Development of a dynamic deformable thorax phantom for the quality management of deformable image registration

The purpose of this study was to develop a novel dynamic deformable thorax phantom for deformable image registration (DIR) quality assurance (QA) and to verify as a tool for commissioning and DIR QA.The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180 mm. The inner phantom consists of deformable, 20 mm thick disk-shaped sponges. To evaluate the physical characteristics of the phantom, we evaluated its image quality and deformation. DIR accuracies were evaluated using the three types of commercially DIR software (MIM, RayStation, and Velocity AI) to test the feasibility of this phantom. We used different DIR parameters to test the impact of parameters on DIR accuracy in various phantom settings. To evaluate DIR accuracy, a target registration error (TRE) was calculated using the anatomical landmark points.The three locations (i.e., distal, middle, and proximal positions) had different displacement amounts. This result indicated that the inner phantom was not moved but deformed. In cases with different phantom settings and marker settings, the ranges of the average TRE were 0.63–15.60 mm (MIM). In cases with different DIR parameters settings, the ranges of the average TRE were as follows: 0.73–7.10 mm (MIM), 8.25–8.66 mm (RayStation), and 8.26–8.43 mm (Velocity). These results suggest that our phantom could evaluate the detailed DIR behaviors with TRE. Therefore, this is indicative of the potential usefulness of our phantom in DIR commissioning and QA.     

Impact of variation in cancer registration practice on observed international cancer survival differences between International Cancer Benchmarking Partnership (ICBP) jurisdictions

BackgroundInternational cancer survival comparisons use cancer registration data to report cancer survival, which informs the development of cancer policy and practice. Studies like the International Cancer Benchmarking Partnership (ICBP) have a duty to understand how registration differences impact on survival prior to drawing conclusions.MethodsKey informants reported differences in registration practice for capturing incidence date, death certificate case handling and registration of multiple primary tumours. Sensitivity analyses estimated their impact on one-year survival using baseline and supplementary cancer registration data from England and Sweden.ResultsVariations in registration practice accounted for up to a 7.3 percentage point difference between unadjusted (estimates from previous ICBP survival data) and adjusted (estimates recalculated accounting for registration differences) one-year survival, depending on tumour site and jurisdiction.One-year survival estimates for four jurisdictions were affected by adjustment: New South Wales, Norway, Ontario, Sweden. Sweden and Ontario’s survival reduced after adjustment, yet they remained the jurisdictions with the highest survival for breast and ovarian cancer respectively. Sweden had the highest unadjusted lung cancer survival of 43.6% which was adjusted to 39.0% leaving Victoria and Manitoba with the highest estimate at 42.7%. For colorectal cancer, Victoria’s highest survival of 85.1% remained unchanged after adjustment.ConclusionPopulation-based cancer survival comparisons can be subject to registration biases that may impact the reported ‘survival gap’ between populations. Efforts should be made to apply consistent registration practices internationally. In the meantime, survival comparison studies should provide acknowledgement of or adjustment for the registration biases that may affect their conclusions.     

A validation framework to assess performance of commercial deformable image registration in lung radiotherapy

IntroductionDeformable image registration (DIR) can play an important role in the context of adaptive radiotherapy. The AAPM Task Group 132 (TG-132) has described several quantitative measures for DIR error assessment but they can only be accurately defined when there is a ground-truth present in high-contrast regions. This work aims to set out a framework to obtain optimal results for CT-CT lung DIR in clinical setting for a commercially available system by quantifying the DIR performance in both low- and high-contrast regions.MethodsFive publicly available thorax datasets were used to assess the DIR quality. A “Ghost fiducial” method was implemented by windowing the contrast in a new feature provided by Varian Velocity v4.1. Target registration error (TRE) of the landmarks and Dice-similarity coefficient of the tumour were calculated at three different contrast settings to assess the algorithm in high- and low-contrast scenarios.ResultsFor the original unedited dataset, higher resolution DIR methods showed best performance acceptable within the recommended limit according to TG-132, when actual displacements were less than 10 mm. The relation of the actual displacement of the landmarks and TRE shows the limited capacity of the algorithm to deal with movements larger than 10 mm.ConclusionThis work found the performance of DIR methods and settings available in Varian Velocity v4.1 to be a function of contrast level as well as extent of motion. This highlights the need for multiple metrics to assess different aspects of DIR performance for various applications related to low-contrast and/or high-contrast regions.     

Image-guided surgery: From X-rays to Virtual Reality

Since the discovery of X-rays, medical imaging has played a major role in the guidance of surgical procedures. While medical imaging began with simple X-ray plates to indicate the presence of foreign objects within the human body, the advent of the computer has been a major factor in the recent development of this field. Imaging techniques have grown greatly in their sophistication and can now provide the surgeon with high quality three-dimensional images depicting not only the normal anatomy and pathology, but also vascularity and function. One key factor in the advances in Image-Guided Surgery (IGS) is the ability not only to register images derived from the various imaging modalities amongst themselves, but also to register them to the patient. The other crucial aspect of IGS is the ability to track instruments in real time during the procedure, and to portray them as part of a realistic model of the operative volume. Stereoscopic and virtual-reality techniques can usefully enhance the visualization process. IGS nevertheless relies heavily on the assumption that the images acquired prior to surgery, and upon which the surgical guidance is based, accurately represent the morphology of the tissue during the surgical procedure. In many instances this assumption is invalid, and intra-operative real-time imaging, using interventional MRI, Ultrasound, and electrophysiological recordings are often employed to overcome this limitation. Although now in extensive clinical use, IGS is often currently perceived as an intrusion into the operating room. It must evolve towards becoming a routine surgical tool, but this will only happen if natural and intuitive human interfaces are developed for these systems.     

Hierarchical Rigid Registration of Femur Surface Model Based on Anatomical Features

Existing model registration of individual bones does not have a high certainly of success due to the lack of anatomic semantic. In light of the surface anatomy and functional structure of bones, we hypothesized individual femur models would be aligned through feature points both in geometrical level and in anatomic level, and proposed a hierarchical approach for the rigid registration (HRR) of point cloud models of femur with high resolution. Firstly, a coarse registration between two simplified point cloud models was implemented based on the extraction of geometric feature points (GFPs); and then, according to the anatomic feature points (AFPs) in two level namely shape features and structure features, the fine weight-based registration was performed to achieve anatomical alignment; finally, the origin source model was automatically transformed by applying the obtained coarse matrix and fine one in sequence. Experimental results show that the hierarchical registration method can rapidly and accurately register point clouds of individual femurs, and achieves the medical semantic alignment, and provides a basic tool for the understanding and comparison of femur anatomy and structure.     

Commissioning and validation of commercial deformable image registration software for adaptive contouring

PurposeTo report the commissioning and validation of deformable image registration(DIR) software for adaptive contouring.MethodsDIR (SmartAdapt®v13.6) was validated using two methods namely contour propagation accuracy and landmark tracking, using physical phantoms and clinical images of various disease sites. Five in-house made phantoms with various known deformations and a set of 10 virtual phantoms were used. Displacement in lateral, anterio-posterior (AP) and superior-inferior (SI) direction were evaluated for various organs and compared with the ground truth. Four clinical sites namely, brain (n = 5), HN (n = 9), cervix (n = 18) and prostate (n = 23) were used. Organs were manually delineated by a radiation oncologist, compared with the deformable image registration (DIR) generated contours. 3D slicer v4.5.0.1 was used to analyze Dice Similarity Co-efficient (DSC), shift in centre of mass (COM) and Hausdorff distances Hf_95%/avg.ResultsMean (SD) DSC, Hf_95% (mm), Hf_avg (mm) and COM of all the phantoms 1–5 were 0.84 (0.2) mm, 5.1 (7.4) mm, 1.6 (2.2) mm, and 1.6 (0.2) mm respectively. Phantom-5 had the largest deformation as compared to phantoms 1–4, and hence had suboptimal indices. The virtual phantom resulted in consistent results for all the ROIs investigated. Contours propagated for brain patients were better with a high DSC score (0.91 (0.04)) as compared to other sites (HN: 0.84, prostate: 0.81 and cervix 0.77). A similar trend was seen in other indices too. The accuracy of propagated contours is limited for complex deformations that include large volume and shape change of bladder and rectum respectively. Visual validation of the propagated contours is recommended for clinical implementation.ConclusionThe DIR algorithm was commissioned and validated for adaptive contouring.     

Tomotherapy archive structure and new software tool for loading and advanced analysis of data contained in it

Aim

The main objective of the study was to analyze the structure of data contained in the archives exported from a tomotherapy treatment planning system. An additional aim was to create an application equipped with a user-friendly interface to enable automatic reading of files and data analysis, also using external algorithms. Analyses had to include image registration, dose deformation and summation.

Materials and methods

Files from the archive exported from the tomotherapy treatment planning system (TPS) were analyzed. Two programs were used to analyze the information contained in the archive files: XML Viewer by MindFusion Limited and HxD hex editor by Maël Hora. To create an application enabling loading and analyzing the data, Matlab by MathWorks, version R2009b, was used.

Results

Archive exported from the TPS is a directory with several files. It contains three types of data: .xml, .img and .sin. Tools available in Matlab offer great opportunities for analysis and transformation of loaded information. Proposed application automates the loading of necessary information and simplifies data handling. Furthermore, the application is equipped with a graphical user interface (GUI). The main application window contains buttons for opening the archives and analyzing the loaded data.

Conclusion

The analysis of data contained in the archive exported from the tomotherapy treatment planning system allowed to determine the way and place of saving information of our interest, such as tomography images, structure sets and dose distributions. This enabled us to develop and optimize methods of loading and analyzing this information.     

Medical image registration using fuzzy theory

Mutual information (MI)-based registration, which uses MI as the similarity measure, is a representative method in medical image registration. It has an excellent robustness and accuracy, but with the disadvantages of a large amount of calculation and a long processing time. In this paper, by computing the medical image moments, the centroid is acquired. By applying fuzzy c-means clustering, the coordinates of the medical image are divided into two clusters to fit a straight line, and the rotation angles of the reference and floating images are computed, respectively. Thereby, the initial values for registering the images are determined. When searching the optimal geometric transformation parameters, we put forward the two new concepts of fuzzy distance and fuzzy signal-to-noise ratio (FSNR), and we select FSNR as the similarity measure between the reference and floating images. In the experiments, the Simplex method is chosen as multi-parameter optimisation. The experimental results show that this proposed method has a simple implementation, a low computational cost, a fast registration and good registration accuracy. Moreover, it can effectively avoid trapping into the local optima. It is adapted to both mono-modality and multi-modality image registrations.