CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

SUMMARY In vivo dynamic contrast-enhanced imaging tools provide non-invasive methods for analyzing various functional changes associated with disease initiation, progression and responses to therapy. The quantitative application of these tools has been hindered by its inability to accurately resolve and characterize targeted tissues due to spatially mixed tissue heterogeneity. Convex Analysis of Mixtures - Compartment Modeling (CAM-CM) signal deconvolution tool has been developed to automatically identify pure-volume pixels located at the corners of the clustered pixel time series scatter simplex and subsequently estimate tissue-specific pharmacokinetic parameters. CAM-CM can dissect complex tissues into regions with differential tracer kinetics at pixel-wise resolution and provide a systems biology tool for defining imaging signatures predictive of phenotypes. AVAILABILITY The MATLAB source code can be downloaded at the authors' website www.cbil.ece.vt.edu/software.htm CONTACT yuewang@vt.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[2]  Chong-Yung Chi,et al.  Convex analysis and separation of composite signals in DCE-MRI , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[3]  I. Jolliffe.  Principal Component Analysis , 2002 .

[4]  Gregory S Karczmar,et al.  Multiple reference tissue method for contrast agent arterial input function estimation , 2007, Magnetic resonance in medicine.

[5]  G Brix,et al.  Multicompartment analysis of gadolinium chelate kinetics: Blood‐tissue exchange in mammary tumors as monitored by dynamic MR imaging , 1999, Journal of magnetic resonance imaging : JMRI.

[6]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[7]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[8]  Jianhua Xuan,et al.  Modeling and Reconstruction of Mixed Functional and Molecular Patterns , 2006, Int. J. Biomed. Imaging.

[9]  David J. Miller,et al.  A Mixture Model and EM-Based Algorithm for Class Discovery, Robust Classification, and Outlier Rejection in Mixed Labeled/Unlabeled Data Sets , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  A. Jackson,et al.  Comparative study into the robustness of compartmental modeling and model‐free analysis in DCE‐MRI studies , 2006, Journal of magnetic resonance imaging : JMRI.

[11]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[12]  Thomas E Yankeelov,et al.  Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. , 2007, Magnetic resonance imaging.

[13]  Anders M. Dale,et al.  Depth-resolved optical imaging and microscopy of vascular compartment dynamics during somatosensory stimulation , 2007, NeuroImage.

[14]  Chong-Yung Chi,et al.  Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  J. Brown,et al.  Exploiting tumour hypoxia in cancer treatment , 2004, Nature Reviews Cancer.

[16]  M. Knopp,et al.  Microarray Gene Expression Analysis of Murine Tumor Heterogeneity Defined by Dynamic Contrast-Enhanced MRI , 2002 .

[17]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[18]  E. Hillman,et al.  All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast. , 2007, Nature photonics.

[19]  P. Choyke,et al.  Imaging of angiogenesis: from microscope to clinic , 2003, Nature Medicine.

[20]  A R Padhani,et al.  Dynamic contrast-enhanced MRI studies in oncology with an emphasis on quantification, validation and human studies. , 2001, Clinical radiology.

[21]  Charles A. Bouman,et al.  Clustered components analysis for functional MRI , 2004, IEEE Transactions on Medical Imaging.

[22]  R. Jain.  Normalization of Tumor Vasculature: An Emerging Concept in Antiangiogenic Therapy , 2005, Science.

[23]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[24]  A Heerschap,et al.  Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors , 2001, Journal of magnetic resonance imaging : JMRI.

[25]  A. Padhani.  MRI for assessing antivascular cancer treatments. , 2003, The British journal of radiology.

[26]  Howard Y. Chang,et al.  Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.

[27]  A. Butler,et al.  Spectroscopic (multi-energy) CT distinguishes iodine and barium contrast material in MICE , 2010, European Radiology.

[28]  M. Knopp,et al.  Functional tumor imaging with dynamic contrast‐enhanced magnetic resonance imaging , 2003, Journal of magnetic resonance imaging : JMRI.

[29]  Marcelino Bernardo,et al.  The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment. , 2010, Diagnostic and interventional radiology.

[30]  G Brix,et al.  Pathophysiologic basis of contrast enhancement in breast tumors , 1999, Journal of magnetic resonance imaging : JMRI.

[31]  Keith L. Black,et al.  A modeling-based factor extraction method for determining spatial heterogeneity of Ga-68 EDTA kinetics in brain tumors , 1996 .

[32]  Christopher M. Bishop,et al.  A Hierarchical Latent Variable Model for Data Visualization , 1998, IEEE Trans. Pattern Anal. Mach. Intell..