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TH‐C‐BRF‐01: The Promise and Potential Pitfalls of Deformable Image Registration in Clinical Practice
Author(s) -
Brock K,
Oldham M,
Pouliot J,
Cai J
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4889611
Subject(s) - image registration , computer science , medical physics , clinical practice , medical imaging , artificial intelligence , machine learning , image (mathematics) , medicine , family medicine
Accurate and robust deformable image registration (DIR) is a key enabling technique in the clinical realization of two approaches for advancing radiation therapy treatment efficacy: adaptive radiation therapy and treatment response assessment. Currently there are a wide variety of DIR methods including the categories of splines, optical/diffusion, free‐form, and biomechanical algorithms. All methods aim to translate information between image sets (including multi‐modal data) in the presence of spatial deformation of tissues. However, recent research has shown that different DIR algorithms can yield substantially different results for the same reference deformation, and that DIR performance can be site and application dependent. As a result, errors can occur, and subsequent patient treatment can be compromised. There is a clear need for greater understanding of appropriate use of DIR techniques, as well as effective methods of validation, evaluation, and improvement. In this session, we will review the state‐of‐the‐art concerning DIR development, clinical application, and performance evaluation. Novel DIR methods and evaluating technologies will be reviewed. Learning Objectives: 1. To understand the underlying principles and physics of current DIR techniques 2. To explore potential clinical applications and areas of high impact for DIR 3. To investigate sources of uncertainty, appropriate usage, and methods for validating and evaluating DIR performance.