Quantitative analysis of tumor burden in mouse lung via MRI.

Lung cancer is the leading cause of cancer death in the United States. Despite recent advances in screening protocols, the majority of patients still present with advanced or disseminated disease. Preclinical rodent models provide a unique opportunity to test novel therapeutic drugs for targeting lung cancer. Respiratory-gated MRI is a key tool for quantitatively measuring lung-tumor burden and monitoring the time-course progression of individual tumors in mouse models of primary and metastatic lung cancer. However, quantitative analysis of lung-tumor burden in mice by MRI presents significant challenges. Herein, a method for measuring tumor burden based upon average lung-image intensity is described and validated. The method requires accurate lung segmentation; its efficiency and throughput would be greatly aided by the ability to automatically segment the lungs. A technique for automated lung segmentation in the presence of varying tumor burden levels is presented. The method includes development of a new, two-dimensional parametric model of the mouse lungs and a multi-faceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation (0.93), comparable with that of fully manual expert segmentation, between the automated method's tumor-burden metric and the tumor burden measured by lung weight.

Automatic segmentation of rodent spinal cord diffusion MR images.

MRI, is a key tool for noninvasive spinal cord lesion analysis; however, accurate, quantitative methods for this analysis are lacking. A new, multistep, multidimensional approach, utilizing the classification expectation maximization algorithm, is proposed for MRI segmentation of spinal cord tissues. Diffusion tensor imaging is used to generate multiple images of each spinal slice, with different diffusion direction weightings. The maximum likelihood tissue classifications are then jointly estimated to produce a binary classification image, corresponding to voxels containing either spinal cord or background. Edge detection is employed to find a nonparametric curve encapsulating the entire spinal cord. The algorithm is evaluated using data from in vivo diffusion tensor imaging of control and injured mouse spinal cords. The algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation in the presence of significant injury. The results of the method are shown to be at least on par with expert manual segmentation.

Quantitative monitoring of mouse lung tumors by magnetic resonance imaging.

Primary lung cancer remains the leading cause of cancer-related death in the Western world, and the lung is a common site for recurrence of extrathoracic malignancies. Small-animal (rodent) models of cancer can have a very valuable role in the development of improved therapeutic strategies. However, detection of mouse pulmonary tumors and their subsequent response to therapy in situ is challenging. We have recently described MRI as a reliable, reproducible and nondestructive modality for the detection and serial monitoring of pulmonary tumors. By combining respiratory-gated data acquisition methods with manual and automated segmentation algorithms described by our laboratory, pulmonary tumor burden can be quantitatively measured in approximately 1 h (data acquisition plus analysis) per mouse. Quantitative, analytical methods are described for measuring tumor burden in both primary (discrete tumors) and metastatic (diffuse tumors) disease. Thus, small-animal MRI represents a novel and unique research tool for preclinical investigation of therapeutic strategies for treatment of pulmonary malignancies, and it may be valuable in evaluating new compounds targeting lung cancer in vivo.