Endoscopic detection of cancer with lensless radioluminescence imaging and machine vision

• Published in: Scientific reports 2016-08, Vol.6 (1), p.30737-30737, Article 30737
• Main Authors: Türkcan, Silvan, Naczynski, Dominik J., Nolley, Rosalie, Sasportas, Laura S., Peehl, Donna M., Pratx, Guillem
• Format: Article
• Language: English
• Subjects: 13; 13/106; 59/5; 631/67/2321; 692/4028/67/2321; Benign; Beta radiation; Cameras; Cancer; Humanities and Social Sciences; Information processing; Kidney cancer; Kidneys; multidisciplinary; Photons; Probes; Radioactive tracers; Science; Tissues; Tumors; Vision systems; Wounds
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep30737

Complete removal of residual tumor tissue during surgical resection improves patient outcomes. However, it is often difficult for surgeons to delineate the tumor beyond its visible boundary. This has led to the development of intraoperative detectors that can image radiotracers accumulated within tumors, thus facilitating the removal of residual tumor tissue during surgical procedures. We introduce a beta imaging system that converts the beta radiation from the radiotracer into photons close to the decay origin through a CdWO 4 scintillator and does not use any optical elements. The signal is relayed onto an EMCCD chip through a wound imaging fiber. The sensitivity of the device allows imaging of activity down to 100 nCi and the system has a resolution of at least 500 μm with a field of view of 4.80 × 6.51 mm. Advances in handheld beta cameras have focused on hardware improvements, but we apply machine vision to the recorded images to extract more information. We automatically classify sample regions in human renal cancer tissue ex-vivo into tumor or benign tissue based on image features. Machine vision boosts the ability of our system to distinguish tumor from healthy tissue by a factor of 9 ± 3 and can be applied to other beta imaging probes.