Dataset Information

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry.

ABSTRACT: OBJECTIVES: To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry. METHODS: The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth B-spline deformation. Subsequently, the deformed images were repeatedly registered with the original images using different BKSs. The quality of the DIR was expressed as the mean residual displacement (MRD) between the known imposed deformation and the result of the B-spline DIR.Finally, for three patients, head-and-neck planning CT scans were deformed with a realistic deformation field derived from a rescan CT of the same patient, resulting in a simulated deformed image and an a-priori known deformation field. Hence, a B-spline DIR was performed between the simulated image and the planning CT at different BKSs. Similar to the phantom cases, the DIR accuracy was evaluated by means of MRD. RESULTS: In total, 162 phantom registrations were performed with varying CNR and BKSs. MRD-values < 1.0 mm were observed with a BKS between 10-20 mm for image contrast ? ± 250 HU and noise < ± 200 HU. Decreasing the image feature content resulted in increased MRD-values at all BKSs. Using BKS = 15 mm for the three clinical cases resulted in an average MRD < 1.0 mm. CONCLUSIONS: For synthetically generated phantoms and three real CT cases the highest DIR accuracy was obtained for a BKS between 10-20 mm. The accuracy decreased with decreasing image feature content, decreasing image contrast, and higher noise levels. Our results indicate that DIR accuracy in clinical CT images (typical noise levels < ± 100 HU) will not be effected by the amount of image noise.

SUBMITTER: Brouwer CL

PROVIDER: S-EPMC4128373 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry.

Brouwer Charlotte L CL Kierkels Roel G J RG van 't Veld Aart A AA Sijtsema Nanna M NM Meertens Harm H

Radiation oncology (London, England) 20140729

<h4>Objectives</h4>To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry.<h4>Methods</h4>The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth ...[more]

PMID: 25074293

Similar Datasets

Project description:PURPOSE:Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images. ACQUISITION AND VALIDATION METHODS:Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model. DATA FORMAT AND USAGE NOTES:The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR. POTENTIAL APPLICATIONS:The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.

Project description: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 phan-toms 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.

Dataset Information

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry.

Publications

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets