Inverse Consistency Research Articles

Evaluation of CT to CBCT non-linear dense anatomical block matching registration for prostate patients

Deformable image registration (DIR) is a rapidly developing discipline in the field of medical imaging that has found numerous applications in modern radiation therapy. To be used in the clinical environment, DIR requires an accurate and robust algorithm supported by the careful evaluation. The purpose of this study was to evaluate the performance of the non-linear Dense Anatomical Block Matching (DABM) algorithm for CT-CBCT image registration of prostate cancer patients. Pre-treatment CT (pCT) images of five prostate patients that underwent intensity modulated radiation therapy (IMRT) were selected for this work. Mid-treatment CBCT data sets acquired during radiotherapy course were used to help validate the algorithm performance and benchmark against other commonly used DIR algorithms. Rigid alignment was followed by the DIR of considered images. After registration, structures (PTV, GTV, Bladder and Rectum) delineated on the pCT were deformed using the obtained deformation vector fields (DVFs), then propagated to the CBCT images and compared to the analogous contours delineated on the CBCT by an experienced radiation oncologist. The accuracy of image registration was assessed by several quantitative metrics: Dice Similarity Coefficient (DSC), Hausdorff Distances (HD; average and 95th percentile), Center of the Mass Shift (COM) as well as by physician validation. The topology of the obtained deformation vector fields was analyzed by the Jacobian determinant. The accuracy of the inverted DFVs was investigated by the application of the Inverse Consistency Error (ICE). The performance of the DABM algorithm was quantitatively compared to Rigid, Affine and B-spline algorithms. Results indicate that for all the patients and anatomical structures considered here, both the accuracy and the consistency of the DABM algorithm are considerably better than the other evaluated registration methods. Generated DVFs have a well-preserved topology and small ICEs. Presented findings show that DABM is a promising alternative to the existing common strategies for CT-CBCT image registration and its application in the adaptive radiation therapy of the pelvic region.

Iterative inversion of deformation vector fields with feedback control.

Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four-dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. We introduce a framework of fixed-point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. The NTDC-adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis-based predictions. Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control. Adaptive control is necessary and highly effective in the presence of nonsmall NTDCs. The adaptive iterations or the spectral measures, or both, may potentially be incorporated into deformable image registration methods.

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r ⩾ 0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

A method to detect landmark pairs accurately between intra-patient volumetric medical images.

An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively. Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency. The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm. A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.

Representing the dosimetric impact of deformable image registration errors

Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis.Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from ‘dir-labs’. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU.The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error.The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.

A GPU-based symmetric non-rigid image registration method in human lung.

Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD. The GPU version demonstrated an average of 43 times speedup and ~5.2 times speedup over the single-threaded and 12-threaded CPU versions, respectively. Run times with the GPU were as fast as 2min. The symmetric method improved the inverse consistency, aiding the use of image registration in the QCT-based evaluation of the lung.

3D non-rigid image registration with inverse consistency constraint on elastodynamics wave equation

In this work, a new inverse consistent non-rigid image registration method is presented. The inter-subject deformations are modeled as elastic waves which tend to propagate over the image domain while recovering the nonlinear discrepancies between the two images. An inverse consistency constraint is introduced into the inertial force that is part of the elastodynamics wave equation which governs the underlying non-rigid deformations. The proposed registration method was analyzed over 3D MR brain scans both quantitatively and qualitatively. Normalized cross-correlation (NCC) was utilized to check the registration accuracy, and it revealed that the performance of proposed registration method with and without inverse consistency constraint is comparable in terms of NCC which increased by $$13\%$$ from its initial value. Moreover, Dice coefficient for segmented structures was computed and compared against state-of-the-art symmetric image normalization method, free form deformation and diffeomorphic demons registration methods. The extent to which the proposed registration scheme enforced inverse consistency was analyzed through inverse consistency error. The results showed that the inverse consistency error reduced by $$99\%$$ with the proposed inverse consistent registration method as compared to the inverse inconsistent counterpart.

A concept for holistic whole body MRI data analysis, Imiomics.

PurposeTo present and evaluate a whole-body image analysis concept, Imiomics (imaging–omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data.MethodsThe presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI.The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect.The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis.ResultsThe image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system.The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept.ConclusionsThe registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

Computing and restoring global inverse consistency in interactive constraint satisfaction

Some applications require the interactive resolution of a constraint problem by a human user. In such cases, it is highly desirable that the person who interactively solves the problem is not given the choice to select values that do not lead to solutions. We call this property global inverse consistency. Existing systems simulate this either by maintaining arc consistency after each assignment performed by the user or by compiling offline the problem as a multi-valued decision diagram. In this article, we define several questions related to global inverse consistency and analyze their complexity. Despite their theoretical intractability, we propose several algorithms for enforcing and restoring global inverse consistency and we show that the best version is efficient enough to be used in an interactive setting on several configuration and design problems.

Triangle-based consistencies for cost function networks

Cost Function Networks (aka Weighted CSP) allow to model a variety of problems, such as optimization of deterministic and stochastic graphical models including Markov random Fields and Bayesian Networks. Solving cost function networks is thus an important problem for deterministic and probabilistic reasoning. This paper focuses on local consistencies which define essential tools to simplify Cost Function Networks, and provide lower bounds on their optimal solution cost. To strengthen arc consistency bounds, we follow the idea of triangle-based domain consistencies for hard constraint networks (path inverse consistency, restricted or max-restricted path consistencies), describe their systematic extension to cost function networks, study their relative strengths, define enforcing algorithms, and experiment with them on a large set of benchmark problems. On some of these problems, our improved lower bounds seem necessary to solve them.

A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis

Deformable image registration (DIR) is essential for adaptive radiotherapy (RT) for tumor sites subject to motion, changes in tumor volume, as well as changes in patient normal anatomy due to weight loss. Several methods have been published to evaluate DIR-related uncertainties but they are not widely adopted. The aim of this study was, therefore, to evaluate intra-patient DIR for two highly deformable organs—the bladder and the rectum—in prostate cancer RT using a quantitative metric based on multiple image registration, the distance discordance metric (DDM). Voxel-by-voxel DIR uncertainties of the bladder and rectum were evaluated using DDM on weekly CT scans of 38 subjects previously treated with RT for prostate cancer (six scans/subject). The DDM was obtained from group-wise B-spline registration of each patient’s collection of repeat CT scans. For each structure, registration uncertainties were derived from DDM-related metrics. In addition, five other quantitative measures, including inverse consistency error (ICE), transitivity error (TE), Dice similarity (DSC) and volume ratios between corresponding structures from pre- and post- registered images were computed and compared with the DDM. The DDM varied across subjects and structures; DDMmean of the bladder ranged from 2 to 13 mm and from 1 to 11 mm for the rectum. There was a high correlation between DDMmean of the bladder and the rectum (Pearson’s correlation coefficient, Rp = 0.62). The correlation between DDMmean and the volume ratios post-DIR was stronger (Rp = 0.51; 0.68) than the correlation with the TE (bladder: Rp = 0.46; rectum: Rp = 0.47), or the ICE (bladder: Rp = 0.34; rectum: Rp = 0.37). There was a negative correlation between DSC and DDMmean of both the bladder (Rp = −0.23) and the rectum (Rp = −0.63). The DDM uncertainty metric indicated considerable DIR variability across subjects and structures. Our results show a stronger correlation with volume ratios and with the DSC using DDM compared to using ICE and TE. The DDM has the potential to quantitatively identify regions of large DIR uncertainties and consequently identify anatomical/scan outliers. The DDM can, thus, be applied to improve the adaptive RT process for tumor sites subject to motion.

SU‐C‐207B‐06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

Purpose:To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer.Methods:Linear and multi‐resolution BSpline deformable registration were performed on Pre‐Post‐CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi‐modality. Similarity of Warped‐CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial‐temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10‐fold cross‐validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC).Results:Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation.Conclusion:Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration.This work was supported in part by the National Cancer Institute Grants R01CA172638.

SU‐F‐J‐88: Comparison of Two Deformable Image Registration Algorithms for CT‐To‐CT Contour Propagation

Purpose:To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling.Methods:Both DIR algorithms were used for CT‐to‐CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagation was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0<RQS<1) assigned by dosimetrists based on the extent of manual refinement needed for acceptable quality.Results:Both algorithms showed similar ICE (< 1.5 mm), but the hybrid DIR (TRE = 3.2 mm) performed better than the biomechanical DIR (TRE = 4.3 mm) with landmark tracking. Both algorithms had comparable DSC (DSC > 0.9 for lungs, > 0.85 for heart, > 0.8 for liver) and similar qualitative assessments (VQS < 0.35, RQS > 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR.Conclusion:The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.

Near-surface anisotropic structure characterization by Love wave inversion for assessing ground conditions in Urban Areas

In many geophysical applications, neglecting of anisotropy is somehow an oversimplification. The mismatch between prediction based on isotropic theory and near-surface seismic observations indicates the need for the inclusion of medium anisotropy. In this paper, surface wave (Love wave) dispersion properties are used to estimate the anisotropic structure of the near-surface layered earth, which is modeled as media possess vertical transverse isotropy (VTI), a reasonable assumption for near-surface sedimentary layers. Our approach utilizes multi-mode surface waves to estimate both the velocity structure and the anisotropy structure. This approach consists of three parts. First, the dispersion analysis is used to extract dispersion curves from real data. Second, the forward modeling is carried out based on the dispersion equation of Love wave in a multi-layered VTI medium. Dispersion curves of multi-modes, which are the numerical solutions of the dispersion equation, are obtained by a graphic-based method. Finally, the very fast simulated annealing (VFSA) algorithm is used to invert velocity structure and anisotropy structure simultaneously. Our approach is verified by the synthetic dispersion curve generated by a VTI medium model. The estimation of shear wave velocity and anisotropy structure of surface wave data acquired at Rentschler Field, an urban center site on sediments in the Connecticut River valley reveals a simple structure of the sediment layer over a bedrock half space. The results are verified by other inversion results based on different data set obtained on the same site. The consistency of inversion results shows the feasibility and efficiency of the approach.

Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

Deformable image registration remains a challenging research area due to difficulties associated with local intensity variation and large motion. In this paper, an Accurate Inverse-consistent Symmetric Optical Flow (AISOF) method is proposed to overcome these difficulties. The two main contributions of AISOF include the following: (1) a coarse-to-fine strategy for an inverse-consistent symmetric method and (2) a novel Hybrid Local Binary Pattern (HLBP) to the classical Lucas–Kanade optical flow method. The HLBP consists of a median binary pattern and a generalised centre-symmetric local binary pattern. The generalised centre-symmetric local binary pattern has two thresholds, and this pattern can capture more information than the classical centre-symmetric local binary pattern, which has one threshold. The proposed HLBP can cope well with high contrast intensity and local intensity variation. Because the inverse-consistent symmetric method can reduce inverse consistency errors in Markov random fields based registration methods, we adopted this method to improve the accuracy of registration. In addition, a coarse-to-fine strategy was adopted to handle large motion. The proposed AISOF method was evaluated for 10 publicly available 4D CT lung datasets from the DIR-Lab. The mean target registration error of the AISOF method is 1.16 mm, which is significantly superior to the error of the classical Lucas–Kanade optical flow method, 2.83 mm. Moreover, this error is also the smallest of all unmasked registration methods using these datasets.

A fast algorithm to estimate inverse consistent image transformation based on corresponding landmarks

Inverse consistency is an important feature for non-rigid image transformation in medical imaging analysis. In this paper, a simple and efficient inverse consistent image transformation estimation algorithm is proposed to preserve correspondence of landmarks and accelerate convergence. The proposed algorithm estimates both the forward and backward transformations simultaneously in the way that they are inverse to each other based on the correspondence of landmarks. Instead of computing the inverse functions and the inverse consistent transformations, respectively, we combine them together, which can improve computation efficiency significantly. Moreover, radial basis functions (RBFs) based transformation is adopted in our algorithm, which can handle deformation with local or global support. Our algorithm maps one landmark to its corresponding position exactly using the forward and backward transformations. Moreover, our algorithm is employed to estimate the forward and backward transformations in robust point matching, as well to demonstrate the application of our algorithm in image registration. The experiment results of uniform grids and test images indicate the improvement of the proposed algorithm in the aspect of inverse consistency of transformations and the reduction of the computation time of the forward and the backward transformations. The performance of our algorithm applying to robust point matching is evaluated using both brain slices and lung slices. Our experiments show that by combing robust point matching with our algorithm, the registration accuracy can be improved and the smoothness of transformations can be preserved.

A highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images

Spatial normalization plays a key role in voxel-based analyses of brain images. We propose a highly accurate algorithm for high-dimensional spatial normalization of brain images based on the technique of symmetric optical flow. We first construct a three dimension optical model with the consistency assumption of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. Then, an efficient inverse consistency optical flow is proposed with aims of higher registration accuracy, where the flow is naturally symmetric. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering brain images data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is not only better than that of those traditional optical flow algorithms, but also comparable to other registration methods used extensively in the medical imaging community. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention.

Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.

The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping. The authors describe a DIR based adaptive radiotherapy workflow, using CT and cone-beam CT (CBCT) imaging. The transformations that mapped the anatomy between the two time points were obtained using four different DIR approaches available in NiftyReg. These included a standard unidirectional algorithm and more sophisticated bidirectional ones that encourage or ensure inverse consistency. The forward (CT-to-CBCT) deformation vector fields (DVFs) were used to propagate the CT Hounsfield units and structures to the daily geometry for "dose of the day" calculations, while the backward (CBCT-to-CT) DVFs were used to remap the dose of the day onto the planning CT (pCT). Data from five head and neck patients were used to evaluate the performance of each implementation based on geometrical matching, physical properties of the DVFs, and similarity between warped dose distributions. Geometrical matching was verified in terms of dice similarity coefficient (DSC), distance transform, false positives, and false negatives. The physical properties of the DVFs were assessed calculating the harmonic energy, determinant of the Jacobian, and inverse consistency error of the transformations. Dose distributions were displayed on the pCT dose space and compared using dose difference (DD), distance to dose difference, and dose volume histograms. All the DIR algorithms gave similar results in terms of geometrical matching, with an average DSC of 0.85 ± 0.08, but the underlying properties of the DVFs varied in terms of smoothness and inverse consistency. When comparing the doses warped by different algorithms, we found a root mean square DD of 1.9% ± 0.8% of the prescribed dose (pD) and that an average of 9% ± 4% of voxels within the treated volume failed a 2%pD DD-test (DD2%-pp). Larger DD2%-pp was found within the high dose gradient (21% ± 6%) and regions where the CBCT quality was poorer (28% ± 9%). The differences when estimating the mean and maximum dose delivered to organs-at-risk were up to 2.0%pD and 2.8%pD, respectively. The authors evaluated several DIR algorithms for CT-to-CBCT registrations. In spite of all methods resulting in comparable geometrical matching, the choice of DIR implementation leads to uncertainties in dose warped, particularly in regions of high gradient and/or poor imaging quality.

The Calibration Process of OSL Detectors used for Staff and Patient Dosimetry in Hospital Environment

The optically stimulated luminescence (OSL-BeO) dosemeter is increasingly being used as a dosimetric technique in various fields such as medical dosimetry. According to our fixed dose protocol, the activities of 3.7-7.4 GBq I-131 source is used for thyroid carcinoma therapy. The in house calibration process for usage of OSL’s at hospital was arranged according to the encapsulated I-131. The measurement point was planned in three different radial distances from source free in air. The dose-rate measurement was done by Geiger-Muller (GM), and then three pieces of OSL was placed in the same positions for one hour. The inverse square law consistency was found (R2=0.99). The calibration coefficient was calculated. For determining the performance of OSL at different dose rates, it used for personnel and patient dosimetry. The average annual dose/2mon to the whole body for all staff by OSL were 0.80 mSv. After administration of 3.7 GBq (100 mCi) therapeutic dose to selected patients, the average pectoral dose was 97.5+32.6 mSv. This calibration process is helpful for confidence of OSL detectors used for dosimetry of staff and patients treated with high activity I-131.

PATTERN –BASED WITH SURFACE-BASED MORPHOMETRY SURVEY ON BRAIN CHANGES

Morphometry is identifying and characterizing differences and correlations between brain shapes among population. Study of brain shape has drawn attention among many researchers on different diseases counting Schizophrenia, dyslexia, autism, Alzheimer and turner’s syndrome. Many approaches have been proposed for computer-assisted diagnostic taxonomy. Several significant progressive brain changes occur during aging. Pattern-Based morphometry is a robust application for measuring the change in brain parametric mapping. By reason of bias featuring in image along with the algorithm, have a penalty term and inverse consistency that are necessary to oversight the change in non-biological structure. Morphometric analysis forces a pact between the sensitivity and specificity that has drawn reporting or increasing attention in the field of biological science. A novel technique illustrating the brain tissue aging survey with surface-based and multivariate pattern of morphometric brain change To keep the robustness and specificity contributed by the spatial term and cortical analyses, while maintaining the localization and sensitivity Experimental results propose a greater inter variability within normal aging as well as the generation of more sensitive based morphometry in brain survey.