101. PIXEL-BY-PIXEL CONTRAST-ENHANCED ULTRASOUND TIME-INTENSITY CURVE ANALYSIS FOR AUTOMATIC TUMOR DIAGNOSIS

Department: Electrical & Computer Engineering
Faculty Advisor(s): Andrew Kummel
Award(s): Department Best Poster

Primary Student
Name: Casey Nghia Ta
Email: cnta@ucsd.edu
Phone: 858-876-5439
Grad Year: 2013

Abstract
Contrast-enhanced ultrasound (CEUS) enables highly specific time-resolved imaging of vasculature by intravenous injection of ~2 Ám gas filled microbubbles. To develop a quantitative automated diagnosis of breast tumors with CEUS, breast tumors were induced in rats by administration of N-ethyl-N-nitrosourea (ENU). A bolus injection of microbubbles was administered and CEUS videos of each tumor were acquired for at least 3 minutes. The time-intensity curve (TIC) of each pixel within a region of interest (ROI) was analyzed to measure kinetic parameters associated with the wash-in, peak enhancement, and wash-out phases of microbubble bolus injections since it was expected that the aberrant vascularity of malignant tumors will result in faster and more diverse perfusion kinetics versus those of benign lesions. Parameters were classified using linear discriminant analysis (LDA) to differentiate between benign and malignant tumors and improve diagnostic accuracy. Preliminary results with a small dataset (10 tumors, 19 videos) showed 100% accuracy with 5-fold cross-validation testing using as few as 2 choice variables for training and validation. Several of the parameters which provided the best differentiation between malignant and benign tumors employed comparative analysis of all the pixels in the ROI including enhancement coverage (EC), fractional enhancement coverage times (FECT), and the standard deviation of the envelope curve difference normalized to the mean of the peak frame (OEDN). Analysis of combinations of 5 variables demonstrated that pixel-by-pixel (PxP) analysis produced the most robust information for tumor diagnostics and achieved 5 times greater separation of benign and malignant cases than ROI-based analysis.

Related Links:

  1. http://kummel.ucsd.edu/crin/

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