value 469 µm IDC NPC 0.0017 938 µm IDC NPC 0.0047 2500 µm IDC NPC 0.9008

Figure 1

Figure 2

Figure 3

Eqn 1

Eqn 2 The authors would like to acknowledge Dr. Susanne Aref and PHS Grant Number 1 R01 CA87009-01, awarded by the National Institutes of Health, National Cancer Institute

2500 µm

Keyhole

RIGR

469 µm

938 µm

Figure 4

Figure 6

Figure 7

Figure 8

Figure 5