skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: SU-C-17A-03: Evaluation of Deformable Image Registration Methods Between MRI and CT for Prostate Cancer Radiotherapy

Abstract

Purpose: We evaluated the performance of two commercially available and one open source B-Spline deformable image registration (DIR) algorithms between T2-weighted MRI and treatment planning CT using the DICE indices. Methods: CT simulation (CT-SIM) and MR simulation (MR-SIM) for four prostate cancer patients were conducted on the same day using the same setup and immobilization devices. CT images (120 kVp, 500 mAs, voxel size = 1.1x1.1x3.0 mm3) were acquired using an open-bore CT scanner. T2-weighted Turbo Spine Echo (T2W-TSE) images (TE/TR/α = 80/4560 ms/90°, voxel size = 0.7×0.7×2.5 mm3) were scanned on a 1.0T high field open MR-SIM. Prostates, seminal vesicles, rectum and bladders were delineated on both T2W-TSE and CT images by the attending physician. T2W-TSE images were registered to CT images using three DIR algorithms, SmartAdapt (Varian), Velocity AI (Velocity) and Elastix (Klein et al 2010) and contours were propagated. DIR results were evaluated quantitatively or qualitatively by image comparison and calculating organ DICE indices. Results: Significant differences in the contours of prostate and seminal vesicles were observed between MR and CT. On average, volume changes of the propagated contours were 5%, 2%, 160% and 8% for the prostate, seminal vesicles, bladder and rectum respectively. Corresponding mean DICEmore » indices were 0.7, 0.5, 0.8, and 0.7. The intraclass correlation coefficient (ICC) was 0.9 among three algorithms for the Dice indices. Conclusion: Three DIR algorithms for CT/MR registration yielded similar results for organ propagation. Due to the different soft tissue contrasts between MRI and CT, organ delineation of prostate and SVs varied significantly, thus efforts to develop other DIR evaluation metrics are warranted. Conflict of interest: Submitting institution has research agreements with Varian Medical System and Philips Healthcare.« less

Authors:
; ; ; ; ; ; ;  [1]
  1. I Chetty, Henry Ford Health System, Detroit, MI (United States)
Publication Date:
OSTI Identifier:
22412453
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 41; Journal Issue: 6; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ALGORITHMS; BLADDER; CAT SCANNING; IMAGE PROCESSING; NEOPLASMS; NMR IMAGING; PATIENTS; PROSTATE; RADIOTHERAPY; RECTUM; VERTEBRAE

Citation Formats

Wen, N, Glide-Hurst, C, Zhong, H, Chin, K, Kumarasiri, A, Liu, C, Liu, M, and Siddiqui, S. SU-C-17A-03: Evaluation of Deformable Image Registration Methods Between MRI and CT for Prostate Cancer Radiotherapy. United States: N. p., 2014. Web. doi:10.1118/1.4889730.
Wen, N, Glide-Hurst, C, Zhong, H, Chin, K, Kumarasiri, A, Liu, C, Liu, M, & Siddiqui, S. SU-C-17A-03: Evaluation of Deformable Image Registration Methods Between MRI and CT for Prostate Cancer Radiotherapy. United States. doi:10.1118/1.4889730.
Wen, N, Glide-Hurst, C, Zhong, H, Chin, K, Kumarasiri, A, Liu, C, Liu, M, and Siddiqui, S. Sun . "SU-C-17A-03: Evaluation of Deformable Image Registration Methods Between MRI and CT for Prostate Cancer Radiotherapy". United States. doi:10.1118/1.4889730.
@article{osti_22412453,
title = {SU-C-17A-03: Evaluation of Deformable Image Registration Methods Between MRI and CT for Prostate Cancer Radiotherapy},
author = {Wen, N and Glide-Hurst, C and Zhong, H and Chin, K and Kumarasiri, A and Liu, C and Liu, M and Siddiqui, S},
abstractNote = {Purpose: We evaluated the performance of two commercially available and one open source B-Spline deformable image registration (DIR) algorithms between T2-weighted MRI and treatment planning CT using the DICE indices. Methods: CT simulation (CT-SIM) and MR simulation (MR-SIM) for four prostate cancer patients were conducted on the same day using the same setup and immobilization devices. CT images (120 kVp, 500 mAs, voxel size = 1.1x1.1x3.0 mm3) were acquired using an open-bore CT scanner. T2-weighted Turbo Spine Echo (T2W-TSE) images (TE/TR/α = 80/4560 ms/90°, voxel size = 0.7×0.7×2.5 mm3) were scanned on a 1.0T high field open MR-SIM. Prostates, seminal vesicles, rectum and bladders were delineated on both T2W-TSE and CT images by the attending physician. T2W-TSE images were registered to CT images using three DIR algorithms, SmartAdapt (Varian), Velocity AI (Velocity) and Elastix (Klein et al 2010) and contours were propagated. DIR results were evaluated quantitatively or qualitatively by image comparison and calculating organ DICE indices. Results: Significant differences in the contours of prostate and seminal vesicles were observed between MR and CT. On average, volume changes of the propagated contours were 5%, 2%, 160% and 8% for the prostate, seminal vesicles, bladder and rectum respectively. Corresponding mean DICE indices were 0.7, 0.5, 0.8, and 0.7. The intraclass correlation coefficient (ICC) was 0.9 among three algorithms for the Dice indices. Conclusion: Three DIR algorithms for CT/MR registration yielded similar results for organ propagation. Due to the different soft tissue contrasts between MRI and CT, organ delineation of prostate and SVs varied significantly, thus efforts to develop other DIR evaluation metrics are warranted. Conflict of interest: Submitting institution has research agreements with Varian Medical System and Philips Healthcare.},
doi = {10.1118/1.4889730},
journal = {Medical Physics},
number = 6,
volume = 41,
place = {United States},
year = {Sun Jun 15 00:00:00 EDT 2014},
month = {Sun Jun 15 00:00:00 EDT 2014}
}
  • Purpose: To evaluate uncertainties of organ specific Deformable Image Registration (DIR) for H and N cancer Adaptive Radiation Therapy (ART). Methods: A commercial DIR evaluation tool, which includes a digital phantom library of 8 patients, and the corresponding “Ground truth Deformable Vector Field” (GT-DVF), was used in the study. Each patient in the phantom library includes the GT-DVF created from a pair of CT images acquired prior to and at the end of the treatment course. Five DIR tools, including 2 commercial tools (CMT1, CMT2), 2 in-house (IH-FFD1, IH-FFD2), and a classic DEMON algorithms, were applied on the patient images.more » The resulting DVF was compared to the GT-DVF voxel by voxel. Organ specific DVF uncertainty was calculated for 10 ROIs: Whole Body, Brain, Brain Stem, Cord, Lips, Mandible, Parotid, Esophagus and Submandibular Gland. Registration error-volume histogram was constructed for comparison. Results: The uncertainty is relatively small for brain stem, cord and lips, while large in parotid and submandibular gland. CMT1 achieved best overall accuracy (on whole body, mean vector error of 8 patients: 0.98±0.29 mm). For brain, mandible, parotid right, parotid left and submandibular glad, the classic Demon algorithm got the lowest uncertainty (0.49±0.09, 0.51±0.16, 0.46±0.11, 0.50±0.11 and 0.69±0.47 mm respectively). For brain stem, cord and lips, the DVF from CMT1 has the best accuracy (0.28±0.07, 0.22±0.08 and 0.27±0.12 mm respectively). All algorithms have largest right parotid uncertainty on patient #7, which has image artifact caused by tooth implantation. Conclusion: Uncertainty of deformable CT image registration highly depends on the registration algorithm, and organ specific. Large uncertainty most likely appears at the location of soft-tissue organs far from the bony structures. Among all 5 DIR methods, the classic DEMON and CMT1 seem to be the best to limit the uncertainty within 2mm for all OARs. Partially supported by research grant from Elekta.« less
  • Purpose: To compute daily dose delivered during radiotherapy, deformable registration needs to be relatively fast, automated, and accurate. The aim of this study was to evaluate the performance of commercial deformable registration software for deforming between two modalities: planning computed tomography (pCT) images acquired for treatment planning and cone beam (CB) CT images acquired prior to each fraction of prostate cancer radiotherapy. Methods: A workflow was designed using MIM Software™ that aligned and deformed pCT into daily CBCT images in two steps: (1) rigid shifts applied after daily CBCT imaging to align patient anatomy to the pCT and (2) normalizedmore » intensity-based deformable registration to account for interfractional anatomical variations. The physician-approved CTV and organ and risk (OAR) contours were deformed from the pCT to daily CBCT over the course of treatment. The same structures were delineated on each daily CBCT by a radiation oncologist. Dice similarity coefficient (DSC) mean and standard deviations were calculated to quantify the deformable registration quality for prostate, bladder, rectum and femoral heads. Results: To date, contour comparisons have been analyzed for 31 daily fractions of 2 of 10 of the cohort. Interim analysis shows that right and left femoral head contours demonstrate the highest agreement (DSC: 0.96±0.02) with physician contours. Additionally, deformed bladder (DSC: 0.81±0.09) and prostate (DSC: 0.80±0.07) have good agreement with physician-defined daily contours. Rectum contours have the highest variations (DSC: 0.66±0.10) between the deformed and physician-defined contours on daily CBCT imaging. Conclusion: For structures with relatively high contrast boundaries on CBCT, the MIM automated deformable registration provided accurate representations of the daily contours during treatment delivery. These findings will permit subsequent investigations to automate daily dose computation from CBCT. However, improved methods need to be investigated to improve deformable results for rectum contours.« less
  • Purpose: Deformable image registration (DIR) plays a pivotal role in head and neck adaptive radiotherapy but a systematic validation of DIR algorithms has been limited by a lack of quantitative high-resolution groundtruth. We address this limitation by developing a GPU-based framework that provides a systematic DIR validation by generating (a) model-guided synthetic CTs representing posture and physiological changes, and (b) model-guided landmark-based validation. Method: The GPU-based framework was developed to generate massive mass-spring biomechanical models from patient simulation CTs and contoured structures. The biomechanical model represented soft tissue deformations for known rigid skeletal motion. Posture changes were simulated by articulatingmore » skeletal anatomy, which subsequently applied elastic corrective forces upon the soft tissue. Physiological changes such as tumor regression and weight loss were simulated in a biomechanically precise manner. Synthetic CT data was then generated from the deformed anatomy. The initial and final positions for one hundred randomly-chosen mass elements inside each of the internal contoured structures were recorded as ground truth data. The process was automated to create 45 synthetic CT datasets for a given patient CT. For instance, the head rotation was varied between +/− 4 degrees along each axis, and tumor volumes were systematically reduced up to 30%. Finally, the original CT and deformed synthetic CT were registered using an optical flow based DIR. Results: Each synthetic data creation took approximately 28 seconds of computation time. The number of landmarks per data set varied between two and three thousand. The validation method is able to perform sub-voxel analysis of the DIR, and report the results by structure, giving a much more in depth investigation of the error. Conclusions: We presented a GPU based high-resolution biomechanical head and neck model to validate DIR algorithms by generating CT equivalent 3D volumes with simulated posture changes and physiological regression.« less
  • Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the randommore » walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940)« less
  • Purpose: Accurate deformable image registration (DIR) between external beam radiotherapy (EBRT) and HDR brachytherapy (BT) CT images in cervical cancer is challenging. DSC has been evaluated only on the basis of the consistency of the structure, and its use does not guarantee an anatomically reasonable deformation. We evaluate the DIR accuracy for cervical cancer with DSC and anatomical landmarks using a 3D-printed pelvis phantom. Methods: A 3D-printed, deformable female pelvis phantom was created on the basis of the patient’s CT image. Urethane and silicon were used as materials for creating the uterus and bladder, respectively, in the phantom. We performedmore » DIR in two cases: case-A with a full bladder (170 ml) in both the EBRT and BT images and case-B with a full bladder in the BT image and a half bladder (100 ml) in the EBRT image. DIR was evaluated using DSCs and 70 uterus and bladder landmarks. A Hybrid intensity and structure DIR algorithm with two settings (RayStation) was used. Results: In the case-A, DSCs of the intensity-based DIR were 0.93 and 0.85 for the bladder and uterus, respectively, whereas those of hybrid-DIR were 0.98 and 0.96, respectively. The mean landmark error values of intensity-based DIR were 0.73±0.29 and 1.70±0.19 cm for the bladder and uterus, respectively, whereas those of Hybrid-DIR were 0.43±0.33 and 1.23±0.25 cm, respectively. In both cases, the Hybrid-DIR accuracy was better than the intensity-based DIR accuracy for both evaluation methods. However, for several bladder landmarks, the Hybrid-DIR landmark errors were larger than the corresponding intensity-based DIR errors (e.g., 2.26 vs 1.25 cm). Conclusion: Our results demonstrate that Hybrid-DIR can perform with a better accuracy than the intensity-based DIR for both DSC and landmark errors; however, Hybrid-DIR shows a larger landmark error for some landmarks because the technique focuses on both the structure and intensity.« less