Average Symmetric Distance Research Articles

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy.

Purpose:To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it.Materials and Methods:Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.Results:A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.Conclusion:The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization.

To propose and assess a new method that automatically extracts a three-dimensional (3D) geometric model of the thoracic aorta (TA) from 3D cine phase contrast MRI (PCMRI) acquisitions. The proposed method is composed of two steps: segmentation of the TA and creation of the 3D geometric model. The segmentation algorithm, based on Level Set, was set and applied to healthy subjects acquired in three different modalities (with and without SENSE reduction factors). Accuracy was evaluated using standard quality indices. The 3D model is characterized by the vessel surface mesh and its centerline; the comparison of models obtained from the three different datasets was also carried out in terms of radius of curvature (RC) and average tortuosity (AT). In all datasets, the segmentation quality indices confirmed very good agreement between manual and automatic contours (average symmetric distance < 1.44 mm, DICE Similarity Coefficient > 0.88). The 3D models extracted from the three datasets were found to be comparable, with differences of less than 10% for RC and 11% for AT. Our method was found effective on PCMRI data to provide a 3D geometric model of the TA, to support morphometric and hemodynamic characterization of the aorta.

TU-C-141-04: Evaluation of Clinical Acceptability of DIR Mapped Contours for Adaptive Radiotherapy with 4D Cone-Beam CT

Purpose: To evaluate clinical acceptability of contours generated using deformable image registration of 4D fan beam (4DFBCT) to 4D cone beam (4DCBCT) images for image-guided adaptive radiotherapy (IGART). Methods: Sixteen locally advanced non-small cell lung cancer (NSCLC) patients underwent one planning 4DFBCT and weekly 4DCBCT scans. 4DFBCT to 4DCBCT registrations were performed using two intensity-driven deformable image registration (DIR) algorithms: 1) small deformation, inverse consistent linear elastic (SICLE) algorithm and 2) Insight Toolkit diffeomorphic demons (DEMONS). Multiple physicians delineated the gross tumor volume in all images (MANUAL). Manual contours were mapped from 4DFBCT to 4DCBCT for two scans in each patient, and were compared with manual contours using Dice similarity coefficient (DSC), average symmetric distance (ASD), false positive (FP), false negative (FN) and percentage volume difference (%VD). A physician was asked to rate DIR generated and manual contours as A) clinically acceptable (CA) B) clinically acceptable after minor modifications (CAMM) and C) clinically unacceptable (CU) using planning CT contour as reference. Reviewing physician was unaware that some of the contours were manual. The results were analyzed with respect to similarity measures, volume and regression characteristics of the tumor. Results: No contours were rated as CU by the physician. The number of contours rated as CA and CAMM was respectively 14 (44%) and 18 (56%) for SICLE, 3 (9%) and 29 (91%) for DEMONS, and 26 (81%) and 6 (19%) for MANUAL. No correlation was observed between physician ratings and DSC, ASD, FP, FN, %VD, tumor shrinkage, day of CBCT scan, and volume of the GTV. Conclusion: DIR generated contours were considered clinically acceptable for IGART, but may require some manual adjustment prior to use. Volume and surface similarity measures did not correlate with physician judgment of clinical acceptability. Supported by NIH Grant P01 CA 116602; This work was supported by a grant (P01CA116602) from the National Cancer Institute. Gary E. Christensen holds a papent related to the SICLE registration algorithm; E. Weiss and J. Williamson has grants from Varian medical systems and Philips Radiation Oncology Systems.

Evaluation of 4-dimensional Computed Tomography to 4-dimensional Cone-Beam Computed Tomography Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy

To evaluate 2 deformable image registration (DIR) algorithms for the purpose of contour mapping to support image-guided adaptive radiation therapy with 4-dimensional cone-beam CT (4DCBCT). One planning 4D fan-beam CT (4DFBCT) and 7 weekly 4DCBCT scans were acquired for 10 locally advanced non-small cell lung cancer patients. The gross tumor volume was delineated by a physician in all 4D images. End-of-inspiration phase planning 4DFBCT was registered to the corresponding phase in weekly 4DCBCT images for day-to-day registrations. For phase-to-phase registration, the end-of-inspiration phase from each 4D image was registered to the end-of-expiration phase. Two DIR algorithms-small deformation inverse consistent linear elastic (SICLE) and Insight Toolkit diffeomorphic demons (DEMONS)-were evaluated. Physician-delineated contours were compared with the warped contours by using the Dice similarity coefficient (DSC), average symmetric distance, and false-positive and false-negative indices. The DIR results are compared with rigid registration of tumor. For day-to-day registrations, the mean DSC was 0.75 ± 0.09 with SICLE, 0.70 ± 0.12 with DEMONS, 0.66 ± 0.12 with rigid-tumor registration, and 0.60 ± 0.14 with rigid-bone registration. Results were comparable to intraobserver variability calculated from phase-to-phase registrations as well as measured interobserver variation for 1 patient. SICLE and DEMONS, when compared with rigid-bone (4.1 mm) and rigid-tumor (3.6 mm) registration, respectively reduced the average symmetric distance to 2.6 and 3.3 mm. On average, SICLE and DEMONS increased the DSC to 0.80 and 0.79, respectively, compared with rigid-tumor (0.78) registrations for 4DCBCT phase-to-phase registrations. Deformable image registration achieved comparable accuracy to reported interobserver delineation variability and higher accuracy than rigid-tumor registration. Deformable image registration performance varied with the algorithm and the patient.

An Automatic Segmentation Method for Regional Analysis of Femoral Neck Images Acquired by pQCT

We developed an automatic method for regional analysis of femoral neck images acquired by peripheral quantitative computed tomography (pQCT), based on automatic spatial re-alignment and segmentation; the segmentation method, based on a morphological approach, explicitly accounts for the presence of three different bone compartments: cortical region, trabecular region, and transition zone between cortical and trabecular compartments. The proposed method was applied on 13 femoral neck sections derived from female donors who were undergoing hip replacement surgery for primary degenerative arthritis or fracture, and a typical densitometric and structural analysis was performed both globally and regionally. The proposed segmentation method was quantitatively evaluated by comparing automatic contour and the corresponding manual contours delineated by three operators using metrics based on surface distance (average symmetric distance, ASD) and volumetric overlapping (dice similarity coefficient, DSC). The same approach was used to validate the automatic spatial orientation, considering as metric the difference between manual and automatic angle orientation. Results confirm a satisfactory agreement between automatic and manual performances (ASD<0.41mm, DSC>0.91, orientation difference=3.61°) and show that globally our algorithm performs very well. Concerning regional analysis application, from our results we can observe that significant differences are present among the four bone quadrants.