With perfusion magnetic resonance imaging (pMRI), perfusion describes the amount of blood passing through a block of tissue in a certain period of time. In pMRI, the tissue having more blood passing through will show higher intensity value as more contrast-labeled blood arrives. Perfusion reflects the delivery of essential nutrients to a block of tissue, and is an important parameter for the tissue status. Considering solitary pulmonary nodules (SPN), perfusion differences between malignant and benign nodules have been studied by different techniques. Much effort has been put into its characterization. In this paper, we proposed and implemented extraction of the SPN time intensity profile to measure blood delivery to solitary pulmonary nodules, describing their perfusion effects. In this method, a SPN time intensity profile is created based on intensity values of the solitary pulmonary nodule in lung pMRI images over time. This method has two steps: nodule tracking and profile clustering. Nodule tracking aligns the solitary pulmonary nodule in pMRI images taken at different time points, dealing with nodule movement resulted from breathing and body movement. Profile clustering implements segmentation of the nodule region and extraction of the time intensity profile of a solitary pulmonary nodule. SPN time intensity profiles reflect patterns of blood delivery to solitary pulmonary nodules, giving us a description of perfusion effect and indirect evidence of tumor angiogenesis. Analysis on SPN time intensity profiles will help the diagnosis of malignant nodules for early lung cancer detection.
To detect lung cancer at an earlier stage, a promising method is to apply perfusion magnetic resonance imaging (pMRI) modified to assess tumor angiogenesis. One key issue is to effectively characterize angiogenic patterns of pulmonary nodules. Based on our previous study addressing this issue, in this work, we develop STAT, a Spatio-Temporal Analysis Tool that implements not only our previously proposed pulmonary nodule modeling framework but also a user friendly interface and many extended functions. Our goal is to make STAT an easy-to-use tool that can be applied to more general cases. STAT employs the following overall strategy for modeling pulmonary nodules: (1) nodule identification using a correlation maximization method, (2) nodule segmentation using edge detection, morphological operations and model-based strategy, and (3) nodule registration using landmark approach and thin-plate spline interpolation. In nodule identification, STAT provides new schemes for selecting the template and refining results in difficult cases. In nodule segmentation, STAT provides additional flexibilities for creating the weighting mask, selecting morphological structure elements and individually fixing segmentation result. In nodule registration, our previous study uses principal component analysis for landmark extraction, which may not work in general. To overcome this limitation, STAT provides an enhanced approach that minimizes the bending energy of the thin plate spline interpolation or mean square distance between each landmark set and the template set. Our main application of STAT is to define blood arrival patterns in the lung to identify tumor angiogenesis as a means of early accurate diagnosis of cancer.