O.258 3D control of resection margins in oral cancer based on PET/CT image-fusion

Feichtinger, Matthias; Zemann, Wolfgang; Aigner, RM; Kaercher, H.

doi:10.1016/s1010-5182(08)71382-9

Cited by 2 publications

(2 citation statements)

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“…Similar to CT and MRI, a major application of PET is in radiology studies for brain diagnosis and treatment [47,135,48,208,140,143]. There are a wide range of the application of image fusion using PET, some of which are for cancer treatments [51,223,220,162,222,209,225,229,131], image segmentation and integration [51,259], 3D tumor simulation [191], gynecological cancer diagnosis [190], inertial electrostatic confinement fusion [270], gross tumor volume detection [200,271], diagnosis of local recurrence of rectal cancer [214], tumor detection and treatment [246,220], pediatric solid extracranial tumors [251], telemedicine [201], breast cancer detection [184,252,253], oral cancer treatment [254], lung cancer diagnosis [256,272], cervical cancer treatment [202], esophageal cancer diagnosis [260], and pancreatic tumors characterization [207,261].…”

Section: Positron Emission Tomographymentioning

confidence: 99%

“…There is increased interest in using fusion techniques to improve the imaging quality. The use of PET data in combination with some of the existing modalities using the image fusion techniques include MRI-CT-PET-SPECT-DSA-MEG [47,135], MRI-CT-PET [51,191,200,201,202], MRI-SPECT-PET [48], MRI/CT-PET-SPECT [190], MRI-PET [207,208,140,184,209,143,130,131], FDG-PET-CT [229,251,253], PET-CT [273,248,214,246,220,221,239,29,223,68,254,256,252,222,76,274,261], FDG-PET [272], and PET-CT-ultrasound [260].…”

Section: Positron Emission Tomographymentioning

confidence: 99%

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Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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