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Title: SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

Abstract

Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized to characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, themore » simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT-CBCT registration.« less

Authors:
; ; ; ; ;  [1];  [2]
  1. University of Texas Health Science Center at San Antonio, Cancer Therapy and Research Center, San Antonio, TX (United States)
  2. University of California San Francisco, San Francisco, CA (United States)
Publication Date:
OSTI Identifier:
22494107
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 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; ACCURACY; ALGORITHMS; BIOMEDICAL RADIOGRAPHY; COMPUTERIZED TOMOGRAPHY; DEFORMATION; HEAD; IMAGES; NECK; PHANTOMS; QUALITY ASSURANCE; SIMULATION

Citation Formats

Cline, K, Narayanasamy, G, Obediat, M, Stanley, D, Stathakis, S, Kirby, N, and Kim, H. SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance. United States: N. p., 2015. Web. doi:10.1118/1.4924176.
Cline, K, Narayanasamy, G, Obediat, M, Stanley, D, Stathakis, S, Kirby, N, & Kim, H. SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance. United States. doi:10.1118/1.4924176.
Cline, K, Narayanasamy, G, Obediat, M, Stanley, D, Stathakis, S, Kirby, N, and Kim, H. Mon . "SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance". United States. doi:10.1118/1.4924176.
@article{osti_22494107,
title = {SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance},
author = {Cline, K and Narayanasamy, G and Obediat, M and Stanley, D and Stathakis, S and Kirby, N and Kim, H},
abstractNote = {Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized to characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT-CBCT registration.},
doi = {10.1118/1.4924176},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: The objective of this study is to propose an alternative QA technique that analyzes imaging quality(IQ) in CBCT-QA processing. Methods: A catphan phantom was used to take CT imaging data set that were imported into a treatment planning system - Eclipse. The image quality was analyzed in terms of in-slice geometry resolution, Hounsfield numbers(HU) accuracy, mean-slice thickness, edge-to-center uniformity, low contrast resolution, and high contrast spatial resolution in Eclipse workstation. The CBCT-QA was also analyzed by OBI-workstation and a commercial software. Comparison was made to evaluation feasibility in a TPS environment. Results: The analysis of IQ was conducted inmore » Eclipse v10.0 TPS. In-slice geometric resolution was measured between 2-rods in section CTP404 and repeated for all 4 rods with the difference between expected and measured values less than +/−0.1 cm. For HU, the difference between expected and measured values in HU was found much less than +/−40. Mean slice thickness measured by a distance on the wire proportional to scanner increment multiplying by a factor of 0.42. After repeating measurements to 4 wires, the average difference between expected and measured values was less +/−0.124 mm in slice thickness. HU uniformity was measured in section CTP486 with the tolerance less than +/−40 HU. Low contrast resolution in section CTP515 and high contrast resolution in section CTP528 were found to be 7 disks in diameter of 4 mm and 6 lp/cm, respectively. Eclipse TPS results indicated a good agreement to those obtained in OBI workstation and ImagePro software for major parameters. Conclusion: An analysis of IQ was proposed as an alternative CBCT QA processing. Based upon measured data assessment, proposed method was accurate and consistent to IQ evaluation and TG142 guideline. The approach was to utilize TPS resource, which can be valuable to re-planning, verification, and delivery in adaptive therapy.« less
  • Purpose: To determine the 6 degree of freedom systematic deviations between 2D/3D and CBCT image registration with various imaging setups and fusion algorithms on the Varian Edge Linac. Methods: An anthropomorphic head phantom with radio opaque targets embedded was scanned with CT slice thicknesses of 0.8, 1, 2, and 3mm. The 6 DOF systematic errors were assessed by comparing 2D/3D (kV/MV with CT) with 3D/3D (CBCT with CT) image registrations with different offset positions, similarity measures, image filters, and CBCT slice thicknesses (1 and 2 mm). The 2D/3D registration accuracy of 51 fractions for 26 cranial SRS patients was alsomore » evaluated by analyzing 2D/3D pre-treatment verification taken after 3D/3D image registrations. Results: The systematic deviations of 2D/3D image registration using kV- kV, MV-kV and MV-MV image pairs were within ±0.3mm and ±0.3° for translations and rotations with 95% confidence interval (CI) for a reference CT with 0.8 mm slice thickness. No significant difference (P>0.05) on target localization was observed between 0.8mm, 1mm, and 2mm CT slice thicknesses with CBCT slice thicknesses of 1mm and 2mm. With 3mm CT slice thickness, both 2D/3D and 3D/3D registrations performed less accurately in longitudinal direction than thinner CT slice thickness (0.60±0.12mm and 0.63±0.07mm off, respectively). Using content filter and using similarity measure of pattern intensity instead of mutual information, improved the 2D/3D registration accuracy significantly (P=0.02 and P=0.01, respectively). For the patient study, means and standard deviations of residual errors were 0.09±0.32mm, −0.22±0.51mm and −0.07±0.32mm in VRT, LNG and LAT directions, respectively, and 0.12°±0.46°, −0.12°±0.39° and 0.06°±0.28° in RTN, PITCH, and ROLL directions, respectively. 95% CI of translational and rotational deviations were comparable to those in phantom study. Conclusion: 2D/3D image registration provided on the Varian Edge radiosurgery, 6 DOF-based system provides accurate target positioning for frameless image-guided cranial stereotactic radiosurgery.« less
  • Purpose: Image-Guided radiation therapy(IGRT) depends on reliable online patient-specific anatomy information to address random and progressive anatomy changes. Large margins have been suggested to bladder cancer treatment due to large daily bladder anatomy variation. KV Cone beam CT(CBCT) has been used in IGRT localization prevalently; however, its lack of soft tissue contrast makes clinicians hesitate to perform daily soft tissue alignment with CBCT for partial bladder cancer treatment. This study compares the localization uncertainties of bladder cancer IGRT using CTon- Rails(CTOR) and CBCT. Methods: Three T2N0M0 bladder cancer patients (total of 66 Gy to partial bladder alone) were localized dailymore » with either CTOR or CBCT for their entire treatment course. A total of 71 sets of CTOR and 22 sets of CBCT images were acquired and registered with original planning CT scans by radiation therapists and approved by radiation oncologists for the daily treatment. CTOR scanning entailed 2mm slice thickness, 0.98mm axial voxel size, 120kVp and 240mAs. CBCT used a half fan pelvis protocol from Varian OBI system with 2mm slice thickness, 0.98axial voxel size, 125kVp, and 680mAs. Daily localization distribution was compared. Accuracy of CTOR and CBCT on partial bladder alignment was also evaluated by comparing bladder PTV coverage. Results: 1cm all around PTV margins were used in every patient except target superior limit margin to 0mm due to bowel constraint. Daily shifts on CTOR averaged to 0.48, 0.24, 0.19 mms(SI,Lat,AP directions); CBCT averaged to 0.43, 0.09, 0.19 mms(SI,Lat,AP directions). The CTOR daily localization showed superior results of V100% of PTV(102% CTOR vs. 89% CBCT) and bowel(Dmax 69.5Gy vs. 78Gy CBCT). CTOR images showed much higher contrast on bladder PTV alignment. Conclusion: CTOR daily localization for IGRT is more dosimetrically beneficial for partial bladder cancer treatment than kV CBCT localization and provided better soft tissue PTV identification.« less
  • Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document.more » NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients.« less
  • Purpose: To develop a CBCT HU correction method using a patient specific HU to mass density conversion curve based on a novel image registration and organ mapping method for head-and-neck radiation therapy. Methods: There are three steps to generate a patient specific CBCT HU to mass density conversion curve. First, we developed a novel robust image registration method based on sparseness analysis to register the planning CT (PCT) and the CBCT. Second, a novel organ mapping method was developed to transfer the organs at risk (OAR) contours from the PCT to the CBCT and corresponding mean HU values of eachmore » OAR were measured in both the PCT and CBCT volumes. Third, a set of PCT and CBCT HU to mass density conversion curves were created based on the mean HU values of OARs and the corresponding mass density of the OAR in the PCT. Then, we compared our proposed conversion curve with the traditional Catphan phantom based CBCT HU to mass density calibration curve. Both curves were input into the treatment planning system (TPS) for dose calculation. Last, the PTV and OAR doses, DVH and dose distributions of CBCT plans are compared to the original treatment plan. Results: One head-and-neck cases which contained a pair of PCT and CBCT was used. The dose differences between the PCT and CBCT plans using the proposed method are −1.33% for the mean PTV, 0.06% for PTV D95%, and −0.56% for the left neck. The dose differences between plans of PCT and CBCT corrected using the CATPhan based method are −4.39% for mean PTV, 4.07% for PTV D95%, and −2.01% for the left neck. Conclusion: The proposed CBCT HU correction method achieves better agreement with the original treatment plan compared to the traditional CATPhan based calibration method.« less