Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer.

PURPOSE: To compare imaging performance of a cadmium telluride (CdTe) based photon counting detector (PCD) with a CMOS based energy integrating detector (EID) for potential phase sensitive imaging of breast cancer. METHODS: A high energy inline phase sensitive imaging prototype consisting of a microfocus X-ray source with geometric magnification of 2 was employed. The pixel pitch of the PCD was 55µm, while 50µm for EID. The spatial resolution was quantitatively and qualitatively assessed through modulation transfer function (MTF) and bar pattern images. The edge enhancement visibility was assessed by measuring edge enhancement index (EEI) using the acrylic edge acquired images. A contrast detail (CD) phantom was utilized to compare detectability of simulated tumors, while an American College of Radiology (ACR) accredited phantom for mammography was used to compare detection of simulated calcification clusters. A custom-built phantom was employed to compare detection of fibrous structures. The PCD images were acquired at equal, and 30% less mean glandular dose (MGD) levels as of EID images. Observer studies along with contrast to noise ratio (CNR) and signal to noise ratio (SNR) analyses were performed for comparison of two detection systems. RESULTS: MTF curves and bar pattern images revealed an improvement of about 40% in the cutoff resolution with the PCD. The excellent spatial resolution offered by PCD system complemented superior detection of the diffraction fringes at boundaries of the acrylic edge and resulted in an EEI value of 3.64 as compared to 1.44 produced with EID image. At equal MGD levels (standard dose), observer studies along with CNR and SNR analyses revealed a substantial improvement of PCD acquired images in detection of simulated tumors, calcification clusters, and fibrous structures. At 30% less MGD, PCD images preserved image quality to yield equivalent (slightly better) detection as compared to the standard dose EID images. CONCLUSION: CdTe-based PCDs are technically feasible to image breast abnormalities (low/high contrast structures) at low radiation dose levels using the high energy inline phase sensitive imaging technique.

Automated daily quality control analysis for mammography in a multi-unit imaging center.

BACKGROUND: The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. PURPOSE: To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. MATERIAL AND METHODS: An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. RESULTS: The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. CONCLUSION: Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

Proposed changes to the ACR phantom filling procedure for more accurate and consistent activity concentrations.

OBJECTIVES: The phantom filling procedures currently specified by the American College of Radiology (ACR) for its PET accreditation program unnecessarily limit how tight the tolerances can be made on the accuracy requirements for the concentrations measured in the resultant images. METHODS: New procedures are proposed to improve the accuracy and consistency of the concentrations within the phantom at the time of imaging. These improvements are gained by exchanging the difficult process of accurately measuring a dose with the more easily achieved accurate measurements of time and liquid volume to control final radioactivity concentrations. A comparison of the results when following the two filling procedures is made. RESULTS: The variability in metrics specified by the ACR was approximately halved by following the new procedures. CONCLUSION: These improvements allow tighter thresholds to be applied when evaluating image quality and quantitative accuracy of the PET images. These changes also render this phantom data more suitable for inter-PET-scanner harmonization and improve its utility for comparing image reconstruction methods.

On replacing the manual measurement of ACR phantom images performed by MRI technologists with an automated measurement approach.

PURPOSE: To assess whether measurements on American College of Radiology (ACR) phantom images performed by magnetic resonance imaging (MRI) technologists as part of a weekly quality control (QC) program could be performed exclusively using an automated system without compromising the integrity of the QC program. MATERIALS AND METHODS: ACR phantom images are acquired on 15 MRI scanners at a number of ACR-accredited sites to fulfill requirements of a weekly QC program. MRI technologists routinely perform several measurements on these images. Software routines are also used to perform the measurements. A set of geometry measurements made by technologists over a five week period and those made using software routines were compared to reference-standard measurements made by two MRI physicists. RESULTS: The geometry measurements performed by software routines had a very high positive correlation (0.92) with the reference-standard measurements. Technologist measurements also had a high positive correlation (0.63), although the correlation was less than for the automated measurements. Bland-Altman analysis revealed overall good agreement between the automated and reference-standard measurements, with the 95% limits of agreement being within ±0.62 mm. Agreement between the technologist and the reference-standard measurements was demonstratively poorer, with 95% limits of agreement being ±1.46 mm. Some of the technologist measurements differed from the reference standard by as much as 2 mm. CONCLUSION: The technologists' geometry measurements may be able to be replaced by automated measurement without compromising the weekly QC program required by the ACR.

Establishing quality control action limits for CT number accuracy in spectral images using an American College of Radiology phantom.

BACKGROUND: Computed tomography (CT) number accuracy is important for quality assurance in CT imaging. However, in dual-energy CT imaging, there are no widely used action limits for CT number accuracy in spectral images, information that is urgently needed. PURPOSE: To establish action limits for spectral CT images using longitudinal spectral data and an American College of Radiology (ACR) phantom. METHODS: An ACR accreditation phantom was scanned routinely as part of a quality control program in our institution. We selected and analyzed 57 continuous weekly scans. The CT numbers or the density values of conventional and spectral images, including virtual monoenergetic images (40, 50, 70, 120, and 200 keV), iodine maps, calcium suppressed, and virtual non-contrast images, were measured in the four inserts (solid water, bone, polyethylene, and acrylic) of the phantom. Longitudinal data were analyzed for correlation using Pearson's correlation coefficient (r) and standard deviation (SD). The SD ratios between spectral images and conventional images were calculated and the action limits for spectral images were established based on the action limits from the ACR. RESULTS: Strong to very strong correlations (r > 0.70 or r < -0.70) were found among most spectral image types except the 200 keV images using solid water, polyethylene, and acrylic inserts (r = [-0.45, 0.64]). The SD ratio was highest for the 40 keV images, ranging from 2.8 to 6.5. The action limits of the bone insert were baseline ± 5.3 mg/mL for the iodine map and ranged from baseline ± 23.0 HU to baseline ± 391.9 HU for the other image types. The action limits for solid water ranged from baseline ± 4.1 HU to baseline ± 25.3 HU. The results for the polyethylene and the acrylic insert were close to those for solid water. Baselines can be established using the average of the initial 5∼10 measurements. CONCLUSIONS: Using longitudinal data, we estimated the action limits for CT number accuracy in the spectral images. This paves the way for establishing a comprehensive quality control program for spectral CT imaging.

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 apvalue of around 0.7 (no statistical difference). Misalignment in they-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.

Quality assurance and temporal stability of a 1.5 T MRI scanner for MR-guided Photon and Particle Therapy.

PURPOSE: To describe performance measurements, adaptations and time stability over 20 months of a diagnostic MR scanner for integration into MR-guided photon and particle radiotherapy. MATERIAL AND METHODS: For realization of MR-guided photon and particle therapy (MRgRT/MRgPT), a 1.5 T MR scanner was installed at the Heidelberg Ion Beam Therapy Center. To integrate MRI into the treatment process, a flat tabletop and dedicated coil holders for flex coils were used, which prevent deformation of the patient external contour and allow for the use of immobilization tools for reproducible positioning. The signal-to-noise ratio (SNR) was compared for the diagnostic and therapy-specific setup using the flat couch top and flexible coils for the a) head & neck and b) abdominal region as well as for different bandwidths and clinical pulse sequences. Additionally, a quality assurance (QA) protocol with monthly measurements of the ACR phantom and measurement of geometric distortions for a large field-of-view (FOV) was implemented to assess the imaging quality parameters of the device over the course of 20 months. RESULTS: The SNR measurements showed a decreased SNR for the RT-specific as compared to the diagnostic setup of (a) 26% to 34% and (b) 11% to 33%. No significant bandwidth dependency for this ratio was found. The longitudinal assessment of the image quality parameters with the ACR and distortion phantom confirmed the long-term stability of the MRI device. CONCLUSION: A diagnostic MRI was commissioned for use in MR-guided particle therapy. Using a radiotherapy specific setup, a high geometric accuracy and signal homogeneity was obtained after some adaptions and the measured parameters were shown to be stable over a period of 20 months.

Exploration of the pulse pileup effects in a clinical CdTe-based photon-counting computed tomography.

BACKGROUND: High tube current generates a high flux of x-rays to photon counting detectors (PCDs) that can potentially result in the piling up of pulses formed by concurrent photons, which can cause count loss and energy resolution degradation. PURPOSE: To evaluate the performance of clinical photon-counting CT (PCCT) systems in high flux, potentially influenced by pulse pileup effects, in terms of task-generic image quality metrics. METHODS: A clinical phantom was scanned on a commercial PCCT scanner (NAEOTOM Alpha, Siemens) at 120 kV under fourteen different tube current levels (40-1000 mA) with a rotation time of 0.25 s and a pitch of 1. The dose levels corresponded to CTDIvol (32 cm phantom) of 0.79-19.8 mGy. CT sinograms were reconstructed using QIR-off mode (noniterative reconstruction algorithm), Br44 kernel, and a voxel size of 0.4102 × 0.4102 × 3 mm 3 $0.4102 \times 0.4102 \times 3{\mathrm{\ mm}}^3$ . imQuest, an open-source MATLAB-based software package was used to calculate noise power spectrum (NPS), task transfer function (TTF), contrast-to-noise ratio (CNR), and CT number according to AAPM Task Group 233 metrology. RESULTS: The 50% cut-off frequency of TTF (f50 ) remained mostly constant across all higher tube currents for all inserts, namely polyethylene, bone, air, and acrylic. Using the lowest two data points (40 and 80 mA), the expected relationship between noise magnitude and tube current was determined to be noise ∝ $ \propto \ $ mA-0.47 . The measured noise magnitude were up to 11.1% higher than the expected value at the highest tube current. The average frequency of NPS (fav ) decreased from 0.32 to 0.29 mm-1 as tube current increased from 40 to 1000 mA. No considerable effects were observed in CT number measurement of any insert; however, CT numbers for air and bone changed almost monotonically as tube current increased. Absolute CNR increased monotonically for all inserts; however, the difference between measured and expected CNRs were approximately -6% to 12% across all tube currents. CONCLUSIONS: Increasing tube currents did not affect the spatial resolution, but slightly affected the CT number and noise measurements of the clinical PCCT system. However, the effects were only considerable at clinically irrelevant tube currents used on a small 20-cm phantom. In general clinical practices, automatic exposure control techniques are used to decrease the variation of flux on the detector, which alleviates the chances of detector saturation due to high count rates. The observed effects could be due to pulse pileup, signal-dependent filtration of the system, or nonlinearities in the reconstruction algorithm. In conclusion, either the deadtime of the detector used in the photon-counting CT system is shorter such that count losses due to pulse pileup are negligible, or pulse pileup has inconsiderable effects on the image quality of clinical photon-counting CT systems in routine clinical practice due to possible corrections applied on the system.

The automated measurement of CT number linearity using an ACR accreditation phantom.

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R2, were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R2> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR2= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR2< 0.99.

A multi-institutional comparison of acceptance testing data for a 0.35 T MRI scanner.

Objective.To present and quantify the variability in the acceptance testing data for the imaging component of the 0.35 T magnetic resonance-linear accelerator (MR-linac).Approach.The current acceptance testing protocol by the MR-linac vendor was described along with the equipment and scanner parameters utilized throughout the process. TheBofield homogeneity, SNR/uniformity of the combined and individual receiver coils, American College of Radiology (ACR) image quality testing, and spatial integrity of the imaging data were collected from twelve different institutions. The variability in the results was accentuated and the ramifications of the results were discussed in the context of MR-guided radiation therapy.Main Results.TheBofield homogeneity was found to have a large gantry dependence with the median values being <4 ppm for all gantry angles. The SNR and uniformity were found to be well above the vendor-specified thresholds with a relatively small institutional-dependence. All institutions passed the ACR image uniformity tests. The largest institutional variability was noted to be for the slice positional accuracy test. The spatial fidelity was calculated to be <1.0 and <2.1 mm within a 100 and a 175 mm radius from the isocenter.Significance.The results from this study can be used to set the tolerances and formal guidelines for MR-linacs imaging quality assurance. Additionally, the multi-institutional data reported in this work will aid in future MR-linac acceptance and commissioning.

Impact of ROI Size on the Accuracy of Noise Measurement in CT on Computational and ACR Phantoms.

Background: The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied. Objective: This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms. Material and Methods: In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3 × 3 to 41 × 41 pixels, with an increased size of 2 × 2 pixels. Results: The use of a minimum ROI size of 21 × 21 pixels shows noise in the range of ± 5% ground truth noise. The measured noise increases above the ± 5% range if the used ROI is smaller than 21 × 21 pixels. Conclusion: A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21 × 21 pixels.

Development and preclinical evaluation of a patient-specific high energy x-ray phase sensitive breast tomosynthesis system.

BACKGROUND: This article reports the first x-ray phase sensitive breast tomosynthesis (PBT) system that is aimed for direct translation to clinical practice for the diagnosis of breast cancer. PURPOSE: To report the preclinical evaluation and comparison of the newly built PBT system with a conventional digital breast tomosynthesis (DBT) system. METHODS AND MATERIALS: The PBT system is developed based on a comprehensive inline phase contrast theoretical model. The system consists of a polyenergetic microfocus x-ray source and a flat panel detector mounted on an arm that is attached to a rotating gantry. It acquires nine projections over a 15° angular span in a stop-and-shoot manner. A dedicated phase retrieval algorithm is integrated with a filtered back-projection method that reconstructs tomographic slices. The American College of Radiology (ACR) accreditation phantom, a contrast detail (CD) phantom and mastectomy tissue samples were imaged at the same glandular dose levels by both the PBT and a standard of care DBT system for image quality characterizations and comparisons. RESULTS: The PBT imaging scores with the ACR phantom are in good to excellent range and meet the quality assurance criteria set by the Mammography Quality Standard Act. The CD phantom image comparison and associated statistical analyses from two-alternative forced-choice reader studies confirm the improvement offered by the PBT system in terms of contrast resolution, spatial resolution, and conspicuity. The artifact spread function (ASF) analyses revealed a sizable lateral spread of metal artifacts in PBT slices as compared to DBT slices. Signal-to-noise ratio values for various inserts of the ACR and CD phantoms further validated the superiority of the PBT system. Mastectomy sample images acquired by the PBT system showed a superior depiction of microcalcifications vs the DBT system. CONCLUSION: The PBT imaging technology can be clinically employed for improving the accuracy of breast cancer screening and diagnosis.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations.

OBJECTIVE: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. MATERIALS AND METHODS: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. RESULTS: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. CONCLUSIONS: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Automated Quantitative Analysis of American College of Radiology PET Phantom Images.

Evaluation of PET image quality is central to annual physics surveys, quality assurance, and laboratory accreditation. A common method is to image the American College of Radiology (ACR) PET phantom, which contains hot and cold structures of various sizes in a warm background. Performance evaluation involves qualitative assessment of hot and cold structure visibility and overall image quality. Some criteria are quantitative and rely on manually drawn regions of interest (ROIs) to measure SUV. Fully automated scoring of ACR PET phantom images would improve efficiency, avoid observer-related dependencies, and possibly provide more robust evaluation of image quality. Methods: Software was developed to coregister PET images to a phantom template and to compute ROI measurements of hot vial activity (SUVmax) and background activity (SUVmean) automatically. In addition, 3-dimensional volumes of interest (VOIs) were generated to measure hot vial activity (SUVvial), background activity, and cold rod contrast. Consistency of the ROI-based and VOI-based methods was evaluated using phantom data from a total of 17 annual physics surveys of 3 PET/CT scanners with the same PET detector design. Results: The automated software processed all PET phantom datasets successfully. SUV consistency for hot vials was improved through use of cylindric VOIs and through normalization with respect to assayed activities and dilution volumes used in phantom preparation. Average vial SUV SD improved from 8.0% for standard SUVmax to 3.2% for normalized SUVvial Similarly, the SD for the SUV ratio of 16- to 25-mm vials improved from 5.0% for SUVmax to 3.2% for SUVvial Background SUVmean had a similar consistency between the ROI and VOI methods. Cold rod contrast was highly consistent, offering a potential alternative to qualitative visual assessment of low-contrast performance. Conclusion: Automated quantitative scoring of the ACR PET phantom is feasible and offers the advantages of more efficient, consistent, and thorough performance characterization. Acceptance ranges for SUVs and ratios likely can be tightened if normalized VOI measurements are used. Further testing with phantom data from a variety of PET scanners is necessary to establish suitable quantitative thresholds for acceptable performance.

LAB-QA2GO: A Free, Easy-to-Use Toolbox for the Quality Assessment of Magnetic Resonance Imaging Data.

Image characteristics of magnetic resonance imaging (MRI) data (e.g., signal-to-noise ratio, SNR) may change over the course of a study. To monitor these changes a quality assurance (QA) protocol is necessary. QA can be realized both by performing regular phantom measurements and by controlling the human MRI datasets (e.g., noise detection in structural or movement parameters in functional datasets). Several QA tools for the assessment of MRI data quality have been developed. Many of them are freely available. This allows in principle the flexible set-up of a QA protocol specifically adapted to the aims of one's own study. However, setup and maintenance of these tools takes substantial time, in particular since the installation and operation often require a fair amount of technical knowledge. In this article we present a light-weighted virtual machine, named LAB-QA2GO, which provides scripts for fully automated QA analyses of phantom and human datasets. This virtual machine is ready for analysis by starting it the first time. With minimal configuration in the guided web-interface the first analysis can start within 10 min, while adapting to local phantoms and needs is easily possible. The usability and scope of LAB-QA2GO is illustrated using a data set from the QA protocol of our lab. With LAB-QA2GO we hope to provide an easy-to-use toolbox that is able to calculate QA statistics without high effort.

Impact of CT parameters on the physical quantities related to image quality for two MDCT scanners using the ACR accreditation phantom: A phantom study.

INTRODUCTION: To evaluate the image quality provided by MDCT scanners using an ACR phantom an to find out the relationship between CT parameters and physical quantities related to image quality. METHODS: A GE Lightspeed VCT and a GE Lightspeed Pro 16 are used. The ACR phantom consists of four modules for evaluating physical parameters. The image quality parameters, such as CT number, linearity, CNR, image uniformity, SNR and at least spatial resolution using MTF, by different sets of image acquisition protocols (IAPs) are characterized. The influences of the IAPs on the physical quantities are also discussed. RESULTS: The CT numbers behaved linearly relative to material density for all tube voltages. The impact of the tube current on the CT numbers is neglectable. However, the variation of the tube current reflects in the CT number uncertainties. The CNR are altered by changing the IAPs. 50% MTF decreases from 6.2 to 3.6 lp/cm and from 6.5 to 3.7 lp/cm using Lung and Soft kernel for the Lightspeed VCT and Lightspeed Pro 16 scanner, respectively. CONCLUSION: The dependence of the image quality parameters on reconstruction kernels, tube peak voltages, tube currents and the slice thicknesses has been discussed. The tube peak voltage has the most influence on the CT numbers. The results indicate that the reconstruction kernel has the main impact on the spatial resolution. The spatial resolution dependence on the tube voltages, tube currents and slice thicknesses can be ignored.