Evaluation of a deformable registration algorithm for subsequent lung computed tomography imaging during radiochemotherapy

Stützer, Kristin; Haase, Robert; Lohaus, Fabian; Barczyk, Steffen; Exner, Florian; Löck, Steffen; Rühaak, Jan; Lassen-Schmidt, Bianca; Corr, Dörte; Richter, Christian

doi:10.1118/1.4960366

Cited by 10 publications

(8 citation statements)

References 51 publications

(90 reference statements)

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“…The presence of extensive tissue changes such as atelectasis resolution, pleural effusion, and radiation-induced damage is known to result in decreased registration accuracy or registration failure for current state-of-the-art algorithms 26–28 . Over 60% of the studies summarized in Table 2 report accuracy of registration between different respiratory phases of a 4DCT study.…”

Section: Discussionmentioning

confidence: 99%

“…Nielsen et al used lymphoma patients for which large tumors were absent from the lung volume 29 . Neither Cazoulat et al nor Stützer et al included patients with atelectasis, though the images used by Cazoulat did contain some tumor regression 16, 28 . The difference in difficulty from intra-fraction to longitudinal registration is illustrated by Stützer’s finding of an increase in mean registration error of their algorithm from 1.0 mm using the DIRlab data to 2.9 mm using longitudinal data 28 .…”

Section: Discussionmentioning

confidence: 99%

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CALIPER: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non‐small cell lung cancer

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

CALIPER: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non‐small cell lung cancer

et al. 2018

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“…In lung cancer and upper-abdominal malignancies, tumor volumes are manually delineated on each phase of the 4D CT [64]. By enabling GTV propagation between the 4D CT phases [65][66][67][68][69][70], DIR reduced the delineation time by a factor of 2 (from 40 min to 18 min) [65,66]. The propagated GTV delineations were similar to the manual delineations [71], with a reported DSC greater than 0.76, which is similar to the reported intra-physician variation score [65,67].…”

Section: Multimodal Image Fusion Morphological and Functionalmentioning

confidence: 99%

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

et al. 2019

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Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformationbased planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of reirradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

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“…The vector fields were assessed qualitatively with tools in RayStation and rated as sufficiently precise for the given purpose. It is highly important to evaluate the vector field quality for each deformable image registration to assess the uncertainties introduced in the dose calculation as highlighted in Ribeiro et al 13 Quantitative evaluation, however, is largely time-consuming and complicated (e.g., based on manually choosing landmarks in both registered datasets) 17 and currently not featured in RayStation.…”

Section: B 4d Dose Reconstruction Qualitymentioning

confidence: 99%

Log file‐based dose reconstruction and accumulation for 4D adaptive pencil beam scanned proton therapy in a clinical treatment planning system: Implementation and proof‐of‐concept

et al. 2019

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Background and purpose Motion‐induced uncertainties hamper the clinical implementation of pencil beam scanning proton therapy (PBS‐PT). Prospective pretreatment evaluations only provide multiscenario predictions without giving a clear conclusion for the actual treatment. Therefore, in this proof‐of‐concept study we present a methodology for a fraction‐wise retrospective four‐dimensional (4D) dose reconstruction and accumulation aiming at the evaluation of treatment quality during and after treatment. Material and methods We implemented an easy‐to‐use, script‐based 4D dose assessment of PBS‐PT for patients with moving tumors in a commercially available treatment planning system. This 4D dose accumulation uses treatment delivery log files and breathing pattern records of each fraction as well as weekly repeated 4D‐CT scans acquired during the treatment course. The approach was validated experimentally and was executed for an exemplary dataset of a lung cancer patient. Results The script‐based 4D dose reconstruction and accumulation was implemented successfully, requiring minimal user input and a reasonable processing time (around 10 min for a fraction dose assessment). An experimental validation using a dynamic CIRS thorax phantom confirmed the precision of the 4D dose reconstruction methodology. In a proof‐of‐concept study, the accumulation of 33 reconstructed fraction doses showed a linear increase of D98 values. Projected treatment course D98 values revealed a CTV underdosage after fraction 25. This loss of target coverage was confirmed in a dose volume histogram comparison of the nominal, the projected (after 16 fractions) and the accumulated (after 33 fractions) dose distribution. Conclusions The presented method allows for the assessment of the conformity between planned and delivered dose as the treatment course progresses. The implemented approach considers the influence of changing patient anatomy and variations in the breathing pattern. This facilitates treatment quality evaluation and supports decisions regarding plan adaptation. In a next step, this approach will be applied to a larger patient cohort to investigate its capability as 4D quality control and decision support tool for treatment adaptation.

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Evaluation of a deformable registration algorithm for subsequent lung computed tomography imaging during radiochemotherapy

Cited by 10 publications

References 51 publications

CALIPER: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non‐small cell lung cancer

CALIPER: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non‐small cell lung cancer

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Log file‐based dose reconstruction and accumulation for 4D adaptive pencil beam scanned proton therapy in a clinical treatment planning system: Implementation and proof‐of‐concept

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