Deformable Image Registration Uncertainty Quantification Using Deep Learning for Dose Accumulation in Adaptive Proton Therapy

Smolders, A.; Lomax, Tony; Weber, D.C.; Albertini, Francesca

doi:10.1007/978-3-031-11203-4_7

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Minimizing the KL divergence with the above assumptions yields a loss function (Dalca et al 2019, Smolders et al 2022a:…”

Section: Unsupervised Learningmentioning

confidence: 99%

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Smolders,

Lomax,

Weber

et al. 2023

Phys. Med. Biol.

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Objective: Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which are the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach: Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results: The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods an expected calibration error of less than 3%. Additionally, the probabilisticly accumulated dose volume histograms (DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining. Significance: Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.

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“…Minimizing the KL divergence with the above assumptions yields a loss function (Dalca et al 2019, Smolders et al 2022a:…”

Section: Unsupervised Learningmentioning

confidence: 99%

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Smolders,

Lomax,

Weber

et al. 2023

Phys. Med. Biol.

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“…Further to its implementation as a DVF generator for the registration process, DL can also be used for the quantification or prediction of uncertainties in DIR (Smolders et al 2022b(Smolders et al , 2022a(Smolders et al , 2023a. DSM that are not used in the optimization of output DVFs, provide further uncertainty quantification metrics that can be used to determine the quality of the overall registration and highlight regions of poor accuracy (Galib et al 2020).…”

Section: Ai/dl-based Uncertainty Quantificationmentioning

confidence: 99%

“…The inter-algorithm variability was proposed to be used for geometric as well as dosimetric DIR uncertainties (Nenoff et al 2020 , Amstutz et al 2021b ). Probabilistic unsupervised DL methods have also been proposed to predict the variance of DVFs in interfraction datasets (Gong et al 2022 , Smolders et al 2022b , 2022a ).…”

Section: Application-specific Dir Uncertaintymentioning

confidence: 99%

“…The simplest method is using the worst-case or average difference distance in all directions for all voxels of a given region or structure. More individualised methods have been investigated (Amstutz et al 2021b , Smolders et al 2022b , 2023c ) and provide patient specific voxel-wise uncertainties maps. We recommend using such voxel-wise uncertainty maps whenever possible.…”

Section: Uncertainty Tolerances Of Dir-facilitated Dosimetric Proceduresmentioning

confidence: 99%

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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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PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration

Siegert,

Fischer,

Heinrich

et al. 2024

Lecture Notes in Computer Science

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Deformable Image Registration Uncertainty Quantification Using Deep Learning for Dose Accumulation in Adaptive Proton Therapy

Cited by 4 publications

References 23 publications

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration

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