Functional CT imaging of prostate cancer.

Department of Radiation Physics, University Health Network-Princess Margaret Hospital, 610 University Avenue, Toronto, ON M5G 2M9, Canada.
Physics in Medicine and Biology (Impact Factor: 2.92). 10/2003; 48(18):3085-100.
Source: PubMed

ABSTRACT The purpose of this paper is to investigate the distribution of blood flow (F), mean capillary transit time (Tc), capillary permeability (PS) and blood volume (vb) in prostate cancer using contrast-enhanced CT. Nine stage T2-T3 prostate cancer patients were enrolled in the study. Following bolus injection of a contrast agent, a time series of CT images of the prostate was acquired. Functional maps showing the distribution of F, Tc, PS and vb within the prostate were generated using a distributed parameter tracer kinetic model, the adiabatic approximation to the tissue homogeneity model. The precision of the maps was assessed using covariance matrix analysis. Finally, maps were compared to the findings of standard clinical investigations. Eight of the functional maps demonstrated regions of increased F, PS and vb, the locations of which were consistent with the results of standard clinical investigations. However, model parameters other than F could only be measured precisely within regions of high F. In conclusion functional CT images of cancer-containing prostate glands demonstrate regions of elevated F, PS and Vb. However, caution should be used when applying a complex tracer kinetic model to the study of prostate cancer since not all parameters can be measured precisely in all areas.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Modeling of the perfusion using dynamic contrast-enhanced (DCE) imaging based on convolution models is gaining increasing attention. The time activity curve of ith voxel is sampled at predefined time intervals, yielding a contrast concentration Ci(t) evaluated at discrete times t=t1…tNT. Perfusion model is then specified to express Ci(t) as the convolution of an impulse response function IRF(t) and the arterial input function A(t). For a given model, IRF(t) is represented as a piecewise analytical function that is parametrized by tissue parameters such as flow rate, transit time, or distribution volume. These parameters {p1, p2, … pn} are then used to fit the measured data Ci(t) by minimizing the residual: Typically the convolution operator is invoked on the order of 104 times for each voxel’s minimization, and the imaging volume of interest contains between 105 and 106 voxels. Thus, an efficient implementation of the convolution represents a significant computational challenge. The naive approach consists of a uniform sampling t=t1, … tN of the temporal domain, extrapolation of A(t) and IRF(t) over these samples, and calculation of convolution either by discrete integration or by multiplying corresponding Fourier transforms. Computational complexity is O(N2) and O(N log(N)) respectively. In practice direct integration is often used due to its simplicity. Additionally, due to discrete sampling of IRF result in an error that is proportional to 1/N. To improve the computational speed and be able to control the convolution error due to discrete sampling of IRF(t) we have developed and implemented the adaptive convolution algorithm. The method was tested for its speed and convolution accuracy on renal perfusion renography data using Gd-DTPA as the tracer.
    ISMRM 2008, Toronoto, Canada; 05/2008
  • [Show abstract] [Hide abstract]
    ABSTRACT: With the introduction of molecularly targeted chemotherapeutics, there is an increasing need for defining new response criteria for therapeutic success because use of morphologic imaging alone may not fully assess tumor response. Computed tomographic (CT) perfusion imaging of the liver provides functional information about the microcirculation of normal parenchyma and focal liver lesions and is a promising technique for assessing the efficacy of various anticancer treatments. CT perfusion also shows promising results for diagnosing primary or metastatic tumors, for predicting early response to anticancer treatments, and for monitoring tumor recurrence after therapy. Many of the limitations of early CT perfusion studies performed in the liver, such as limited coverage, motion artifacts, and high radiation dose of CT, are being addressed by recent technical advances. These include a wide area detector with or without volumetric spiral or shuttle modes, motion correction algorithms, and new CT reconstruction technologies such as iterative algorithms. Although several issues related to perfusion imaging-such as paucity of large multicenter trials, limited accessibility of perfusion software, and lack of standardization in methods-remain unsolved, CT perfusion has now reached technical maturity, allowing for its use in assessing tumor vascularity in larger-scale prospective clinical trials. In this review, basic principles, current acquisition protocols, and pharmacokinetic models used for CT perfusion imaging of the liver are described. Various oncologic applications of CT perfusion of the liver are discussed and current challenges, as well as possible solutions, for CT perfusion are presented. © RSNA, 2014 Online supplemental material is available for this article.
    Radiology 08/2014; 272(2):322-344. · 6.21 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Utilizing CT angiography enhances image quality in PCT, thereby permitting acquisition at ultra-low dose.
    Neuroradiology 08/2014; · 2.37 Impact Factor