A virtual phantom library for the quantification of deformable image registration uncertainties in patients with cancers of the head and neck

“Reg Refine” is a tool available in the MIM Maestro v6.4.5 platform (www.mimsoftware.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground‐truth models, the Pixel‐based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck. Image noise matched in both magnitude and texture to clinical CBCT scans was also added to each model to simulate the use case of CBCT–CT alignment. For lung, the results showed Reg Refine effective at improving registration accuracy when controlled by an expert user within the context of large lung deformation. CBCT noise was also shown to have no effect on DIR performance while using the MIM algorithm for this site. For head and neck, the results showed CBCT noise to have a large effect on the accuracy of registration, specifically for low‐contrast structures such as the brainstem and parotid glands. In these cases, the Reg Refine tool was able to improve the registration accuracy when controlled by an expert user. Several strategies for how to achieve these results have been outlined to assist other users and provide feedback for developers of similar tools.PACS number(s): 87.44.Qr, 87.57.nj, 87.57.c

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the tool “Reg Refine” for user‐guided deformable image registration

Johnson

Padgett

Chen

et al. 2016

“…These phantoms were previously created based on volumetric imaging of 10 head and neck cancer patients (8) . Briefly, each patient received a start‐of‐treatment CT (SOT) and a near end‐of‐treatment CT (EOT).…”

Section: Methodsmentioning

confidence: 99%

“…Examples include the extended cardiac–torso (XCAT) phantom, (3) the point‐validated pixel‐based breathing thorax model (POPI model), (4) the DIR‐Lab Thoracic 4D CT model, 5 , 6 and those provided as part of the EMPIRE 10 challenge (7) . In an effort to improve the correlation of computer‐based phantoms with the actual anatomical changes seen over a course of radiation therapy, the current authors previously developed synthetic datasets derived from clinical images of real head and neck patients (8) . These phantoms are publicly available for users to download as part of the Deformable Image Registration Evaluation Project (DIREP) (http://sites.google.com/site/dirphantoms).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

Pukala

Johnson

Shah

et al. 2016

Self Cite

Benchmarking is a process in which standardized tests are used to assess system performance. The data produced in the process are important for comparative purposes, particularly when considering the implementation and quality assurance of DIR algorithms. In this work, five commercial DIR algorithms (MIM, Velocity, RayStation, Pinnacle, and Eclipse) were benchmarked using a set of 10 virtual phantoms. The phantoms were previously developed based on CT data collected from real head and neck patients. Each phantom includes a start of treatment CT dataset, an end of treatment CT dataset, and the ground‐truth deformation vector field (DVF) which links them together. These virtual phantoms were imported into the commercial systems and registered through a deformable process. The resulting DVFs were compared to the ground‐truth DVF to determine the target registration error (TRE) at every voxel within the image set. Real treatment plans were also recalculated on each end of treatment CT dataset and the dose transferred according to both the ground‐truth and test DVFs. Dosimetric changes were assessed, and TRE was correlated with changes in the DVH of individual structures. In the first part of the study, results show mean TRE on the order of 0.5 mm to 3 mm for all phantoms and ROIs. In certain instances, however, misregistrations were encountered which produced mean and max errors up to 6.8 mm and 22 mm, respectively. In the second part of the study, dosimetric error was found to be strongly correlated with TRE in the brainstem, but weakly correlated with TRE in the spinal cord. Several interesting cases were assessed which highlight the interplay between the direction and magnitude of TRE and the dose distribution, including the slope of dosimetric gradients and the distance to critical structures. This information can be used to help clinicians better implement and test their algorithms, and also understand the strengths and weaknesses of a dose adaptive approach.PACS number(s): 87.57.nj, 87.55.dk, 87.55.Qr

Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis

Padgett

Stoyanova

Pirozzi

et al. 2018

PurposeValidating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic‐MRI and radiation treatment planning‐CT by utilizing a planning‐MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain.MethodsFor thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning (diagnostic‐MRI) and the second on the same day as the planning‐CT (planning‐MRI). The diagnostic‐MRI was deformed to the planning‐CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning‐MRI provided an independent surrogate for the planning‐CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas.ResultsThe planning‐MRI provided an excellent surrogate for the planning‐CT with residual error in fiducial alignment between the two datasets being submillimeter, 0.78 mm. DIR was superior to the rigid registration in 11 of 13 cases demonstrating a 27.37% improvement in NMI (P < 0.009) within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013).ConclusionBy utilizing the planning‐MRI as a surrogate for the planning‐CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging.