Benchmarking of fluorescence cameras through the use of a composite phantom

Abstract. Fluorescence molecular imaging (FMI) has shown potential to detect and delineate cancer during surgery or diagnostic endoscopy. Recent progress on imaging systems has allowed sensitive detection of fluorescent agents even in video rate mode. However, lack of standardization in fluorescence imaging challenges the clinical application of FMI, since the use of different systems may lead to different results from a given study, even when using the same fluorescent agent. In this work, we investigate the use of a composite fluorescence phantom, employed as an FMI standard, to offer a comprehensive method for validation and standardization of the performance of different imaging systems. To exclude user interaction, all phantom features are automatically extracted from the acquired epi-illumination color and fluorescence images, using appropriately constructed templates. These features are then employed to characterize the performance and compare different cameras to each other. The proposed method could serve as a framework toward the calibration and benchmarking of FMI systems, to facilitate their clinical translation.

[1]  Jeffrey K. Mito,et al.  A mouse-human phase 1 co-clinical trial of a protease-activated fluorescent probe for imaging cancer , 2016, Science Translational Medicine.

[2]  P. Low,et al.  Intraoperative tumor-specific fluorescence imaging in ovarian cancer by folate receptor-α targeting: first in-human results , 2011, Nature Medicine.

[3]  Eva M. Sevick-Muraca,et al.  Near-Infrared Fluorescence Imaging in Humans with Indocyanine Green: A Review and Update~!2009-12-07~!2009-12-23~!2010-05-26~! , 2010 .

[4]  G. C. Langhout,et al.  Near-infrared fluorescence (NIRF) imaging in breast-conserving surgery: assessing intraoperative techniques in tissue-simulating breast phantoms. , 2011, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[5]  G. Dionigi,et al.  Surgical margins in breast conservation. , 2013, International journal of surgery.

[6]  E M Sevick-Muraca,et al.  Validating the Sensitivity and Performance of Near-Infrared Fluorescence Imaging and Tomography Devices Using a Novel Solid Phantom and Measurement Approach , 2012, Technology in cancer research & treatment.

[7]  Tyler R. McClintock,et al.  Near-Infrared Fluorescence Imaging with Intraoperative Administration of Indocyanine Green for Robotic Partial Nephrectomy , 2015, Current Urology Reports.

[8]  Despoina Daskalaki,et al.  Fluorescence in robotic surgery , 2015, Journal of surgical oncology.

[9]  E. Peli Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[10]  L. Newman,et al.  Predictors of Re-excision among Women Undergoing Breast-Conserving Surgery for Cancer , 2008, Annals of Surgical Oncology.

[11]  N. Usui,et al.  Clinical application of indocyanine green (ICG) fluorescent imaging of hepatoblastoma. , 2015, Journal of pediatric surgery.

[12]  I-Chih Tan,et al.  Near-Infrared Fluorescence Imaging in Humans with Indocyanine Green: A Review and Update. , 2010, Open surgical oncology journal.

[13]  Abe Fingerhut,et al.  Clinical applications of indocyanine green (ICG) enhanced fluorescence in laparoscopic surgery , 2014, Surgical Endoscopy.

[14]  Omar Touhami,et al.  Sentinel node mapping with indocyanine green and endoscopic near-infrared fluorescence imaging in endometrial cancer. A pilot study and review of the literature. , 2015, Gynecologic oncology.

[15]  Rainer Siebert,et al.  Optical phantoms with variable properties and geometries for diffuse and fluorescence optical spectroscopy , 2012, Journal of biomedical optics.

[16]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[17]  R. Simmons,et al.  Factors Associated With Residual Breast Cancer After Re-excision for Close or Positive Margins , 2004, Annals of Surgical Oncology.

[18]  H. Feigelson,et al.  Variability in reexcision following breast conservation surgery. , 2012, JAMA.

[19]  Vasilis Ntziachristos,et al.  Comprehensive phantom for interventional fluorescence molecular imaging , 2016, Journal of biomedical optics.

[20]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[21]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[22]  Dirk Grosenick,et al.  Development of a handheld fluorescence imaging camera for intraoperative sentinel lymph node mapping , 2015, Journal of biomedical optics.

[23]  Banghe Zhu,et al.  A matter of collection and detection for intraoperative and noninvasive near-infrared fluorescence molecular imaging: to see or not to see? , 2014, Medical physics.

[24]  B. Pogue,et al.  Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry. , 2006, Journal of biomedical optics.

[25]  Vasilis Ntziachristos,et al.  Concurrent video-rate color and near-infrared fluorescence laparoscopy , 2013, Journal of biomedical optics.