Spatial Resolution Effects on Permeability-Surface Area Estimation

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Information about Spatial Resolution Effects on Permeability-Surface Area Estimation
Health & Medicine

Published on July 22, 2009

Author: MikeAref

Source: slideshare.net

Description

This project attempts to use the optimal parameters for histopathological determination of tumor type and grade and translate them for use with dynamic contrast
enhanced (DCE) MRI. We test the hypothesis that poor spatial resolution used in clinical DCE MRI results in partial volume effects that yield inaccurate physiological
parameters, which results in erroneous diagnostic information. Preliminary data shows that correlation between tumor PS and tumor type and grade done at clinical imager
resolutions are inaccurate. PS estimated from higher resolution DCE MRI obtained by direct imaging or reduced encoding techniques may provide more diagnostically
relevant information.

Spatial Resolution Effects on Permeability-Surface Area Estimation M. Aref1, X. Ji1, J. D. Handbury1, K. Bailey1, Z. Liang1, E. C. Wiener1 1 University of Illinois at Urbana-Champaign, Urbana, IL, United States Synopsis: This project attempts to use the optimal parameters for histopathological determination of tumor type and grade and translate them for use with dynamic contrast enhanced (DCE) MRI. We test the hypothesis that poor spatial resolution used in clinical DCE MRI results in partial volume effects that yield inaccurate physiological parameters, which results in erroneous diagnostic information. Preliminary data shows that correlation between tumor PS and tumor type and grade done at clinical imager resolutions are inaccurate. PS estimated from higher resolution DCE MRI obtained by direct imaging or reduced encoding techniques may provide more diagnostically relevant information. Introduction: One in vitro histopathological technique for differentiating benign from malignant tumors relies on differences in capillary density “hot spots” resulting from angiogenesis or neovascularization. Weidner et al determined that the lowest magnification required for optimally determining tumor type and grade based on capillary density was a microscope field of view (FOV) of 0.740 mm2 (860. µm diameter). The greatest magnification at which capillary densities significantly distinguished benign from malignant tumors was a microscope FOV of 0.152 mm2 (390. µm diameter)(1). Any area between these two limits can be used to grade and, thus accurately distinguish benign from malignant tumors. In DCE MRI it is hypothesized that this neovascularization and these capillary density “hot spots” result in different physiological parameters for benign and malignant tumors, such as the contrast agent transfer rate or permeability surface area product (PS). DCE MRI shows promise as a diagnostic tool based on PS due to differences in capillary density. Diagnostically relevant PS “hot spots” may be missed at clinical imaging (low) spatial resolution. However, high spatial resolution leads to low temporal resolution and a decreased signal-to-noise ratio(2). The diagnostic parameter of DCE MRI, PS, is a resolution-weighted average value(3). One possible solution to the limitations of spatial resolution is using reduced encoding techniques, such as Keyhole, reduced-encoding imaging by generalized-series reconstruction (RIGR), and two- reference reduced-encoding imaging by generalized-series reconstruction (TRIGR). Methods: Fourteen Female Sprague Dawley rats with N-ethyl-N-nitrosourea (ENU) induced mammary tumors(4) were imaged on a SISCO 4.7 T / 33 cm bore system by a rapid T1-weighted GEMS (TR = 70 ms, TE = 4.7 ms, flip angle = 80°, # slices = 7, thickness = 2 mm, coronal orientation, FOV = RO 24 cm / 512 × PE 6 cm / 128, averages = 4, TA = 35 s + 10 s delay). Each rat was imaged under anesthesia (1 mL/kg Ketamine/Xylazine/Acepromazine IM) pre- and post-injection of a bolus of Gd- DTPA (0.3 mmoles/kg IV). Lower resolution images will be formed from central k-space subsets of the high-resolution (469 µm) images(5). The following resolutions were analyzed: 96 RO / 24 PE (2500 µm), 256 RO / 64 PE (938 µm), and 512 RO / 128 PE (469 µm). For Keyhole, RIGR, and TRIGR, FOVREF = RO 24 cm / 512 × PE 6 cm / 128 baseline (pre- injection) and, for TRIGR, active reference sets were used with FOVDYN = RO 24 cm / 512 × PE 6 cm / 24. The active reference image used in TRIGR will be selected based on the average maximum tumor intensity. To find PS, GEMS image signal intensities were converted to contrast agent concentration by a standard curve(3) and fit for PS by a two-compartment model(6):             K p↔ t − [CA (t )]= Da t  ve   ve   a 1v e a2v e  vp + e −αt + Da2 v p + e −βt − D + e v e VT ve VT α ve VTβ v e VTα v e VTβ t 1       1−        1−    1−  1−   K p↔ t   K p↔ t   Kp↔ t K p↔ t  Where D (mmol•kg-1) is the contrast agent dose, a1,2 (kg•L-1) are the normalized concentration amplitudes for unit dose, α and β (min-1) are the distribution and excretion rate constants, Kp↔t (= Pp↔tSpt) (L•kg-1•min-1) is the isodirectional transfer rate per unit volume between the plasma and tumor EES compartments, VT (L•kg-1) is the mass- normalized tumor volume, vp and ve are the tumor plasma and EES volume fractions. The dose, D, is known, while a1,2, α and β are obtained by fitting the contrast agent concentration’s time-dependent biexponential decay from slices through the heart. The parameters, ve, vp and Kp↔t/VT, are fit by a nonlinear least squares fitting by the Gauss-Newton method(7) on a voxel-by-voxel basis. Each mapped point has an F-test for p values and r2(8). The mapped points are filtered: mapped points that (1) did not converge, (2) were physiologically unrealistic, that is, the fitted values must be 0 ve < 1, 0 vp < 1, and 0 Kp↔t/VT, or (3) were poorly fit (r2 0.5), are dropped (set to ≤ ≤ ≤ ≤ zero). All analysis was performed with MATLAB, (The Mathworks, Inc., Natick, MA). Results and Discussion: Of the 14 animals used in this study seven animals bearing 11 tumors were used. Two animals died under anesthesia during imaging and the remainder were discarded because their whole body pharmacokinetic parameters (i.e. a1,2, α and β) were not the physiological norm. As expected, the lower resolution (2500 and 938 µm) top five PS values are significantly different from those obtained with the high-resolution (469 µm) FFT data. This means that the PS values used to distinguish tumor type and grade done at clinical imager resolutions (2500 µm) do not represent areas of pure neovascularization. The diagnostically relevant areas, the PS “hot spots” corresponding to regions of greater capillary density, are lost due to partial volume effects. We used the five highest values of the transfer rates to mimic the use of the highest regions of capillary density in vessel counting histopathological techniques (Table 1). Tumor Type 469 µm FFT parent data 2500 µm FFT (p value to parent) 469 µm TRIGR (p value) 469 µm Keyhole (p value) Invasive Ductal Carcinoma 0.0145 0.00435 (0.00009)** 0.0143 (0.85)+ 0.0122 (0.09)+ Papillary Carcinoma 0.0506 0.0137 (0.000001)** 0.0459 (0.15)+ 0.0359 (0.0006)** Table 1: Averages of the top five transfer rates (L•kg-1•min-1) as a function of tumor type, resolution, and reconstruction algorithm. AnOVa with posthoc comparisons (planned comparison statistical test) was used to determine p values. + not statistically significant, ** statistically significant difference. In conclusion, the transfer rates obtained at resolutions used clinically are lower than those obtained from images acquired at in plane resolutions consistent with in vitro histopathological techniques, and transfer rates calculated from dynamic data sets reprocessed with TRIGR are not significantly different from those obtained from the corresponding high-resolution data sets. References: 1. Weidner, N. (1995) Am J Pathol 147, 9-19. 2. Liang, Z. & Lauterbur, P. (1998) in Principles of MRI: Selected Topics, eds. Markisz, J. & Whalen, J. (Appleton & Lange, London, UK), pp. 199-219. 3. Aref, M., Brechbiel, M. & Wiener, E. C. (2002) Investigative Radiology 37, 178-192. 4. Stoica, G., Koestner, A. & Capen, C. C. (1983) Am J Pathol 110, 161-169. 5. Hanson, J. M., Liang, Z.-P., Wiener, E. C. & Lauterbur, P. C. (1996) Magn Reson Med 36, 172-175. 6. Tofts, P. S. (1997) J Magn Reson Imaging 7, 91-101. 7. Jones, B. A. (1994) (The Mathworks, Inc., Natick, MA). 8. Byrkit, D. R. (1987) Statistics Today: A Comprehensive Introduction (The Benjamin/Cummings Publishing Company, Inc., Menlo Park, CA). Acknowledgements: The Waste Management and Research Center, UIUC, the UIUC College of Medicine Summer Research Fellowship and the National Institutes of Health PHS Grant Numbers 5 T32 CA 09067, 1 R29 CA61918, 1 R01 CA87009-01, and 5 P41 RR05964.

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