Evaluation of deformable image registration and a motion model in CT images with limited features

Liu, F; Hu, Yu‐Chi; Zhang, Q; Kincaid, Russell; Goodman, Karyn A.; Mageras, G.

doi:10.1088/0031-9155/57/9/2539

Cited by 37 publications

(39 citation statements)

References 21 publications

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“…A method has been developed by Castillo et al [Castillo et al, 2009] to automatically identify landmark points in lung patients images. How-ever, visual based evaluations are of limited use in regions of uniform image intensity and by the number of the objects being tracked [Kashani et al, 2008, Liu et al, 2012.…”

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confidence: 99%

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

et al. 2016

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Stereotactic body image guided radiation therapy (SBRT) shows excellent results for the local control of early stage lung cancer. However, not all patients are eligible for SBRT, and advanced stage treatment is less successful and often associated with severe side effects. Scanned carbon ion therapy (PT) can deliver more conformal dose likely benefiting these patient groups.Therefore an in silico trial was conducted on early and advanced stage patients to identify potential advantages of PT. The patients were treated with SBRT at Champalimaud Center for the Unknown, Lisbon (Portugal). PT plans were simulated on 4DCTs, and rescanning was investigated for motion mitigation in 4D-dose calculations. A dedicated strategy for 4D intensity modulated particle therapy (IMPT) was developed and applied for advanced stage patients with multiple lesions. For clinically valid and reliable results the deformable image registrationnecessary for 4D-dose calculation -a quality assurance tool was developed and applied in the study.The results showed that target coverage was comparable in SBRT and PT, while PT delivered significantly lower doses to most critical structures, especially the heart, lungs, and esophagus.A highly complex case of advanced stage lung cancer could be treated in a single fraction of 24 Gy with PT, while SBRT could not deliver the full ablative dose treatment due to an excessive heart dose. The mean heart dose was reduced from 10 Gy to 0.8 Gy with PT for this specific patient.

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confidence: 99%

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

et al. 2016

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“…On the other hand, the binning artifacts in 4DMRI are much fewer than 4DCT (see more discussion below). Overall, the quality of DIR alignment in 4DMRI should be similar to that in 4DCT,46 if not better, as shown in Fig. 2A.…”

Section: Discussionmentioning

confidence: 73%

“…The uncertainty of the free‐form DIR algorithm was reported to be ~3.5 mm in 4DCT in the thoracic and upper abdomen 46. Given more visible landmarks and fewer artifacts in T2W 4DMRI image,23 it is expected that the DIR uncertainty should be reduced.…”

Section: Discussionmentioning

confidence: 99%

“…Two sets of DIR‐propagated and two sets of manual contours in all respiratory states were generated, compared, and analyzed using the method described below. The accuracy of the free‐form DIR was previously found to be ~3.5 mm using 4DCT of a deformable phantom 46. It is expected that the DIR accuracy for 4DMRI with better soft‐tissue contrast and low binning artifacts should be no worse than 4DCT.…”

Section: Methodsmentioning

confidence: 97%

“…A fast free‐form multi‐resolution DIR method45, 46 was employed to propagate the contour from the full‐exhalation phase to the other respiratory phases. An intensity‐based metric was used as the registration criterion to minimize an energy function that accounts for voxel intensity similarity and smoothness between two images:

E (u) = \int (I_{B} (() \overset{x}{true} + \overset{u}{true} ())) - I_{A} (\overset{false\to}{x}) {())}^{2} d \overset{false\to}{x} + λ {false\sum}_{i = 1}^{3} \int {| \nabla u_{i} |}^{2} d \overset{false\to}{x}

…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of automatic contour propagation in T2‐weighted 4DMRI for normal‐tissue motion assessment using internal organ‐at‐risk volume (IRV)

Zhang

Markova

García

et al. 2018

J Applied Clin Med Phys

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PurposeThe purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion.Methods and MaterialsT2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm.ResultsVisual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI.ConclusionAutomatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.

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“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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Evaluation of deformable image registration and a motion model in CT images with limited features

Cited by 37 publications

References 21 publications

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

Evaluation of automatic contour propagation in T2‐weighted 4DMRI for normal‐tissue motion assessment using internal organ‐at‐risk volume (IRV)

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

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