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

Title: SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

Abstract

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessingmore » the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.« less

Authors:
; ; ; ;  [1]
  1. UCLA Radiological Sciences, Los Angeles, CA (United States)
Publication Date:
OSTI Identifier:
22649306
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 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; 61 RADIATION PROTECTION AND DOSIMETRY; BIOMEDICAL RADIOGRAPHY; CLINICAL TRIALS; IMAGES; PERFORMANCE; PHANTOMS

Citation Formats

Wahi-Anwar, M, Lo, P, Kim, H, Brown, M, and McNitt-Gray, M. SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials. United States: N. p., 2016. Web. doi:10.1118/1.4956942.
Wahi-Anwar, M, Lo, P, Kim, H, Brown, M, & McNitt-Gray, M. SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials. United States. doi:10.1118/1.4956942.
Wahi-Anwar, M, Lo, P, Kim, H, Brown, M, and McNitt-Gray, M. Wed . "SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials". United States. doi:10.1118/1.4956942.
@article{osti_22649306,
title = {SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials},
author = {Wahi-Anwar, M and Lo, P and Kim, H and Brown, M and McNitt-Gray, M},
abstractNote = {Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.},
doi = {10.1118/1.4956942},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: The purpose of this study was to: 1) compare scanners output by measuring normalized CTDIw (mGy/100mAs) in different CT makes and models and at different kV’s, and 2) quantify the relationship between kV and CTDI and compare this relationship between the different manufacturers. Methods: Study included forty scanners of major CT manufacturers and of various models. Exposure was measured at center and 12 o’clock holes of head and body CTDI phantoms, at all available kV’s, and with the largest or second largest available collimation in each scanner. Average measured CTDI’s from each CT manufacturer were also plotted against kVmore » and the fitting equation: CTDIw (normalized) = a.kVb was calculated. The power (b) value may be considered as an indicator of spectral filtration, which affects the degree of beam hardening. Also, HVLs were measured at several scanners. Results: Results showed GE scanners, on average, had higher normalized CTDIw than those of Siemens and Philips, in both phantom sizes and at all kV’s. ANOVA statistic indicated the difference was statistically significant (p < 0.05). Comparison between Philips and Siemens, however, was not statistically significant. Curve fitting showed b values ranged from 2.4 to 2.9 (for Head periphery and center, respectively); and was about 2.8 for Body phantom periphery, and 3.2 at the center of Body phantom. Fitting equations (kV vs. CTDI) will be presented and discussed. GE’s CTDIw vs. HVL showed very strong correlation (r > 0.99). Conclusion: Partial characterization of scanners output was performed which may be helpful in dose estimation to internal organs. The relatively higher output from GE scanners may be attributed to lower filtration. Work is still in progress to obtain CTDI values from other scanners as well as to measure their HVLs.« less
  • Purpose: Localizer projection radiographs acquired prior to CT scans are used to estimate patient size, affecting the function of Automatic Tube Current Modulation (ATCM) and calculation of the Size Specific Dose Estimate (SSDE). Due to geometric effects, the projected patient size varies with scanner table height and with the orientation of the localizer (AP versus PA). Consequently, variations in scanner table height may affect both CTDIvol and the calculated size-corrected dose index (SSDE). This study sought to characterize these effects. Methods: An anthropomorphic phantom was imaged using an AP localizer, followed by a diagnostic scan using ATCM and our institution’smore » routine abdomen protocol. This was repeated at various scanner table heights, recording the scanner-reported CTDIvol for each diagnostic scan. The width of the phantom was measured from the localizer and diagnostic images using in-house software. The measured phantom width and scanner-reported CTDIvol were used to calculate SSDE. This was repeated using PA localizers followed by diagnostic scans. Results: 1) The localizer-based phantom width varied by up to 54% of the nominal phantom width between minimum and maximum table heights. 2) Changing the table height caused a variation in scanner-reported CTDIvol of a factor greater than 4.6 when using a PA localizer and almost 2 when using an AP localizer. 3) SSDE, calculated from measured phantom size and scanner-reported CTDIvol, varied by a factor of more than 2.8 when using a PA localizer and almost 1.5 when using an AP localizer. Conclusion: Our study demonstrates that off-center patient positioning affects the efficacy of ATCM, more severely when localizers are acquired in the PA rather than AP projection. Further, patient positioning errors can cause a large variation in the calculated SSDE. This hinders interpretation of SSDE for individual patients and aggregate SSDE data when evaluating CT protocols and clinical practices.« less
  • The medical linear accelerator (linac) integrated with a kilovoltage (kV) flat-panel imager has been emerging as an important piece of equipment for image-guided radiation therapy. Due to the sagging of the linac head and the flexing of the robotic arms that mount the x-ray tube and flat-panel detector, geometric nonidealities generally exist in the imaging geometry no matter whether it is for the two-dimensional projection image or three-dimensional cone-beam computed tomography. Normally, the geometric parameters are established during the commissioning and incorporated in correction software in respective image formation or reconstruction. A prudent use of an on-board imaging system necessitatesmore » a routine surveillance of the geometric accuracy of the system like the position of the x-ray source, imager position and orientation, isocenter, rotation trajectory, and source-to-imager distance. Here we describe a purposely built phantom and a data analysis software for monitoring these important parameters of the system in an efficient and automated way. The developed tool works equally well for the megavoltage (MV) electronic portal imaging device and hence allows us to measure the coincidence of the isocenters of the MV and kV beams of the linac. This QA tool can detect an angular uncertainty of 0.1 deg. of the x-ray source. For spatial uncertainties, such as the source position, the imager position, or the kV/MV isocenter misalignment, the demonstrated accuracy of this tool was better than 1.6 mm. The developed tool provides us with a simple, robust, and objective way to probe and monitor the geometric status of an imaging system in a fully automatic process and facilitate routine QA workflow in a clinic.« less
  • Purpose: To study the deformation effects in CT/MR image registration of head and neck (HN) cancers. We present a clinical indication in guiding and simplifying registration procedures of this process while CT images possessed artifacts. Methods: CT/MR image fusion provides better soft tissue contrast in intracranial GTV definition with artifacts. However, whether the fusion process should include the deformation process is questionable and not recommended. We performed CT/MR image registration of a HN patient with tonsil GTV and nodes delineation on Varian Velocity™ system. Both rigid transformation and deformable registration of the same CT/MR imaging data were processed separately. Physician’smore » selection of target delineation was implemented to identify the variations. Transformation matrix was shown with visual identification, as well as the deformation QA numbers and figures were assessed. Results: The deformable CT/MR images were traced with the calculated matrix, both translation and rotational parameters were summarized. In deformable quality QA, the calculated Jacobian matrix was analyzed, which the min/mean/max of 0.73/0/99/1.37, respectively. Jacobian matrix of right neck node was 0.84/1.13/1.41, which present dis-similarity of the nodal area. If Jacobian = 1, the deformation is at the optimum situation. In this case, the deformation results have shown better target delineation for CT/MR deformation than rigid transformation. Though the root-mean-square vector difference is 1.48 mm, with similar rotational components, the cord and vertebrae position were aligned much better in the deformable MR images than the rigid transformation. Conclusion: CT/MR with/without image deformation presents similar image registration matrix; there were significant differentiate the anatomical structures in the region of interest by deformable process. Though vendor suggested only rigid transformation between CT/MR assuming the geometry remain similar, our findings indicated with patient positional variations, deformation registration is needed to generate proper GTV coverage, which will be irradiated more accurately in the following boost phase.« less
  • Purpose: Computed tomography (CT) exam rooms are shielded more quickly and accurately compared to manual calculations using RadShield, a semi-automated diagnostic shielding software package. Last year, we presented RadShield’s approach to shielding radiographic and fluoroscopic rooms calculating air kerma rate and barrier thickness at many points on the floor plan and reporting the maximum values for each barrier. RadShield has now been expanded to include CT shielding design using not only NCRP 147 methodology but also by overlaying vendor provided isodose curves onto the floor plan. Methods: The floor plan image is imported onto the RadShield workspace to serve asmore » a template for drawing barriers, occupied regions and CT locations. SubGUIs are used to set design goals, occupancy factors, workload, and overlay isodose curve files. CTDI and DLP methods are solved following NCRP 147. RadShield’s isodose curve method employs radial scanning to extract data point sets to fit kerma to a generalized power law equation of the form K(r) = ar^b. RadShield’s semiautomated shielding recommendations were compared against a board certified medical physicist’s design using dose length product (DLP) and isodose curves. Results: The percentage error found between the physicist’s manual calculation and RadShield’s semi-automated calculation of lead barrier thickness was 3.42% and 21.17% for the DLP and isodose curve methods, respectively. The medical physicist’s selection of calculation points for recommending lead thickness was roughly the same as those found by RadShield for the DLP method but differed greatly using the isodose method. Conclusion: RadShield improves accuracy in calculating air-kerma rate and barrier thickness over manual calculations using isodose curves. Isodose curves were less intuitive and more prone to error for the physicist than inverse square methods. RadShield can now perform shielding design calculations for general scattering bodies for which isodose curves are provided.« less