Visualization of Deformable Image Registration Quality Using Local Image Dissimilarity

Schlachter, Matthias; Fechter, Tobias; Jurisic, Miro; Schimek-Jasch, Tanja; Oehlke, Oliver; Adebahr, S.; Birkfellner, Wolfgang; Nestle, Ursula; Bühler, Katja

doi:10.1109/tmi.2016.2560942

Cited by 21 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they require different registration algorithms, which are not often available in a clinical environment, and their selection could be affected by a systematic failure of the registration. Various DIR types and especially the class of biophysical and finite element modeling‐based registration approaches should be in fact included in the comparison, since they could outperform purely intensity‐based DIR, as shown for liver registration …”

Section: The Geometric Accuracy Paradigmmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: The Geometric Accuracy Paradigmmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In the study of Hamdan et al [HBR*17] checkerboard visualizations are used to verify the alignment of the registration of MRI and CT images for prostate images together with contours. Visualization of DIR quality using local image dissimilarity has been proposed by Schlachter et al [SFJ*16], where the verification is based on voxel‐wise calculated dissimilarity value to indicate the match or mismatch. Furthermore, it includes different interaction and visualization features for exploration of candidate regions to simplify the process of visual assessment.…”

Section: Taxonomy and Presentation Of Previous Work In Vc For Rtmentioning

confidence: 99%

“…Also, each step of the RT workflow involves heterogeneous sources of information. These might relate, for example, to multi‐modal registration [SFJ*16] and segmentation data [RMB*16], to ensemble data from the optimization phase of the dose planning [SRV16], or to modeling data from tumor control probability (TCP) [RCM*16] and normal tissue complication probability (NTCP) [RBGR18, RCA*18]. Understanding, exploring and analyzing all these data channels can be a demanding and time‐consuming task.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, for treatment methods which involve critically high doses, such as Stereotactic Body RT (SBRT) for lung cancer, even slight movements cannot be ignored. 4D imaging and deformable registration can be used to model breathing motion [WSVHD08], but these introduce uncertainty [SFJ*16]. Uncertainties are also present in segmentation [RBGR18, RMB*16] and (radiobiological) modeling [RCA*18, RCM*16], to name a few.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

Schlachter

Raidou

Muren

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process—from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.

show abstract

“…In the context of multimodal imaging, the relevance of deformable registration is limited as these methods will most likely stay in the domain of pure algorithmic applications from a current perspective. Still it is emerging as a powerful tool in image-guided and adaptive radiotherapy for compensation of organ motion and handling of anatomical changes in the course of treatment [101][102][103][104][105][106][107][108].…”

Section: Deformable Registrationmentioning

confidence: 99%