ACR CT Phantom Research Articles

A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability

Purpose. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images. Method. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between −980 HU (Hounsfield units) and −1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images. Results. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with R2 values of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves. Conclusion. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.

Algorithm development for automatic laser alignment assessment on an ACR CT phantom and its evaluation on sixteen CT scanners

Purpose. The aim of this study is to develop software to automatically assess the laser alignment on the ACR CT phantom and evaluate its accuracy on sixteen CT scanners. Methods. Software for an automated method of laser alignment assessment on the ACR CT phantom was developed. Laser alignment assessment was based on the positions of the ball-bearing markers at the edge of the ACR CT phantom. The automatic assessment was performed using several steps, including segmentation to acquire the coordinates of the ball-bearing markers and determination of the distances between lines connecting them with lines through the center of the image. A comparison of the results from the automatic method with those from the manual method was performed. The manual measurements were carried out using MicroDicom Viewer. A Mann–Whitney U test was performed to determine the statistical difference between both methods. The evaluation was performed on images of the ACR CT phantom scanned with 16 CT scanners from 5 different CT manufacturers. Results. The results confirmed that our software successfully segments the ball-bearing markers and determines the laser alignment assessment on the ACR CT phantom. Evaluation of the algorithm with images from the 16 CT scanners revealed that the difference between the results from automatic and manual methods were about 0.2 mm with a p value of around 0.7 (no statistical difference). Misalignment in the y-axis was larger than the misalignment in the x-axisfor the majority of the scanners tested. It was found that the phantom tended to be placed 2 mm higher than the iso-center. Conclusions. Software to automatically assess CT laser alignment with the ACR CT phantom was successfully developed and evaluated. The automatic assessment was comparable to manual assessment. In addition, the automatic method was user independent and fast.

Establishing a quality assurance program for photon counting detector (PCD) CT: Tips and caveats.

To determine the suitability of a quality assurance (QA) program based on the American College of Radiology's (ACR) CT quality control (QC) manual to fully evaluate the unique capabilities of a clinical photon-counting-detector (PCD) CT system. A daily QA program was established to evaluate CT number accuracy and artifacts for both standard and ultra-high-resolution (UHR) scan modes. A complete system performance evaluation was conducted in accordance with the ACR CT QC manual by scanning the CT Accreditation Phantom with routine clinical protocols and reconstructing low-energy-threshold (T3D) and virtual monoenergetic images (VMIs) between 40 and 120keV. Spatial resolution was evaluated by computing the modulation transfer function (MTF) for the UHR mode, and multi-energy performance was evaluated by scanning a body phantom containing four iodine inserts with concentrations between 2 and 15mg I/cc. The daily QA program identified instances when the detector needed recalibration or replacement. CT number accuracy was impacted by image type: CT numbers at 70keV VMI were within the acceptable range (defined for 120kV). Other keV VMIs and the T3D reconstruction had at least one insert with CT number outside the acceptable range. The limiting resolution was nearly 40 lp/cm based on MTF measurements, which far exceeds the 12 lp/cm maximum capability of the ACR phantom. The CT numbers in the iodine inserts were accurate on all VMIs (3.8% average percentage error), while the iodine concentrations had an average root mean squared error of 0.3mg I/cc. Protocols and parameters must be properly selected on PCD-CT to meet current accreditation requirements with the ACR CT phantom. Use of the 70keV VMI allowed passing all tests prescribed in the ACR CT manual. Additional evaluations such an MTF measurement and multi-energy phantom scans are also recommended to comprehensively evaluate PCD-CT scanner performance.

Technical Note: Scanner dependence of adaptive statistical iterative reconstruction with 3D noise power spectrum central frequency and noise magnitude ratios.

In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. The homogeneous module of the ACR CT phantom was scanned on the 64 and 256slices CT scanners from the same vendor in the range of 15-40mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0mGy, but there was no observable dose-dependence on the 64-slice. Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.

COMPARISON OF DIFFERENT APPROACHES TO THE DIAGNOSTIC IMAGE QUALITY ASSESSMENT FOR THE CHEST COMPUTED TOMOGRAPHY

The aim of the current study was to assess the physical and technical image quality parameters of the CT images obtained on low-dose chest protocols. Materials and methods. The study was performed using the ACR CT PHANTOM on the Philips Ingenuity 128 CT unit. The following parameters were selected for the assessment of image quality: Hounsfield unit accuracy, image noise, contrast to noise ratio and spatial resolution. The results of the study indicate the lack of significant differences between the selected low-dose protocols. Additionally, the correlation between the results of the assessment of physical and technical parameters of CT images and expert image quality evaluation was weak. That does not allow implementing the low-dose protocols into clinical practice based solely on the assessment of the physical and technical parameters.

The influence of focal spot blooming on high-contrast spatial resolution in CT imaging.

The objective of this work was to investigate focal spot blooming effects on the spatial resolution of CT images and to evaluate an x-ray tube that uses dynamic focal spot control for minimizing focal spot blooming. The influence of increasing tube current at a fixed tube potential of 80 kV on high-contrast spatial resolution of seven different CT scanner models (scanners A-G), including one scanner that uses dynamic focal spot control to reduce focal spot blooming (scanner A), was evaluated. Spatial resolution was assessed using a wire phantom for the modulation transfer function (MTF) calculation and a copper disc phantom for measuring the slice sensitivity profile (SSP). The impact of varying the tube potential was investigated on two scanner models (scanners A and B) by measuring the MTF and SSP and also by using the resolution bar pattern module of the ACR CT phantom. The phantoms were scanned at 70-150 kV on scanner A and 80-140 kV on scanner B, with tube currents from 100 mA up to the maximum tube current available on each scanner. The images were reconstructed using a slice thickness of 0.6 mm with both smooth and sharp kernels. Additionally, focal spot size at varying tube potentials and currents was directly measured using pinhole and slit camera techniques. Evaluation of the MTF and SSP data from the 7 CT scanner models evaluated demonstrated decreased focal spot blooming for newer scanners, as evidenced by decreasing deviations in MTF and SSP as tube current varied. For scanners A and B, where focal spot blooming effects as a function of tube potential were assessed, the spatial resolution variation in the axial plane was much smaller on scanner A compared to scanner B as tube potential and current changed. On scanner A, the 50% MTF never decreased by more than 2% from the 50% MTF measured at 100 mA. On scanner B, the 50% MTF decreased by as much as 19% from the 50% MTF measured at 100 mA. Assessments of the SSP, the bar patterns in the ACR phantom and the pinhole and slit camera measurements were consistent with the MTF calculations. Focal spot blooming has a noticeable effect on spatial resolution in CT imaging. The focal spot shaping technology of scanner A greatly reduced blooming effects.

SU‐E‐I‐89: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Pediatric Anthropomorphic and ACR Phantoms

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. 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.

SU‐E‐I‐81: Assessment of CT Radiation Dose and Image Quality for An Automated Tube Potential Selection Algorithm Using Adult Anthropomorphic and ACR Phantoms

Purpose:To assess the impact of General Electrics (GE) 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 an adult anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, Auto mA (180 to 380 mA), noise index (NI) = 14, adaptive iterative statistical reconstruction (ASiR) of 20%, 0.8s rotation time. Image quality was evaluated 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. 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:The CNR for the adult male was found to decrease from CNR = 0.912 ± 0.045 for the baseline protocol without kVa to a CNR = 0.756 ± 0.049 with kVa 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 = 0.903 ± 0.023. The difference in the central liver dose with and without kVa was found to be 0.07%.Conclusion:Dose reduction was insignificant in the adult phantom. As determined by NPS analysis, ASiR of 40% produced images with similar noise texture to the baseline protocol. However, the CNR at ASiR of 40% with kVa fails to meet the current ACR CNR passing requirement of 1.0.

TH-C-18A-07: A Software Tool for Automated Analysis of CT Quality Assurance Phantoms

Purpose: Automated software can save time, reduce cost and minimize human error in the analysis of quality-assurance phantom images. The goal of this work was to develop a software tool to facilitate an efficient, enterprise-wide, CT quality-assurance program and to perform the daily water QC tests recommended by the ACR. Methods: The QC software was developed in Matlab and includes tools for automating analysis of the daily water phantom and all four modules of the ACR CT phantom. The daily water phantom analysis follows guidelines from the ACR's 2012 CT quality control manual, which requires ROI analysis in the center and leading/trailing image of an image stack of both axial and helical acquisitions. A DICOM receiver on a dedicated workstation was configured to run the software automatically after receiving phantom images from a scanner. Results are added to a Matlab structure that serves as a database for tracking data trends, and a standardized report is automatically generated. Performance of the automated ACR phantom analysis was compared to that of trained observers for two tasks requiring subjective user decisions (slice thickness and high-contrast spatial resolution tasks). Images with multiple slice thicknesses and reconstruction kernels were evaluated by two observers and their results compared to the automated analyses. Results: As part of an ongoing pilot study, preliminary tests of the software have been performed on two scanners. Results are available within seconds of scanning the QC phantom. In the human observer study, the software-estimated slice thickness was within +/− 0.5 mm and the highest frequency bar pattern was within +/− 1 line pairs/cm. Conclusion: The developed software provides a robust tool for efficiently managing quality assurance of a large number of scanners. The workflow features an efficient web-based design that automatically analyzes QC images sent from the scanner and generates user-customizable reports.

SU‐E‐I‐12: Assessment of Absorbed Dose and Image Quality Using Cone Beam CT On An X‐Ray Angiography System

Purpose: Cone beam CT (CBCT) acquisitions using interventional X‐ray Angiography (XA) systems are becoming more prevalent. Patient dose should be considered when performing these studies. This work assesses absorbed radiation dose and image quality from CBCT acquisitions on an XA system. Methods: CBCT acquisitions of a CIRS tissue equivalent abdomen phantom and the Gammex ACR CT phantom were performed at 3 detector dose settings using a 90 kVp, 210 degree, 420 view, 8 second acquisition on a Siemens Artis Zee system. Radiation dose was measured using a Radcal 0.6cc ionization chamber positioned at central and peripheral locations in the abdomen phantom. Patient absorbed dose was estimated via a weighted average of the peripheral (2/3) and central (1/3) doses. Image quality was assessed via the standard deviation of the reconstructed image pixel values in a homogeneous region of the ACR CT phantom and contrast to noise ratio (CNR) measurements of the polyethylene rod to the background. For comparison, absorbed dose was measured with the same setup on a standard CT system using a clinical abdomen and pelvis protocol. Results: Estimates of patient absorbed dose for the CBCT acquisitions were 38, 26, and 15 mGy at detector dose settings of 540, 360 (default) and 200 nGy per view. The image noise at different dose settings was consistent with a 0.5 power law relationship. The absolute value of the CNR was determined to be 6.1 (540 nGy), 5.5 (360 nGy) and 3.7(200 nGy). The absorbed dose estimate for the abdomen and pelvis standard CT was 9 mGy. Conclusion: Absorbed dose from cone beam CT acquisitions on an interventional XA system is greater than that of a standard CT. The necessary image quality for a given CBCT imaging task and the potential for dose reduction should be assessed in accordance with ALARA principles. Research Grant: Siemens AG

TU‐C‐103‐12: An Experimental Study of the Effects of Different Beam‐Hardening Filters On CTDIvol and Low‐Contrast Image Detectability in a CT Scanner

Purpose: To study the effects of different beam‐hardening filters on CTDIvol and low‐contrast image detectability in a CT scanner Methods: The default Molybdenum beam‐hardening filter of GE HiSpeed CT/i scanner was replaced with three different types of filters, having K‐edges in low, middle, and high diagnostic energy range. The thickness of each filter was chosen such that it approximated Molybdenum energy‐equivalent thickness for the simulated mean energy of X‐ray spectrum generated at 120 kVp. The scanner calibration routine was run for each filter prior to image acquisition to ensure optimal image quality (optimized signal‐to‐noise ratio, optimized contrast‐to‐noise ratio, and no noticeable ring artifacts). An ACR CT phantom was imaged under a routine axial head protocol to evaluate the CNR for a wide range of mAs. Next, the head dose phantom was scanned to obtain CTDIvol for the same mAs range. The beam quality was also recorded. Filters were compared according to measured CNR, and CTDIvol for the smallest mAs providing CNR > 1 (= 200 mAs). Results: Examination of CNRs indicates no significant difference among filters. Evaluation of NPS for raw images acquired from different filters will address the image noise. CTDIvol measurements indicate that the filters attenuate the beam differently due to their K‐edge energies, and copper, having a low‐energy K‐edge (= 8.98 keV), provides the minimum CTDIvol. Conclusion: CNR is a function of CTDIvol and better addressed when accompanied by CTDIvol. The current project has the potential for selecting a new beam‐hardening filter to possibly reduce patient dose without compromising low‐contrast detectability. This work is in progress to optimize CNR by investigating other available kVps, filters, measured energy spectra, NPS, and K‐edge effect.

WE‐C‐103‐06: An Automated CT Quality Control Program

Purpose: Daily image quality assessment is an essential part of a rigorous quality control program. Time constraints; however, make it difficult to fully analyze image quality daily. Therefore, the goal of this work was to develop a robust automated CT quality control program to extract meaningful image quality metrics including artifact analysis, noise texture measurement, and the calculation of a detectability index. Methods: The ACR CT phantom was scanned on five different CT scanners. The automated algorithm was used to calculate the standard metrics including HU accuracy, CNR, noise, and uniformity as well as more advanced metrics including the MTF, NPS, detectability index, and artifact detection. To validate the automated program, the HU accuracy, CNR, and noise metrics were compared to measurements conducted by a human observer. Additionally, the 10th percentile of the MTF (MTF 10) was compared the high‐contrast resolution and the detectability index was compared to the low‐contrast detectability recorded by the human observer using the Spearman rank sum correlation. Results: The HU, CNR, and noise determined by the automated algorithm agreed well with the human observer measurements (0.12%, 5.1%, and 7.1% difference for the HU, CNR, and noise respectively). Further, there was a strong correlation between MTF10 and the observer high‐contrast resolution as well as the detectability index and the low‐contrast detectability (rho=1 in both cases). Conclusion: There was a strong agreement between the results of the automated quality control program and the human observer measurements. Further, the Fourier based MTF and detectability index were found to correlate strongly with observer image quality assessment. Therefore, this automated quality control program offers a viable alternative for routine image quality assessment of the ACR phantom.

TH-E-217BCD-03: Image Contrast Dependent Spatial Resolution Differences for Varying Levels of ASiR™ Implementation

Purpose: Introduce MTF calculation technique for contrast dependent spatial resolution measurements of ASiR™ using the first module of the ACR CT phantom. Compare three approaches to account for spatially variant MTF for CT.Methods: Each material insert within module one of the ACR phantom was imaged with a pixel pitch of 0.188. An MTF methodology described by Boone was used to validate the use of the ACR phantom. To account for spatially variant MTF of CT, three MTF calculation methodologies were investigated: an ensemble of edge response functions (ERFs) were aligned and averaged to produce one MTF;MTF curves were calculated for each ERF and averaged into one; and a cumulative distribution function was created by integrating a 300 bin histogram for one MTF calculation. Three reconstruction filters were investigated: standard, bone, and lung.Results: ACR phantom and Boone methodology MTF curves agreed to within 12%. Three methodologies investigated to account for spatially variant CT agreed to within 1%; histogram methodology was susceptible to number of bins used to form the histogram, and was not reliable. High‐contrast resolution improved by 10% when 100% ASiR™ was compared to 0% ASiR™; this trend agreed with previous reports and with Boone methodology calculations. Lung and bone reconstruction filters measured increased spatial resolution with increased levels of ASiR™: 10% and 17% for 0 and 100% ASiR™, respectively. Intermediary contrast resolution measured at acrylic‐water and at polyethylene‐water interfaces did not change with increasing ASiR™ reconstruction. Conclusions: ACR phantom MTF calculations agreed with the Boone methodology. Of the three methodologies investigated to account for spatially variant MTF in CT, method three varied depending on sampling interval. MTF values for high‐contrast objects were found to improve between 10–17% with 100% ASiR™ reconstruction. ASiR™ did not affect intermediary contrast object spatial resolution.

SU‐GG‐I‐46: An Automatic Region Detection Algorithm for Analyzing Module 1 of the ACR CT Accreditation Phantom

Purpose: A region identification algorithm has been developed to facilitate the automatic analysis of Module 1 of the ACR CT accreditation phantom. Method and Materials: The ACR CT accreditation phantom was scanned using a GE Lightspeed QX/i scanner, and a preliminary algorithm was developed to identify the centers of the air and bone‐mimicking areas in Module 1 of the phantom. The air and the “bone” regions in the CT image were first isolated by binarizing the image with a threshold of −500 and 800 HU, respectively. Next, a Canny edge detector was applied to the air‐only binary images to detect the edges, and the center of the phantom was then identified using a Hough‐based circular object detection algorithm. Lastly, the edge of the phantom was removed and the center of the air‐only region is subsequently detected. A similar procedure was used to identify the center of the “bone” region. Results: To assess the algorithm, the CT image from Module 1 of the ACR CT phantom was computationally rotated to various orientations. The centers of the air and “bone” regions were then located using the preliminary algorithm. The resulting averages for both regions were very reproducible (within 1 HU). Conclusion: The preliminary results above have demonstrated that the algorithm can robustly identify the centers of the air and bone regions. Once the centers of these two regions are located, the required ROIs can be extracted from the original CT image, and the required statistics computed. Since the phantom is a rigid object, this implies that the other areas of interest in Module 1 can be easily identified. This will be demonstrated in the presentation.

WE-A-M100J-01: ACR Application Process: Pitfalls to Avoid

This presentation will discuss the phantom physics tests required for ACR CT Accreditation. In particular it will focus on pitfalls to avoid in setting up the special ACR CT Phantom and performing the tests. In order to successfully complete all 0of the imaging tests, it is helpful to understand the internal construction of the ACR Phantom and it proper use. To that end, the details of the ACR CT Phantom construction will be presented. The most frequent failure is improper alignment of the phantom. Helpful methods of proper phantom alignment will be presented with examples of correct and incorrect images. Another area of difficulty is determining and implementing appropriate “Axial” CT scan protocols as substitutes for “Helical” protocols. Examples for several different types of CT scanners (multiple detector row) and different manufacturers will be presented. The ACR CT Phantom has 4 different imaging sections; the proper scanning of each of these sections will be presented. Commonly seen problems in imaging these sections will be presented. Finally, the radiation dose measurement process required for the ACR Physics submission will be discussed, again with commonly observed problems.

TU‐D‐L100J‐04: Respiration Motion Correction in Helical Tomotherapy Imaging

Purpose: In helical tomotherapy, Megavoltage CT (MVCT) images are acquired at a rate of one transverse slice per five seconds. The slow acquisition of raw data results in CT images with varying degrees of motion‐induced artifacts. In addition, the high energy of the x‐ray beam (4–6 MV) presents unique challenges for image reconstruction. The purpose of this work was to correct for motion in the superior‐inferior (S/I) direction using retrospective manipulation of the unprocessed sinogram data. Method and Materials: A respiratory motion platform with a S/I motion range of 3‐cm, and periods of 2, 4 and 6 seconds was used to move an ACR CT accreditation phantom and a anthropomorphic thorax phantom in the superior‐inferior direction. The motion of the respiratory platform was measured using the Varian respiratory gating system. Sinogram data was acquired axially with the couch stationary for 5 complete rotations of the gantry. The respiration cycle was synchronized to the projections in the raw sinogram so that the position of the moving object was known in each projection. Motion correction was performed by identifying and extracting projections occurring within a specified respiration phase, correcting for missing projections, and then reconstructing the CT images. Results: Images acquired with the ACR CT phantom showed considerable improvement with motion correction using temporal re‐binning over the uncorrected images. Images acquired with the RANDO phantom suffered from greater image artifacts than the ACR phantom because of the larger variation in the phantom's anatomy over the 3‐cm range of S/I motion. Improvement was seen for each period, however only slight improvement was seen for the 2‐second period of motion. Conclusion: Temporal re‐binning can successfully reduce S/I motion artifacts in reconstructed MVCT images. Conflict of Interest: This research is supported by TomoTherapy, Inc.

SU-FF-I-06: A Motion Phantom System For Image Quality Assessment of a 64-Slice Volume CT Scanner

Purpose: As the utilization of cardiac CT grows, the development of the testing tools to assess the image quality parameters that are associated with cardiac CT protocols is lagging behind. This paper presents a motion system that was designed to provide the moving imaging targets by rotating the CT phantom in order to simulate the heart beat; thus evaluate the performance of a cardiac CT system. Methods and Materials: To assess the image quality of a ultra-fast/thin slice VCT (GE LightSpeed), we designed a motion device that was capable of rotating CT phantoms at a controlled rotation speed ranging from 0 to 30 rpm. The apparatus consisted of radiation translucent support plates, gear motor, speed controller, rpm sensor, gears and shafts. This motion phantom system was designed to assess the additional image quality parameters associated with cardiac CT protocols such as the motion artifact occurrence, the loss of spatial resolution due to motion, temporal resolution and isotropic resolution during gated and non-gated protocols. Results: The motion phantom system was designed and made in-house. The flexibility of the system allowed its usage with multiple existing CT phantoms including the ACR CT phantom, CATPHAN, CIRS Helical CT phantom and AAPM CT performance phantom. By scanning the phantom in stationary and in various rotation speeds, we were able to examine the performance parameters of the VCT scanner and optimize the clinical protocols with regards to slice thickness selections, detector row combinations, pitch and other technique settings. Conclusions: The motion phantom system is an essential tool to evaluate the image quality and verify the manufacturer specifications on temporal resolution and isotropic resolution which should reveal the true capability of a volume CT scanner. This motion system is also useful in optimizing the clinical protocols.