Tong San Koh

Compartmental tracer kinetic models currently used for analysis of dynamic contrast‐enhanced MRI data yield poor fittings or parameter values that are unphysiological in necrotic regions of the tumor, as these models only describe microcirculation in perfused tissue. In this study, we explore the use of Fick's law of diffusion as an alternative method for analysis of dynamic contrast‐enhanced MRI data in the necrotic regions. Xenografts of various human cancer cell lines were implanted in 14 mice that were subjected to dynamic contrast‐enhanced MRI performed using a spoiled gradient recalled sequence. Tracer concentration was estimated using the variable flip angle technique. Poorly perfused and necrotic tumor regions exhibiting delayed and slow enhancement were identified using a k‐means clustering algorithm. Tracer behavior in necrotic regions was shown to be consistent with Fick's diffusion equation and the in vivo gadolinium diffusivity was estimated to be 2.08 (±0.88) × 10⁻⁴ mm²/s. This study proposes the use of gadolinium diffusivity as an alternative parameter for quantifying tracer transport within necrotic tumor regions. Magn Reson Med, 2013.

Tracer kinetic methods employed for quantitative analysis of dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial‐venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods forT₁‐weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast‐enhanced MRI data. J. Magn. Reson. Imaging 2011;.

To devise a noninvasive imaging method for resolving the relative contribution of splenic and splanchnic blood flow to portal venous flow and derive quantitative estimates for parameters pertaining to splenic and portal hemodynamics.

The aim of the present study was to compare three tracer kinetics methods for the analysis of dynamic contrast‐enhanced (DCE) MRI data, namely the generalized kinetics model, the distributed‐parameter model and the initial area under the tumor tracer curve (IAUC) method, in a Phase I study of an anti‐angiogenic drug ABT ‐869; and to explore their utility as biomarkers. Twenty‐eight patients with a range of tumors formed the study population. DCE MRI performed at baseline and 2 weeks post‐treatment was analyzed using all three methods, yielding percentage changes for various tracer kinetics parameters. Correlation analyzes were performed between these parameters and in relation to drug exposure. The association of these parameters with time‐to‐progression was examined using receiver‐operating characteristic and Kaplan–Meier curves. Significant correlation with drug exposure was found for the following parameters: normalized IAUC (IAUC_norm), fractional interstitial volume v_e, fractional intravascular volume v₁ and permeability PS. However, only v_e and PS were effective in predicting late progression. A decrease in v_e of more than 1.7% and a decrease in PS of more than 25.1% observed at 2 weeks post‐treatment could be associated with late progression. All three tracer kinetics methods have biomarker potential for assessing the effects of anti‐angiogenic therapy. Copyright © 2011 John Wiley & Sons, Ltd.

Purpose To study the possibility of using independent component analysis (ICA) to identify breast lesions as separate hemodynamic sources on dynamic contrast-enhanced (DCE) MR images, as depicted by the passage of contrast medium. Materials and Methods Six patients who were histopathologically confirmed with breast carcinoma underwent DCE MRI with 5 precontrast and 60 postcontrast scans at a time-resolution of 8 s. A spatial ICA algorithm was applied on the DCE MRI data set to extract spatial component maps corresponding to source locations with different signal time–intensity patterns. To verify the present hypothesis of the ability of ICA to reveal tumor voxels as a separate hemodynamic phase, tumor margins were outlined by an experienced radiologist who was blinded from the ICA results, and the manual outlines were compared with the ICA maps. Results Consistently for each of the six patient study cases, it was found that ICA yields a tumor component map associated with typical tumor enhancement patterns of rapid enhancement with washout or plateau. Tumor outlines manually drawn by the radiologist were in good agreement with the tumor locations depicted in the tumor component maps. Conclusion ICA may provide an objective method for identifying the outlines of enhancing breast tumors on DCE MR images and to automatically extract the tumor signal intensity–time curve for subsequent tracer kinetics analysis. J. Magn. Reson. Imaging 2008.

Independent component analysis (ICA) was applied on dynamic contrast-enhanced computed tomography images of cerebral tumours to extract spatial component maps of the underlying vascular structures, which correspond to different haemodynamic phases as depicted by the passage of the contrast medium. The locations of arteries, veins and tumours can be separately identified on these spatial component maps. As the contrast enhancement behaviour of the cerebral tumour differs from the normal tissues, ICA yields a tumour component map that reveals the location and extent of the tumour. Tumour outlines can be generated using the tumour component maps, with relatively simple segmentation methods.

We present a numerical deconvolution scheme for estimating regional blood flow and tissue retention functions by dynamic functional imaging. The present approach implements the Tikhonov-Miller regularization in general form, which allows for prior knowledge or assumptions to be incorporated during the deconvolution process, so as to stabilize the solution against variations due to noise. Appropriate approximations and simplifications in the context of functional imaging, were also introduced to ease numerical computations. Monte Carlo simulation experiments were carried out to study the applicability of the present approach and to compare with other deconvolution techniques previously studied.