Voxel-based analysis: Roadmap for clinical translation

McWilliam, Alan; Palma, Giuseppe; Abravan, Azadeh; Acosta, Oscar; Appelt, Ane; Aznar, Marianne; Monti, Serena; Onjukka, Eva; Panettieri, Vanessa; Placidi, Lorenzo; Rancati, Tiziana; Vasquez Osorio, Eliana; Witte, Marnix; Cella, Laura

doi:10.1016/j.radonc.2023.109868

Cited by 3 publications

(1 citation statement)

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“…Several measures to evaluate DIR uncertainty for voxel-based analysis have been proposed (Palma et al 2020 , Shortall et al 2021 , McWilliam et al 2023 ). Quantified DIR uncertainties are often incorporated in the analysis by treating them as random errors and blurring the mapped dose distributions (McWilliam et al 2017 , Beasley et al 2018 , Green et al 2020 , Vasquez Osorio et al 2023a ).…”

Section: Application-specific Dir Uncertaintymentioning

confidence: 99%

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Nenoff,

Amstutz,

Murr

et al. 2023

Phys. Med. Biol.

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Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

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