The utilization of consistency metrics for error analysis in deformable image registration

Bender, Edward T.; Tomé, Wolfgang A.

doi:10.1088/0031-9155/54/18/014

Cited by 60 publications

(59 citation statements)

References 19 publications

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“…All three quantitative checks have been used in literature as a possible DIRQA [Varadhan et al, 2013, Leow et al, 2007, Christensen and Johnson, 2001, Bender and Tomé, 2009. They all share the same flaw, however, that they are a necessary but not sufficient condition for a successful DIR.…”

Section: Summary and Discussionmentioning

confidence: 99%

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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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Section: Summary and Discussionmentioning

confidence: 99%

“…The inverse consistency error (ICE) is consistently used in literature as one of the main vector field checks [Christensen andJohnson, 2001, Bender andTomé, 2009]. The principle is as follows.…”

Section: Inverse Consistency Errormentioning

confidence: 99%

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

et al. 2016

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“…9,11,12 In addition, the concept of self or inverse consistency may allow the accuracy of deformation registration to be evaluated. [13][14][15][16] Computational phantoms offer another option to directly derive the displacement errors at each voxel. Built from tomographic images of real subjects, the phantom deformation can be simulated using different mathematical formulae [17][18][19][20][21] so that displacements can be compared in submillimeter anatomical detail.…”

Section: Introductionmentioning

confidence: 99%

Analysis of deformable image registration accuracy using computational modeling

2010

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Computer aided modeling of anatomic deformation, allowing various techniques and protocols in radiation therapy to be systematically verified and studied, has become increasingly attractive. In this study the potential issues in deformable image registration ͑DIR͒ were analyzed based on two numerical phantoms: One, a synthesized, low intensity gradient prostate image, and the other a lung patient's CT image data set. Each phantom was modeled with region-specific material parameters with its deformation solved using a finite element method. The resultant displacements were used to construct a benchmark to quantify the displacement errors of the Demons and B-Spline-based registrations. The results show that the accuracy of these registration algorithms depends on the chosen parameters, the selection of which is closely associated with the intensity gradients of the underlying images. For the Demons algorithm, both single resolution ͑SR͒ and multiresolution ͑MR͒ registrations required approximately 300 iterations to reach an accuracy of 1.4 mm mean error in the lung patient's CT image ͑and 0.7 mm mean error averaged in the lung only͒. For the low gradient prostate phantom, these algorithms ͑both SR and MR͒ required at least 1600 iterations to reduce their mean errors to 2 mm. For the B-Spline algorithms, best performance ͑mean errors of 1.9 mm for SR and 1.6 mm for MR, respectively͒ on the low gradient prostate was achieved using five grid nodes in each direction. Adding more grid nodes resulted in larger errors. For the lung patient's CT data set, the B-Spline registrations required ten grid nodes in each direction for highest accuracy ͑1.4 mm for SR and 1.5 mm for MR͒. The numbers of iterations or grid nodes required for optimal registrations depended on the intensity gradients of the underlying images. In summary, the performance of the Demons and B-Spline registrations have been quantitatively evaluated using numerical phantoms. The results show that parameter selection for optimal accuracy is closely related to the intensity gradients of the underlying images. Also, the result that the DIR algorithms produce much lower errors in heterogeneous lung regions relative to homogeneous ͑low intensity gradient͒ regions, suggests that feature-based evaluation of deformable image registration accuracy must be viewed cautiously.

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“…A further issue is represented by the lack of inverse consistency of the adopted B-spline based deformable registration algorithm (28). This did not allow us to implement the virtual 4D PET strategy as an intra-reconstruction optimization (29,30), similarly to state-of-the-art motion compensation in 4D PET (10).…”

Section: Resultsmentioning

confidence: 99%

Optimized PET Imaging for 4D Treatment Planning in Radiotherapy: the Virtual 4D PET Strategy

Gianoli

Riboldi

Fontana

et al. 2014

Technol Cancer Res Treat

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The purpose of the study is to evaluate the performance of a novel strategy, referred to as "virtual 4D PET", aiming at the optimization of hybrid 4D CT-PET scan for radiotherapy treatment planning. The virtual 4D PET strategy applies 4D CT motion modeling to avoid time-resolved PET image acquisition. This leads to a reduction of radioactive tracer administered to the patient and to a total acquisition time comparable to free-breathing PET studies.The proposed method exploits a motion model derived from 4D CT, which is applied to the free-breathing PET to recover respiratory motion and motion blur. The free-breathing PET is warped according to the motion model, in order to generate the virtual 4D PET. The virtual 4D PET strategy was tested on images obtained from a 4D computational anthropomorphic phantom. The performance was compared to conventional motion compensated 4D PET.Tests were also carried out on clinical 4D CT-PET scans coming from seven lung and liver cancer patients. The virtual 4D PET strategy was able to recover lesion motion, with comparable performance with respect to the motion compensated 4D PET. The compensation of the activity blurring due to motion was successfully achieved in terms of spill out removal.Specific limitations were highlighted in terms of partial volume compensation. Results on clinical 4D CT-PET scans confirmed the efficacy in 4D PET count statistics optimization, as equal to the free-breathing PET, and recovery of lesion motion. Compared to conventional motion compensation strategies that explicitly require 4D PET imaging, the virtual 4D PET strategy reduces clinical workload and computational costs, resulting in significant advantages for radiotherapy treatment planning.

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The utilization of consistency metrics for error analysis in deformable image registration

Cited by 60 publications

References 19 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

Analysis of deformable image registration accuracy using computational modeling

Optimized PET Imaging for 4D Treatment Planning in Radiotherapy: the Virtual 4D PET Strategy

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