HU Accuracy Research Articles

SU‐E‐I‐02: Characterizing Low‐Contrast Resolution for Non‐Circular CBCT Trajectories

Purpose:The use of non‐circular scanning trajectories with optimization‐basedreconstruction algorithms can be used in conjunction with non‐planaracquisition geometries for axial field‐of‐view (FOV) extension incone‐beam CT (CBCT). To evaluate the utility of these trajectories,quantitative image quality metrics should be evaluated. Low‐contrastresolution (LCR) and CT number accuracy are significant challenges forCBCT. With unprecedented axial coverage provided by thesetrajectories, measuring such metrics throughout the axial range iscritical. There are currently no phantoms designed to measurelow‐contrast resolution over such an extended volume.Methods:The CATPHAN (The Phantom Laboratory, Salem NY) is the current standardfor image quality evaluation. While providing several useful modulesfor different evaluation metrics, each module was designed to beevaluated in a single slice and not for comparison across axialpositions. To characterize the LCR and HU accuracy over an extendedaxial length, we have designed and built a phantom with evaluationmodules at multiple and adjustable axial positions.Results:The modules were made from a cast polyurethane resin. Holes rangingfrom 1/8 to 5/8 inch were added at a constant radius from the modulecenter into which rods of two different plastic materials were pressedto provide two nominal levels of contrast (1.0% and 0.5%). Largerholes were bored to accept various RMI plugs with known electrondensities for HU accuracy evaluation. The modules can be inserted intoan acrylic tube long enough to cover the entire axial FOV and theirpositions adjusted to desired evaluation points.Conclusion:This phantom allows us to measure the LCR and HU accuracy across theaxial coverage within a single acquisition. These metrics can be usedto characterize the impact different trajectories and reconstructionparameters have on clinically relevant image quality performancemetrics.Funding was provided in part by Varian Medical Systems and NIH R01 Grants Nos. CA158446, CA182264, EB018102, and EB000225. The contents of this poster are solely the responsibility of the authors and do not necessarily represent the official view of any of the supporting organizations.

Lesion Insertion in Projection Domain for Computed Tomography Image Quality Assessment.

To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way to achieve this objective is to create hybrid images that combine patient images with simulated lesions. Because conventional hybrid images generated in the image-domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Liver lesion models were forward projected according to the geometry of a commercial CT scanner to acquire lesion projections. The lesion projections were then inserted into patient projections (decoded from commercial CT raw data with the assistance of the vendor) and reconstructed to acquire hybrid images. To validate the accuracy of the forward projection geometry, simulated images reconstructed from the forward projections of a digital ACR phantom were compared to physically acquired ACR phantom images. To validate the hybrid images, lesion models were inserted into patient images and visually assessed. Results showed that the simulated phantom images and the physically acquired phantom images had great similarity in terms of HU accuracy and high-contrast resolution. The lesions in the hybrid image had a realistic appearance and merged naturally into the liver background. In addition, the inserted lesion demonstrated reconstruction-parameter-dependent appearance. Compared to conventional image-domain approach, our method enables more realistic hybrid images for image quality assessment.

SU‐E‐J‐175: Proton Dose Calculation On Scatter‐Corrected CBCT Image: Feasibility Study for Adaptive Proton Therapy

Purpose:To demonstrate feasibility of proton dose calculation on scattercorrected CBCT images for the purpose of adaptive proton therapy.Methods:Two CBCT image sets were acquired from a prostate cancer patient and a thorax phantom using an on‐board imaging system of an Elekta infinity linear accelerator. 2‐D scatter maps were estimated using a previously introduced CT‐based technique, and were subtracted from each raw projection image. A CBCT image set was then reconstructed with an open source reconstruction toolkit (RTK). Conversion from the CBCT number to HU was performed by soft tissue‐based shifting with reference to the plan CT. Passively scattered proton plans were simulated on the plan CT and corrected/uncorrected CBCT images using the XiO treatment planning system. For quantitative evaluation, water equivalent path length (WEPL) was compared in those treatment plans.Results:The scatter correction method significantly improved image quality and HU accuracy in the prostate case where large scatter artifacts were obvious. However, the correction technique showed limited effects on the thorax case that was associated with fewer scatter artifacts. Mean absolute WEPL errors from the plans with the uncorrected and corrected images were 1.3 mm and 5.1 mm in the thorax case and 13.5 mm and 3.1 mm in the prostate case. The prostate plan dose distribution of the corrected image demonstrated better agreement with the reference one than that of the uncorrected image.Conclusion:A priori CT‐based CBCT scatter correction can reduce the proton dose calculation error when large scatter artifacts are involved. If scatter artifacts are low, an uncorrected CBCT image is also promising for proton dose calculation when it is calibrated with the soft‐tissue based shifting.

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.

A methodology for image quality evaluation of advanced CT systems

This work involved the development of a phantom-based method to quantify the performance of tube current modulation and iterative reconstruction in modern computed tomography (CT) systems. The quantification included resolution, HU accuracy, noise, and noise texture accounting for the impact of contrast, prescribed dose, reconstruction algorithm, and body size. A 42-cm-long, 22.5-kg polyethylene phantom was designed to model four body sizes. Each size was represented by a uniform section, for the measurement of the noise-power spectrum (NPS), and a feature section containing various rods, for the measurement of HU and the task-based modulation transfer function (TTF). The phantom was scanned on a clinical CT system (GE, 750HD) using a range of tube current modulation settings (NI levels) and reconstruction methods (FBP and ASIR30). An image quality analysis program was developed to process the phantom data to calculate the targeted image quality metrics as a function of contrast, prescribed dose, and body size. The phantom fabrication closely followed the design specifications. In terms of tube current modulation, the tube current and resulting image noise varied as a function of phantom size as expected based on the manufacturer specification: From the 16- to 37-cm section, the HU contrast for each rod was inversely related to phantom size, and noise was relatively constant (<5% change). With iterative reconstruction, the TTF exhibited a contrast dependency with better performance for higher contrast objects. At low noise levels, TTFs of iterative reconstruction were better than those of FBP, but at higher noise, that superiority was not maintained at all contrast levels. Relative to FBP, the NPS of iterative reconstruction exhibited an ~30% decrease in magnitude and a 0.1 mm(-1) shift in the peak frequency. Phantom and image quality analysis software were created for assessing CT image quality over a range of contrasts, doses, and body sizes. The testing platform enabled robust NPS, TTF, HU, and pixel noise measurements as a function of body size capable of characterizing the performance of reconstruction algorithms and tube current modulation techniques.

Towards task-based assessment of CT performance: System and object MTF across different reconstruction algorithms

To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast. An algorithm was developed to extract the task-based modulation transfer function (MTF) from disk images generated from the rod inserts in the ACR phantom (model 464 Gammex, WI). These inserts are conventionally employed for HU accuracy assessment. The edge of the disk objects was analyzed to determine the edge-spread function, which was differentiated to yield the line-spread function and Fourier-transformed to generate the object-specific MTF for task-based assessment, denoted MTF(Task). The proposed MTF measurement method was validated against the conventional wire technique and further applied to measure the MTF of CT images reconstructed with an adaptive statistical iterative algorithm (ASIR) and a model-based iterative (MBIR) algorithm. Results were further compared to the standard filtered back projection (FBP) algorithm. Measurements were performed and compared across different doses and contrast levels to ascertain the MTF(Task) dependencies on those factors. For the FBP reconstructed images, the MTF(Task) measured with the inserts were the same as the MTF measured from the wire-based method. For the ASIR and MBIR data, the MTF(Task) using the high contrast insert was similar to the wire-based MTF and equal or superior to that of FBP. However, results for the MTF(Task) measured using the low-contrast inserts, the MTF(Task) for ASIR and MBIR data was lower than for the FBP, which was constant throughout all measurements. Similarly, as a function of mA, the MTF(Task) for ASIR and MBIR varied as a function of noise--with MTF(Task) being proportional to mA. Overall greater variability of MTF(Task) across dose and contrast was observed for MBIR than for ASIR. This approach provides a method for assessing the task-based MTF of a CT system using conventional and iterative reconstructions. Results demonstrated that the object-specific MTF can vary as a function of dose and contrast. The analysis highlighted the paradigm shift for iterative reconstructions when compared to FBP, where iterative reconstructions generally offer superior noise performance but with varying resolution as a function of dose and contrast. The MTF(Task) generated by this method is expected to provide a more comprehensive assessment of image resolution across different reconstruction algorithms and imaging tasks.

Untersuchung zur geometrischen 3-D-Genauigkeit und zur Bildqualität (MTF, SRV und W) von Volumentomografie-Einrichtungen (CT, CBCT und DVT)

The study aims at investigating how far image quality (MTF and NPS) differs in between CT, CBCT and DVT units and how far the geometrical 3 D accuracy and the HU calibration differ in respect to surgical or radio therapeutic planning. X ray image stacks have been made using a new designed test device which contains structures for measuring MTF, NPS, the 3 D accuracy and the Hounsfield calibration (jaw or skull program). The image stacks of the transversal images were analyzed with a dedicated computer program. The MTF values are correlated with the physical resolution (CT and DVT) and are influenced by the used Kernel (CT). The NPS values are limited to an intra system comparison due to the insufficient HU accuracy. The 3 D accuracy is comparable in between the system types. The values of image quality are not yet correlated with dose values: NPS. Investigations to an appropriate dosimetry are ongoing to establish the ratio between dose and image quality (ALARA principle). No fundamental difference between the systems can be stated in respect radio therapeutic planning: improper HU calibration accuracy in CBCT and DVT units. The geometric 3 D accuracy of high performance DVT systems is greater than that of CT Systems.

TH-C-110-03: Pitfalls and Remedies of MDCT Scanners as Quantitative Instruments

Objectives: CT HU accuracy is influenced by the fundamental properties of x‐ray physics, CT system calibration processes, data acquisitionhardware and protocols, image reconstruction techniques, and patient preparation and handling. With a renewed and increased importance of quantitative imaging, this talk will provide an overview on this issue and focus on a few specific areas: scatter, beam‐hardening effect, CT components and system impact, and data acquisition protocols. Various techniques to reduce, correct, or avoid the pitfalls are presented. Current system limitations and new technologies to overcome the shortcomings will also be discussed.

Monte Carlo evaluation of scatter mitigation strategies in cone‐beam CT

To investigate the efficacy of two widely used scatter mitigation methods: antiscatter grids (ASGs) and beam modulating with bowtie filters (BTFs), in combination with subtractive scatter correction or zeroth order normalization phantom calibration, for improving image noise, contrast, contrast-to-noise ratio (CNR), and image uniformity for on-board cone-beam CT (CBCT) imaging systems used for image-guided radiation therapy. PTRAN Monte Carlo CBCT x-ray projections of head and pelvic phantoms were calculated for combinations of beam-modulation and scatter rejection methods and images were reconstructed by in-house developed software. In addition, a simple one-dimensional analytic model was developed to predict scatter-to-primary ratio (SPR) and CNR as a function of cylindrical phantom thickness, ASG transmission, and beam modulation with bow-tie filters. ASGs were found to have slightly negative or no effect on head phantom image CNR and to modestly improve CNR (10%-20%) in pelvic phantom images. However, scatter subtraction and norm-phantom calibration perform better when applied on data acquired with ASGs. Scatter subtraction improves CT number accuracy, but increases noise, and in high SPR/low primary-photon transmission scenarios can dramatically reduce CNR and introduce streaking artifacts. The BTF is found to reduce SPR and image noise, resulting in a better trade-off between CNR and imaging dose, but introduces a circular band artifact. Our study shows that ASGs have a modest positive impact in pelvic scans and negative in head scans, scatter subtraction improves the HU accuracy but reduces CNR, while BTF has a clearly positive effect.

TU‐D‐204B‐07: Correction for the ‘Black Hole’ Artifact in Cone‐Beam Computed Tomography

Purpose: An artifact typically seen in axial cone‐beam computed tomography (CBCT) reconstructions is a dark region or ‘black hole’ situated beneath the scan isocenter. We have traced the cause of the artifact to scattered radiation coming from the patient table. The artifact is most severe for offset‐detector scans and is especially significant in image‐guided radiotherapy (IGRT) where patient tables have flat tops and are larger than those used for diagnostic radiology. To address this problem, we have developed a hybrid scatter kernel superposition (SKS) method designed to correct for scatter from both the object‐of‐interest and the patient table. Method and Materials: The proposed algorithm uses separate kernels to estimate scatter from the patient table and the object‐of‐interest. For each projection image, the location of the shadow cast by the patient table on the imager is determined. The region not shadowed by the table is processed using asymmetric kernels that best model scatter transport through body‐like objects. The patient table region is processed using a combination of the asymmetric kernels and slab‐derived symmetric kernels. The composition of these hybrid kernels is projection‐angle dependent. To test the algorithm, pelvis phantom and patient data were acquired using the Varian On‐Board Imager (OBI) and processed. Artifact intensities and HU accuracies were assessed. Results: The hybrid kernel algorithm produced substantially better scatter estimates than previous SKS methods. HU non‐uniformities in the pelvis phantom were reduced from 97HU to 29HU. In the in vivo scan, the black hole artifact was reduced by 128HU and anatomical details were made more visible. Conclusions: Radiotherapy patient tables can produce significant scatter‐related artifacts. The proposed hybrid SKS algorithm effectively addresses this problem. Scatter from both the object‐of‐interest and the patient table was accurately estimated, and image uniformity and HU accuracy were greatly improved. Employees of Varian Medical Systems.

SU‐FF‐I‐26: A Simple Quantitative Method to Optimize and Monitor Pediatric CT Image Quality

Purpose: To describe a simple quantitative method to optimize and monitor pediatric CT image quality and dose. Method and Materials: Measurements were performed on a multi‐detector CT using helical pediatric brain and body protocols. Water phantoms were used to evaluate HU accuracy and image noise (HU standard deviation) with increasing mAs (dose). A commercial 20‐cm water phantom was used and homemade 15‐cm and 10‐cm diameter water phantoms used to simulate pediatric conditions. Images were inspected for artifacts. Low contrast and noise measurements were performed with the Catphan 15‐cm diameter low contrast module inside and outside the Catphan phantom. Quantitative contrast, noise and contrast‐to‐noise ratio (CNR) measurements were obtained by placing ROIs over the target and background areas. Results: Noise decreased with increasing mAs and was proportional to 1/SQRT (mAs). Noise were strongly dependent on object size. At constant mAs, image noise was 33% and 58% lower in the 15‐cm and 10‐cm objects respectively than in the 20‐cm object. CT number accuracy in the 10‐cm object was outside the ± 5 HU range (AAPM TG‐66). Ring artifacts were observed on the 10‐cm images. Measured contrast was in agreement with the Catphan's nominal contrast. Measured noise in the low contrast module was in agreement with the water‐phantom measurements and provided an independent noise measurement in addition to providing a CNR measurement. At constant technique, CNR increased with decreasing object size. Conclusion: Simple 10‐cm and 15‐cm water phantoms and the 15‐cm Catphan low contrast module can be used to rapidly and quantitatively assess HU accuracy, image contrast, noise and CNR of pediatric CT protocols. Scanning requires less than 15 minutes. Quantitative analysis can be performed rapidly on most CT viewing consoles. This quantitative method can be used to optimize pediatric CT image quality and dose, to monitor pediatric CT scanner performance routinely.