Nkiruka C. Atuegwu

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Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging (MRI) data of 28 patients were obtained pretreatment, after one cycle, and after completion of all cycles of neoadjuvant chemotherapy (NAC). For each patient at each time point, the tumor cell number was estimated using the apparent diffusion coefficient and the extravascular(More)
Quantitative imaging data obtained from multiple modalities may be integrated into mathematical models of tumor growth and treatment response to achieve additional insights of practical predictive value. We show how this approach can describe the development of tumors that appear realistic in terms of producing proliferating tumor rims and necrotic cores.(More)
We build on previous work to show how serial diffusion-weighted MRI (DW-MRI) data can be used to estimate proliferation rates in a rat model of brain cancer. Thirteen rats were inoculated intracranially with 9L tumor cells; eight rats were treated with the chemotherapeutic drug 1,3-bis(2-chloroethyl)-1-nitrosourea and five rats were untreated controls. All(More)
In many clinical applications of image-guided surgery, skin fiducial placement is poorly defined and occasionally poorly executed, leading to an increase in the target registration error (TRE). Fiducial placement analysis usually focuses on a single target, where surgical guidance requires accurate localization of a region or volume of tissue. To address(More)
With the recent development of integrated positron emission tomography-magnetic resonance imaging (PET-MRI) scanners, new possibilities for quantitative molecular imaging of cancer are realized. However, the practical advantages and potential clinical benefits of the ability to record PET and MRI data simultaneously must be balanced against the substantial(More)
PURPOSE The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of(More)
Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here(More)
Diffusion-weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion-weighted magnetic resonance imaging immediately before, after one cycle, and after all(More)
PURPOSE The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level. METHODS First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images(More)
We review current approaches to predicting tumor growth and treatment response that combine non-invasive imaging data with mathematical models of cancer progression, and propose some new directions for integrating quantitative imaging measurements with such numerical analyses. Historically, tumor modeling has been described by parameters that are measurable(More)