Literature DB >> 30168155

"Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats".

Chiara Paganelli1, Giorgia Meschini1, Silvia Molinelli2, Marco Riboldi3, Guido Baroni1,2.   

Abstract

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.
© 2018 American Association of Physicists in Medicine.

Entities:  

Keywords:  DIR; DIR assessment; DIR in radiotherapy; DIR validation

Mesh:

Year:  2018        PMID: 30168155     DOI: 10.1002/mp.13162

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  16 in total

1.  Automatic large quantity landmark pairs detection in 4DCT lung images.

Authors:  Yabo Fu; Xue Wu; Allan M Thomas; Harold H Li; Deshan Yang
Journal:  Med Phys       Date:  2019-08-07       Impact factor: 4.071

Review 2.  Online daily adaptive proton therapy.

Authors:  Francesca Albertini; Michael Matter; Lena Nenoff; Ye Zhang; Antony Lomax
Journal:  Br J Radiol       Date:  2019-11-11       Impact factor: 3.039

3.  Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy.

Authors:  Guillaume Cazoulat; Brian M Anderson; Molly M McCulloch; Bastien Rigaud; Eugene J Koay; Kristy K Brock
Journal:  Med Phys       Date:  2021-08-25       Impact factor: 4.506

4.  Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools.

Authors:  Mohammad Hussein; Adeyemi Akintonde; Jamie McClelland; Richard Speight; Catharine H Clark
Journal:  Br J Radiol       Date:  2021-04-29       Impact factor: 3.039

5.  MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy.

Authors:  Nicholas Lowther; Rob Louwe; Johnson Yuen; Nicholas Hardcastle; Adam Yeo; Michael Jameson
Journal:  Phys Eng Sci Med       Date:  2022-05-06

6.  Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning.

Authors:  Matteo Rossi; Gabriele Belotti; Chiara Paganelli; Andrea Pella; Amelia Barcellini; Pietro Cerveri; Guido Baroni
Journal:  Med Phys       Date:  2021-10-26       Impact factor: 4.506

7.  Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers.

Authors:  Nalee Kim; Jee Suk Chang; Yong Bae Kim; Jin Sung Kim
Journal:  Radiat Oncol       Date:  2020-05-13       Impact factor: 3.481

8.  A study of the clinical, treatment planning and dosimetric feasibility of dose painting in external beam radiotherapy of prostate cancer.

Authors:  Steve W Blake; Alison Stapleton; Andrew Brown; Sian Curtis; Janice Ash-Miles; Emma Dennis; Susan Masson; Dawn Bowers; Serena Hilman
Journal:  Phys Imaging Radiat Oncol       Date:  2020-08-10

9.  A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer.

Authors:  Matteo Maspero; Antonetta C Houweling; Mark H F Savenije; Tristan C F van Heijst; Joost J C Verhoeff; Alexis N T J Kotte; Cornelis A T van den Berg
Journal:  Phys Imaging Radiat Oncol       Date:  2020-05-25

10.  Structure guided deformable image registration for treatment planning CT and post stereotactic body radiation therapy (SBRT) Primovist® (Gd-EOB-DTPA) enhanced MRI.

Authors:  Svetlana Kuznetsova; Petra Grendarova; Soumyajit Roy; Rishi Sinha; Kundan Thind; Nicolas Ploquin
Journal:  J Appl Clin Med Phys       Date:  2019-11-22       Impact factor: 2.102

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