INVITED REVIEW—IMAGE REGISTRATION IN VETERINARY RADIATION ONCOLOGY: INDICATIONS, IMPLICATIONS, AND FUTURE ADVANCES. Issue 2 (18th January 2016)

• Record Type: Journal Article
• Authors: Feng, Yang; Lawrence, Jessica; Cheng, Kun; Montgomery, Dean; Forrest, Lisa; Mclaren, Duncan B.; McLaughlin, Stephen; Argyle, David J.; Nailon, William H.
• Is Part Of: Veterinary radiology & ultrasound. Volume 57:Issue 2(2016)
• Page Start: 113
• Page End: 123
• Publication Date: 2016-01-18
• Subjects: computed tomography -- image registration -- magnetic resonance imaging -- oncology -- radiation therapy; Veterinary radiology -- Periodicals; Radiologie vétérinaire -- Périodiques; Échographie vétérinaire -- Périodiques; Radiography -- Periodicals; Ultrasonography -- Periodicals; Diagnosis -- Periodicals; Veterinary Medicine -- Periodicals; Societies, Medical -- Periodicals; 636.089607
• DOI: 10.1111/vru.12342
• Languages: English
• ISSNs: 1058-8183

Abstract

Abstract : The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image‐guided radiation therapy (IGRT), and intensity‐modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor‐bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms.