Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images

• Authors: Guolan Lu, Georgia Institute of Technology; Luma Halig, Emory University; Dongsheng Wang, Emory University; Georgia Chen, Emory University; Baowei Fei, Emory University
• Proceedings Title: Proceedings of SPIE
• Conference Name: Conference on Medical Imaging - Image-Guided Procedures, Robotic Interventions, and Modeling
• Publisher: Society of Photo-optical Instrumentation Engineers
• Conference Place: San Diego, CA
• Volume/Issue: Volume 9036
• Publication Date: 2014-01-01
• Type of Work: Conference | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1117/12.2043805
• Subjects: Engineering, Biomedical; Health Sciences, Radiology; Health Sciences, Oncology
• Keywords: Science & Technology; Technology; Physical Sciences; Life Sciences & Biomedicine; Engineering, Biomedical; Robotics; Optics; Radiology, Nuclear Medicine & Medical Imaging; Engineering; Medical hyperspectral imaging; head and neck cancer; surgical margin delineation; affine registration; B-spline free form deformation; NONRIGID REGISTRATION; HEAD; CLASSIFICATION; SEGMENTATION; DIAGNOSIS; PROSTATE; SURGERY

Abstract

The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.