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Proceedings Paper

Breast tumour visualization using 3D quantitative ultrasound methods
Author(s): Mehrdad J. Gangeh; Abdul Raheem; Hadi Tadayyon; Simon Liu; Farnoosh Hadizad; Gregory J. Czarnota
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Paper Abstract

Breast cancer is one of the most common cancer types accounting for 29% of all cancer cases. Early detection and treatment has a crucial impact on improving the survival of affected patients. Ultrasound (US) is non-ionizing, portable, inexpensive, and real-time imaging modality for screening and quantifying breast cancer. Due to these attractive attributes, the last decade has witnessed many studies on using quantitative ultrasound (QUS) methods in tissue characterization. However, these studies have mainly been limited to 2-D QUS methods using hand-held US (HHUS) scanners. With the availability of automated breast ultrasound (ABUS) technology, this study is the first to develop 3-D QUS methods for the ABUS visualization of breast tumours. Using an ABUS system, unlike the manual 2-D HHUS device, the whole patient’s breast was scanned in an automated manner. The acquired frames were subsequently examined and a region of interest (ROI) was selected in each frame where tumour was identified. Standard 2-D QUS methods were used to compute spectral and backscatter coefficient (BSC) parametric maps on the selected ROIs. Next, the computed 2-D parameters were mapped to a Cartesian 3-D space, interpolated, and rendered to provide a transparent color-coded visualization of the entire breast tumour. Such 3-D visualization can potentially be used for further analysis of the breast tumours in terms of their size and extension. Moreover, the 3-D volumetric scans can be used for tissue characterization and the categorization of breast tumours as benign or malignant by quantifying the computed parametric maps over the whole tumour volume.

Paper Details

Date Published: 1 April 2016
PDF: 6 pages
Proc. SPIE 9790, Medical Imaging 2016: Ultrasonic Imaging and Tomography, 979007 (1 April 2016); doi: 10.1117/12.2213504
Show Author Affiliations
Mehrdad J. Gangeh, Univ. of Toronto (Canada)
Sunnybrook Health Sciences Ctr. (Canada)
Sunnybrook Research Institute (Canada)
Abdul Raheem, Sunnybrook Research Institute (Canada)
Hadi Tadayyon, Univ. of Toronto (Canada)
Sunnybrook Health Sciences Ctr. (Canada)
Sunnybrook Research Institute (Canada)
Simon Liu, Sunnybrook Research Institute (Canada)
Farnoosh Hadizad, Sunnybrook Research Institute (Canada)
Gregory J. Czarnota, Univ. of Toronto (Canada)
Sunnybrook Health Sciences Ctr. (Canada)
Sunnybrook Research Institute (Canada)


Published in SPIE Proceedings Vol. 9790:
Medical Imaging 2016: Ultrasonic Imaging and Tomography
Neb Duric; Brecht Heyde, Editor(s)

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