Deformable image registration in radiation therapy

Oh, Seungjong; Kim, Siyong

doi:10.3857/roj.2017.00325

Cited by 124 publications

(91 citation statements)

References 40 publications

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“…The accumulation of dose distributions calculated on different volumetric data sets inevitably involves a registration of each data set to a reference set where the dose will be accumulated. And image registration is not an error free process . Although rigid registrations can typically be performed with rather good accuracy, there are few regions of a patient that are truly “rigid” (the upper cranium and everything above the upper jaw being perhaps exceptions).…”

Section: Small Is Beautifulmentioning

confidence: 99%

“…And image registration is not an error free process. 36,37 Although rigid registrations can typically be performed with rather good accuracy, there are few regions of a patient that are truly "rigid" (the upper cranium and everything above the upper jaw being perhaps exceptions). For most anatomical regions, daily changes in patient geometry are more likely to be deformable in nature, and thus dose accumulation must also be performed using deformable registration algorithms.…”

Section: C Reducing Margins (Predictions 23 and 24)mentioning

confidence: 99%

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What will the medical physics of proton therapy look like 10 yr from now? A personal view

Lomax

2018

Medical Physics

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Despite growing rapidly, proton therapy is still a relatively immature treatment modality, at least in comparison to conventional radiotherapy using photons. As such, in this article, the author gives a very personal view of some of the potential areas for development in medical physics for proton therapy in the next 10 yr by identifying six topics for detailed discussion. The first of these are all related to reducing various “parameters” of proton therapy treatments (size and cost, treatment times, penumbras, and margins), while the second group are related to providing more quantitative “knowledge” about the quality of the treatments we are delivering. In conclusion, there is much interesting and important work still to be done in many areas of medical physics related to proton therapy, of which, the topics selected here are just the tip of the iceberg.

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Section: Small Is Beautifulmentioning

confidence: 99%

Section: C Reducing Margins (Predictions 23 and 24)mentioning

confidence: 99%

What will the medical physics of proton therapy look like 10 yr from now? A personal view

Lomax

2018

Medical Physics

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“…Therefore, many innovative ideas have been proposed over the past few decades. Good overviews of the field can be found in [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking

Fechter

Baltas

2020

IEEE Trans. Med. Imaging

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Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present an one shot registration approach for periodic motion tracking in 3D and 4D datasets. When applied to 3D dataset the algorithm calculates the inverse of a registration vector field simultaneously. For registration we employed a U-Net combined with a coarse to fine approach and a differential spatial transformer module. The algorithm was thoroughly tested with multiple 4D and 3D datasets publicly available. The results show that the presented approach is able to track periodic motion and to yield a competitive registration accuracy. Possible applications are the use as a stand-alone algorithm for 3D and 4D motion tracking or in the beginning of studies until enough datasets for a separate training phase are available.

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“…In this scenario, modeling the transformation due to inter‐ and intra‐fractional motion via image registration is fundamental to map, overlap, and integrate the information coming from different images. In particular, Deformable Image Registration (DIR) plays a key role to account for nonrigid changes which typically occur during a radiotherapy treatment . Different algorithms have been proposed in the literature (e.g., intensity‐based approaches such B‐spline and demons, landmark‐based thin‐plate spline, or biophysical and finite element modeling‐based registration) and DIR has extensively been proposed to analyze target motion and monitor tumor changes, including 4D motion modeling, contour propagation, and treatment adaptation by means of the so‐called “virtual CT” .…”

Section: Introductionmentioning

confidence: 99%

“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”

et al. 2018

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Over the last few decades, deformable image registration (DIR) has gained popularity in image-guided radiation therapy for a number of applications, such as contour propagation, dose warping, and accumulation. Although this raises promising perspectives for the improvement of treatment outcomes and quality of radiotherapy clinical practice, the variety of proposed DIR algorithms, combined with the lack of an effective quantitative quality control metric of the registration, is slowing the transfer of DIR into the clinical routine. Recently, a task group (AAPM TG132) report was published outlining the essential aspects of DIR for image guidance in radiotherapy. However, an accurate and efficient patient-specific validation is not yet defined, and appropriate metrics should be identified to achieve the definition of both geometric and dosimetric accuracy. In this respect, the use of a dense set of anatomical landmarks, along with additional evaluations on contours or deformation field analysis, are likely to drive patient-specific DIR validation in clinical image-guided radiotherapy applications to account for geometric inaccuracies. Automatic and efficient strategies able to provide spatial information of DIR uncertainties and to evaluate monomodal and multimodal image registration, as well as to describe homogenous and un-contrasted regions are believed to represent the future direction in DIR validation. But especially in the case of DIR applications for dose mapping and accumulation, the need of accurate patient-specific validation is not only limited to the evaluation of geometric accuracy. In fact, the need to account for dosimetric inaccuracies due to DIR represents another important area in the field of adaptive treatments. Different approaches are currently being investigated to quantify the effect of DIR error on dose analysis, mainly relying on clinically relevant dose metrics, or on the study of deformation field properties for a voxel-by-voxel evaluation. However, novel research is required for the definition of dedicated and personalized measures capable to relate the geometric and dosimetric inaccuracies, thus bearing useful information for a safe use of DIR by clinical end users. In this paper we provide insights on DIR results evaluation on a patient-specific basis, facing the issues of both geometric and dosimetric paradigms. Challenges on DIR validation are overviewed and discussed, in order to push preliminary clinical guidelines forward on this fundamental topic and boost the implementation of more robust and reliable patient-specific evaluation metrics.

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Deformable image registration in radiation therapy

Cited by 124 publications

References 40 publications

What will the medical physics of proton therapy look like 10 yr from now? A personal view

What will the medical physics of proton therapy look like 10 yr from now? A personal view

One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking

“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”

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