Robustness of Textural Features to Predict Stone Fragility Across Computed Tomography Acquisition and Reconstruction Parameters

RATIONALE AND OBJECTIVES

Previous studies have demonstrated that quantitative relationships exist between stone fragility at lithotripsy and morphological features extracted from computed tomography (CT) scans. The goal of this study was to determine if variations in scanner model, patient size, radiation dose, or reconstruction parameters impact the accuracy of the prediction of renal stone fragility in an in vitro model.

MATERIALS AND METHODS

Sixty-seven kidney stones were scanned using routine single and dual energy stone protocols, mimicking average, and large patient habitus. Low dose scans were also performed. Each scan was reconstructed with routine protocol parameters, and with thinner (0.6 mm) or thicker (3 mm) images, two different reconstruction kernels, and iterative reconstruction at two strengths. Fragility of each stone was measured in a controlled ex vivo experiment. A single predictive model was developed from a reference CT protocol configuration and applied to data from each CT acquisition and reconstruction parameter tested to obtain estimated stone comminution times.

RESULTS

None of the investigated protocols showed a significant variation in the accuracy of stone fragility classification, except for the ones with the most aggressive iterative reconstruction and/or with thicker images. In these protocols, a number of stone fragility assessments changed from fragile to hard (or vice versa), compared to their ground truth measurement.

CONCLUSION

Prediction accuracy of stone fragility models developed from CT data is robust to expected variations in CT stone protocols used for quantification tasks. This finding facilitates their future adoption to different clinical practices.

Estimating a size-specific dose for helical head CT examinations using Monte Carlo simulation methods

PURPOSE

Size-specific dose estimates (SSDE) conversion factors have been determined by AAPM Report 204 to adjust CTDI_vol to account for patient size but were limited to body CT examinations. The purpose of this work was to determine conversion factors that could be used for an SSDE for helical, head CT examinations for patients of different sizes.

METHODS

Validated Monte Carlo (MC) simulation methods were used to estimate dose to the center of the scan volume from a routine, helical head examination for a group of patient models representing a range of ages and sizes. Ten GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data were used in this study. CT scans were simulated using a Siemens multidetector row CT equivalent source model. Scan parameters were taken from the AAPM Routine Head protocols for a fixed tube current (FTC), helical protocol, and scan lengths were adapted to the anatomy of each patient model. MC simulations were performed using mesh tallies to produce voxelized dose distributions for the entire scan volume of each model. Three tally regions were investigated: (1) a small 0.6 cc volume at the center of the scan volume, (2) 0.8-1.0 cm axial slab at the center of the scan volume, and (3) the entire scan volume. Mean dose to brain parenchyma for all three regions was calculated. Mean bone dose and a mass-weighted average dose, consisting of brain parenchyma and bone, were also calculated for the slab in the central plane and the entire scan volume. All dose measures were then normalized by CTDI_vol for the 16 cm phantom (CTDI_vol,16 ). Conversion factors were determined by calculating the relationship between normalized doses and water equivalent diameter (D_w ).

RESULTS

CTDI_vol,16 -normalized mean brain parenchyma dose values within the 0.6 cc volume, 0.8-1.0 cm central axial slab, and the entire scan volume, when parameterized by D_w , had an exponential relationship with a coefficient of determination (R² ) of 0.86, 0.84, and 0.88, respectively. There was no statistically significant difference between the conversion factors resulting from these three different tally regions. Exponential relationships between CTDI_vol,16 -normalized mean bone doses had R² values of 0.83 and 0.87 for the central slab and for the entire scan volume, respectively. CTDI_vol,16 -normalized mass-weighted average doses had R² values of 0.39 and 0.51 for the central slab and for the entire scan volume, respectively.

CONCLUSIONS

Conversion factors that describe the exponential relationship between CTDI_vol,16 -normalized mean brain dose and a size metric (D_w ) for helical head CT examinations have been reported for two different interpretations of the center of the scan volume. These dose descriptors have been extended to describe the dose to bone in the center of the scan volume as well as a mass-weighted average dose to brain and bone. These may be used, when combined with other efforts, to develop an SSDE dose coefficients for routine, helical head CT examinations.

Estimating lung, breast, and effective dose from low-dose lung cancer screening CT exams with tube current modulation across a range of patient sizes

PURPOSE

The purpose of this study was to estimate the radiation dose to the lung and breast as well as the effective dose from tube current modulated (TCM) lung cancer screening (LCS) scans across a range of patient sizes.

METHODS

Monte Carlo (MC) methods were used to calculate lung, breast, and effective doses from a low-dose LCS protocol for a 64-slice CT that used TCM. Scanning parameters were from the protocols published by AAPM's Alliance for Quality CT. To determine lung, breast, and effective doses from lung cancer screening, eight GSF/ICRP voxelized phantom models with all radiosensitive organs identified were used to estimate lung, breast, and effective doses. Additionally, to extend the limited size range provided by the GSF/ICRP phantom models, 30 voxelized patient models of thoracic anatomy were generated from LCS patient data. For these patient models, lung and breast were semi-automatically segmented. TCM schemes for each of the GSF/ICRP phantom models were generated using a validated method wherein tissue attenuation and scanner limitations were used to determine the TCM output as a function of table position and source angle. TCM schemes for voxelized patient models were extracted from the raw projection data. The water equivalent diameter, Dw, was used as the patient size descriptor. Dw was estimated for the GSF/ICRP models. For the thoracic patient models, Dw was extracted from the DICOM header of the CT localizer radiograph. MC simulations were performed using the TCM scheme for each model. Absolute organ doses were tallied and effective doses were calculated using ICRP 103 tissue weighting factors for the GSF/ICRP models. Metrics of scanner radiation output were determined based on each model's TCM scheme, including CTDI_vol , dose length product (DLP), and CTDI_{vol, Low Att} , a previously described regional metric of scanner output covering most of the lungs and breast. All lung and breast doses values were normalized by scan-specific CTDI_vol and CTDI_{vol, Low Att} . Effective doses were normalized by scan-specific CTDI_vol and DLP. Absolute and normalized doses were reported as a function of Dw.

RESULTS

Lung doses normalized by CTDI_{vol, Low Att} were modeled as an exponential relationship with respect to Dw with coefficients of determination (R² ) of 0.80. Breast dose normalized by CTDI_{vol, Low Att} was modeled with an exponential relationship to Dw with an R² of 0.23. For all eight GSF/ICRP phantom models, the effective dose using TCM protocols was below 1.6 mSv. Effective doses showed some size dependence but when normalized by DLP demonstrated a constant behavior.

CONCLUSION

Lung, breast, and effective doses from LCS CT exams with TCM were estimated with respect to patient size. Normalized lung dose can be reasonably estimated with a measure of a patient size such as Dw and regional metric of CTDI_vol covering the thorax such as CTDI_{vol, Low Att} , while normalized breast dose can also be estimated with a regional metric of CTDI_vol but with a larger degree of variability than observed for lung. Effective dose normalized by DLP can be estimated with a constant multiplier.

Ultra-High Resolution Photon-Counting Detector CT Reconstruction using Spectral Prior Image Constrained Compressed-Sensing (UHR-SPICCS)

Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts. This limits the potential benefits that high-resolution PCD-CT could offer. Conventional reconstruction techniques such as the filtered back projection (FBP) have poor performance when reconstructing noisy CT projection data. To enable low noise multi-energy reconstructions, we employed a spectral prior image constrained compressed sensing (SPICCS) framework that exploits the spatio-spectral redundancy in the multi-energy acquisitions. We demonstrated noise reduction in narrow energy bins without losing energy-specific attenuation information and spatial resolution. We scanned an anthropomorphic head phantom, and a euthanized pig using our whole-body prototype PCD-CT system in the ultra-high resolution mode at 120kV. Image reconstructions were performed using SPICCS and compared with conventional FBP. Noise reduction of 18 to 46% was noticed in narrow energy bins corresponding to 25 - 65 keV and 65 - 12 keV, while the mean CT number was preserved. Spatial resolution measurement showed similar modulation transfer function (MTF) values between FBP and SPICCS, demonstrating preservation of spatial resolution.

Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system

An ultra-high resolution (UHR) mode, with a detector pixel size of [Formula: see text] relative to isocenter, has been implemented on a whole body research photon-counting detector (PCD) computed tomography (CT) system. Twenty synthetic lung nodules were scanned using UHR and conventional resolution (macro) modes and reconstructed with medium and very sharp kernels. Linear regression was used to compare measured nodule volumes from CT images to reference volumes. The full-width-at-half-maximum of the calculated curvature histogram for each nodule was used as a shape index, and receiver operating characteristic analysis was performed to differentiate sphere- and star-shaped nodules. Results showed a strong linear relationship between measured nodule volumes and reference volumes for both modes. The overall volume estimation was more accurate using UHR mode and the very sharp kernel, having 4.8% error compared with 10.5% to 12.6% error in the macro mode. The improvement in volume measurements using the UHR mode was more evident for small nodule sizes or star-shaped nodules. Images from the UHR mode with the very sharp kernel consistently demonstrated the best performance [[Formula: see text]] for separating star- from sphere-shaped nodules, showing advantages of UHR mode on a PCD CT scanner for lung nodule characterization.

A comparison of relative proton stopping power measurements across patient size using dual- and single-energy CT

PURPOSE

To evaluate the accuracy and precision across phantom size of a dual-energy computed tomography (DECT) technique used to calculate relative proton stopping power (SPR) in tissue-simulating materials and a silicone implant relative to conventional single-energy CT (SECT).

MATERIAL AND METHODS

A 32 cm lateral diameter (CIRS model 062M, Norfolk, Virginia) electron density phantom containing inserts which simulated the chemical composition of eight tissues in a solid-water background was scanned using SECT and DECT. A liquid water insert was included to confirm CT number accuracy. All materials were also placed in four water tanks, ranging from 15 to 45 cm in lateral width and scanned using DECT and SECT. A silicone breast implant was scanned in the same water phantoms. SPR values were calculated based on commercial software (syngo CT Dual Energy, Siemens Healthcare GmbH) and compared to reference values derived from proton beam measurements. Accuracy and precision were quantified across phantom size using percent error and standard deviation. Graphical and regression analysis were used to determine whether SECT or DECT was superior in estimating SPR across phantom size.

RESULTS

Both DECT and SECT SPR data resulted in good agreement with the reference values. Percent error was ±3% for both DECT and SECT in all materials except lung and dense bone. The coefficient of variation (CV) across materials and phantom sizes was 1.12% for SECT and 0.96% for DECT. Material-specific regression and graphical analysis did not reveal size dependence for either technique but did show reduced systematic bias with DECT for dense bone and liver. Mean percent error in SPR for the implant was reduced from 11.46% for SECT to 0.49% for DECT.

CONCLUSIONS

We demonstrate the superior ability of DECT to mitigate systematic bias in bones and liver and estimate SPR in a silicone breast implant.

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

Technical Note: Insertion of digital lesions in the projection domain for dual-source, dual-energy CT

PURPOSE

To compare algorithms performing material decomposition and classification in dual-energy CT, it is desirable to know the ground truth of the lesion to be analyzed in real patient data. In this work, we developed and validated a framework to insert digital lesions of arbitrary chemical composition into patient projection data acquired on a dual-source, dual-energy CT system.

METHODS

A model that takes into account beam-hardening effects was developed to predict the CT number of objects with known chemical composition. The model utilizes information about the x-ray energy spectra, the patient/phantom attenuation, and the x-ray detector energy response. The beam-hardening model was validated on samples of iodine (I) and calcium (Ca) for a second-generation dual-source, dual-energy CT scanner for all tube potentials available and a wide range of patient sizes. The seven most prevalent mineral components of renal stones were modeled and digital stones were created with CT numbers computed for each patient/phantom size and x-ray energy spectra using the developed beam-hardening model. Each digital stone was inserted in the dual-energy projection data of a water phantom scanned on a dual-source scanner and reconstructed with the routine algorithms in use in our practice. The geometry of the forward projection for dual-energy data was validated by comparing CT number accuracy and high-contrast resolution of simulated dual-energy CT data of the ACR phantom with experimentally acquired data.

RESULTS

The beam-hardening model and forward projection method accurately predicted the CT number of I and Ca over a wide range of tube potentials and phantom sizes. The images reconstructed after the insertion of digital kidney stones were consistent with the images reconstructed from the scanner, and the CT number ratios for different kidney stone types were consistent with data in the literature. A sample application of the proposed tool was also demonstrated.

CONCLUSION

A framework was developed and validated for the creation of digital objects of known mineral composition, and for inserting the digital objects into projection data from a commercial dual-source, dual-energy CT scanner. Among other applications, it will allow a systematic investigation of the impact of scan and reconstruction parameters on kidney stone dual-energy properties under rigorously controlled conditions.

Ultra-High Spatial Resolution, Multi-Energy CT using Photon Counting Detector Technology

Two ultra-high-resolution (UHR) imaging modes, each with two energy thresholds, were implemented on a research, whole-body photon-counting-detector (PCD) CT scanner, referred to as sharp and UHR, respectively. The UHR mode has a pixel size of 0.25 mm at iso-center for both energy thresholds, with a collimation of 32 × 0.25 mm. The sharp mode has a 0.25 mm pixel for the low-energy threshold and 0.5 mm for the high-energy threshold, with a collimation of 48 × 0.25 mm. Kidney stones with mixed mineral composition and lung nodules with different shapes were scanned using both modes, and with the standard imaging mode, referred to as macro mode (0.5 mm pixel and 32 × 0.5 mm collimation). Evaluation and comparison of the three modes focused on the ability to accurately delineate anatomic structures using the high-spatial resolution capability and the ability to quantify stone composition using the multi-energy capability. The low-energy threshold images of the sharp and UHR modes showed better shape and texture information due to the achieved higher spatial resolution, although noise was also higher. No noticeable benefit was shown in multi-energy analysis using UHR compared to standard resolution (macro mode) when standard doses were used. This was due to excessive noise in the higher resolution images. However, UHR scans at higher dose showed improvement in multi-energy analysis over macro mode with regular dose. To fully take advantage of the higher spatial resolution in multi-energy analysis, either increased radiation dose, or application of noise reduction techniques, is needed.

Renal Stone Characterization using High Resolution Imaging Mode on a Photon Counting Detector CT System

In addition to the standard-resolution (SR) acquisition mode, a high-resolution (HR) mode is available on a research photon-counting-detector (PCD) whole-body CT system. In the HR mode each detector consists of a 2x2 array of 0.225 mm × 0.225 mm subpixel elements. This is in contrast to the SR mode that consists of a 4x4 array of the same sub-elements, and results in 0.25 mm isotropic resolution at iso-center for the HR mode. In this study, we quantified ex vivo the capabilities of the HR mode to characterize renal stones in terms of morphology and mineral composition. Forty pure stones - 10 uric acid (UA), 10 cystine (CYS), 10 calcium oxalate monohydrate (COM) and 10 apatite (APA) - and 14 mixed stones were placed in a 20 cm water phantom and scanned in HR mode, at radiation dose matched to that of routine dual-energy stone exams. Data from micro CT provided a reference for the quantification of morphology and mineral composition of the mixed stones. The area under the ROC curve was 1.0 for discriminating UA from CYS, 0.89 for CYS vs COM and 0.84 for COM vs APA. The root mean square error (RMSE) of the percent UA in mixed stones was 11.0% with a medium-sharp kernel and 15.6% with the sharpest kernel. The HR showed qualitatively accurate characterization of stone morphology relative to micro CT.

Lung Nodule Volume Quantification and Shape Differentiation with an Ultra-High Resolution Technique on a Photon Counting Detector CT System

A new ultra high-resolution (UHR) mode has been implemented on a whole body photon counting-detector (PCD) CT system. The UHR mode has a pixel size of 0.25 mm by 0.25 mm at the iso-center, while the conventional (macro) mode is limited to 0.5 mm by 0.5 mm. A set of synthetic lung nodules (two shapes, five sizes, and two radio-densities) was scanned using both the UHR and macro modes and reconstructed with 2 reconstruction kernels (4 sets of images in total). Linear regression analysis was performed to compare measured nodule volumes from CT images to reference volumes. Surface curvature was calculated for each nodule and the full width half maximum (FWHM) of the curvature histogram was used as a shape index to differentiate sphere and star shape nodules. Receiver operating characteristic (ROC) analysis was performed and area under the ROC curve (AUC) was used as a figure of merit for the differentiation task. Results showed strong linear relationship between measured nodule volume and reference standard for both UHR and macro mode. For all nodules, volume estimation was more accurate using UHR mode with sharp kernel (S80f), with lower mean absolute percent error (MAPE) (6.5%) compared with macro mode (11.1% to 12.9%). The improvement of volume measurement from UHR mode was more evident particularly for small nodule size (3mm, 5mm), or star-shape nodules. Images from UHR mode with sharp kernel (S80f) consistently demonstrated the best performance (AUC = 0.85) when separating star from sphere shape nodules among all acquisition and reconstruction modes. Our results showed the advantages of UHR mode on a PCD CT scanner in lung nodule characterization. Various clinical applications, including quantitative imaging, can benefit substantially from this high resolution mode.

Utility of single-energy and dual-energy computed tomography in clot characterization: An in-vitro study

Background and purpose Because computed tomography (CT) is the most commonly used imaging modality for the evaluation of acute ischemic stroke patients, developing CT-based techniques for improving clot characterization could prove useful. The purpose of this in-vitro study was to determine which single-energy or dual-energy CT techniques provided optimum discrimination between red blood cell (RBC) and fibrin-rich clots. Materials and methods Seven clot types with varying fibrin and RBC densities were made (90% RBC, 99% RBC, 63% RBC, 36% RBC, 18% RBC and 0% RBC with high and low fibrin density) and their composition was verified histologically. Ten of each clot type were created and scanned with a second generation dual source scanner using three single (80 kV, 100 kV, 120 kV) and two dual-energy protocols (80/Sn 140 kV and 100/Sn 140 kV). A region of interest (ROI) was placed over each clot and mean attenuation was measured. Receiver operating characteristic curves were calculated at each energy level to determine the accuracy at differentiating RBC-rich clots from fibrin-rich clots. Results Clot attenuation increased with RBC content at all energy levels. Single-energy at 80 kV and 120 kV and dual-energy 80/Sn 140 kV protocols allowed for distinguishing between all clot types, with the exception of 36% RBC and 18% RBC. On receiver operating characteristic curve analysis, the 80/Sn 140 kV dual-energy protocol had the highest area under the curve for distinguishing between fibrin-rich and RBC-rich clots (area under the curve 0.99). Conclusions Dual-energy CT with 80/Sn 140 kV had the highest accuracy for differentiating RBC-rich and fibrin-rich in-vitro thrombi. Further studies are needed to study the utility of non-contrast dual-energy CT in thrombus characterization in acute ischemic stroke.

Dose-efficient ultrahigh-resolution scan mode using a photon counting detector computed tomography system

An ultrahigh-resolution (UHR) data collection mode was enabled on a whole-body, research photon counting detector (PCD) computed tomography system. In this mode, 64 rows of [Formula: see text] detector pixels were used, which corresponded to a pixel size of [Formula: see text] at the isocenter. Spatial resolution and image noise were quantitatively assessed for the UHR PCD scan mode, as well as for a commercially available UHR scan mode that uses an energy-integrating detector (EID) and a set of comb filters to decrease the effective detector size. Images of an anthropomorphic lung phantom, cadaveric swine lung, swine heart specimen, and cadaveric human temporal bone were qualitatively assessed. Nearly equivalent spatial resolution was demonstrated by the modulation transfer function measurements: 15.3 and [Formula: see text] spatial frequencies were achieved at 10% and 2% modulation, respectively, for the PCD system and 14.2 and [Formula: see text] for the EID system. Noise was 29% lower in the PCD UHR images compared to the EID UHR images, representing a potential dose savings of 50% for equivalent image noise. PCD UHR images from the anthropomorphic phantom and cadaveric specimens showed clear delineation of small structures.

Technical Note: Improved CT number stability across patient size using dual-energy CT virtual monoenergetic imaging

PURPOSE

The purpose of this study was to evaluate, over a wide range of phantom sizes, CT number stability achieved using two techniques for generating dual-energy computed tomography (DECT) virtual monoenergetic images.

METHODS

Water phantoms ranging in lateral diameter from 15 to 50 cm and containing a CT number test object were scanned on a DSCT scanner using both single-energy (SE) and dual-energy (DE) techniques. The SE tube potentials were 70, 80, 90, 100, 110, 120, 130, 140, and 150 kV; the DE tube potential pairs were 80/140, 70/150Sn, 80/150Sn, 90/150Sn, and 100/150Sn kV (Sn denotes that the 150 kV beam was filtered with a 0.6 mm tin filter). Virtual monoenergetic images at energies ranging from 40 to 140 keV were produced from the DECT data using two algorithms, monoenergetic (mono) and monoenergetic plus (mono+). Particularly in large phantoms, water CT number errors and/or artifacts were observed; thus, datasets with water CT numbers outside ±10 HU or with noticeable artifacts were excluded from the study. CT numbers were measured to determine CT number stability across all phantom sizes.

RESULTS

Data exclusions were generally limited to cases when a SE or DE technique with a tube potential of less than 90 kV was used to scan a phantom larger than 30 cm. The 90/150Sn DE technique provided the most accurate water background over the large range of phantom sizes evaluated. Mono and mono+ provided equally improved CT number stability as a function of phantom size compared to SE; the average deviation in CT number was only 1.4% using 40 keV and 1.8% using 70 keV, while SE had an average deviation of 11.8%.

CONCLUSIONS

The authors' report demonstrates, across all phantom sizes, the improvement in CT number stability achieved with mono and mono+ relative to SE.

Lesion insertion in the projection domain: Methods and initial results

PURPOSE

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 of achieving this objective is to create hybrid images that combine patient images with inserted 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.

METHODS

Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion.

RESULTS

For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically acquired for the ACR phantom in terms of Hounsfield unit and high-contrast resolution. For the validation of the lesion realism, lesions of various types were successfully inserted, including well circumscribed and invasive lesions, homogeneous and heterogeneous lesions, high-contrast and low-contrast lesions, isolated and vessel-attached lesions, and small and large lesions. The two experienced radiologists who reviewed the original and inserted lesions could not identify the lesions that were inserted. The same lesion, when inserted into the projection domain and reconstructed with different parameters, demonstrated a parameter-dependent appearance.

CONCLUSIONS

A framework has been developed for projection-domain insertion of lesions into commercial CT images, which can be potentially expanded to all geometries of CT scanners. Compared to conventional image-domain methods, the authors' method reflected the impact of scan and reconstruction parameters on lesion appearance. Compared to prior projection-domain methods, the authors' method has the potential to achieve higher anatomical complexity by employing clinical patient projections and real patient lesions.

Technical Note: Development and validation of an open data format for CT projection data

PURPOSE

Lack of access to projection data from patient CT scans is a major limitation for development and validation of new reconstruction algorithms. To meet this critical need, this work developed and validated a vendor-neutral format for CT projection data, which will further be employed to build a library of patient projection data for public access.

METHODS

A digital imaging and communication in medicine (DICOM)-like format was created for CT projection data (CT-PD), named the DICOM-CT-PD format. The format stores attenuation information in the DICOM image data block and stores parameters necessary for reconstruction in the DICOM header under various tags (51 tags to store the geometry and scan parameters and 9 tags to store patient information). To validate the accuracy and completeness of the new format, CT projection data from helical scans of the ACR CT accreditation phantom were acquired from two clinical CT scanners (Somatom Definition Flash, Siemens Healthcare, Forchheim, Germany and Discovery CT750 HD, GE Healthcare, Waukesha, WI). After decoding (by the authors for Siemens, by the manufacturer for GE), the projection data were converted to the DICOM-CT-PD format. Off-line CT reconstructions were performed by internal and external reconstruction researchers using only the information stored in the DICOM-CT-PD files and the DICOM-CT-PD field definitions.

RESULTS

Compared with the commercially reconstructed CT images, the off-line reconstructed images created using the DICOM-CT-PD format are similar in terms of CT numbers (differences of 5 HU for the bone insert and -9 HU for the air insert), image noise (±1 HU), and low contrast detectability (6 mm rods visible in both). Because of different reconstruction approaches, slightly different in-plane and cross-plane high contrast spatial resolution were obtained compared to those reconstructed on the scanners (axial plane: GE off-line, 7 lp/cm; GE commercial, 7 lp/cm; Siemens off-line, 8 lp/cm; Siemens commercial, 7 lp/cm. Coronal plane: Siemens off-line, 6 lp/cm; Siemens commercial, 8 lp/cm).

CONCLUSIONS

A vendor-neutral extended DICOM format has been developed that enables open sharing of CT projection data from third-generation CT scanners. Validation of the format showed that the geometric parameters and attenuation information in the DICOM-CT-PD file were correctly stored, could be retrieved with use of the provided instructions, and contained sufficient data for reconstruction of CT images that approximated those from the commercial scanner.

Implementation and evaluation of a protocol management system for automated review of CT protocols

Protocol review is important to decrease the risk of patient injury and increase the consistency of CT image quality. A large volume of CT protocols makes manual review labor‐intensive, error‐prone, and costly. To address these challenges, we have developed a software system for automatically managing and monitoring CT protocols on a frequent basis. This article describes our experiences in the implementation and evaluation of this protocol monitoring system. In particular, we discuss various strategies for addressing each of the steps in our protocol‐monitoring workflow, which are: maintaining an accurate set of master protocols, retrieving protocols from the scanners, comparing scanner protocols to master protocols, reviewing flagged differences between the scanner and master protocols, and updating the scanner and/or master protocols. In our initial evaluation focusing only on abdomen and pelvis protocols, we detected 309 modified protocols in a 24‐week trial period. About one‐quarter of these modified protocols were determined to contain inappropriate (i.e., erroneous) protocol parameter modifications that needed to be corrected on the scanner. The most frequently affected parameter was the series description, which was inappropriately modified 47 times. Two inappropriate modifications were made to the tube current, which is particularly important to flag as this parameter impacts both radiation dose and image quality. The CT protocol changes detected in this work provide strong motivation for the use of an automated CT protocol quality control system to ensure protocol accuracy and consistency.

PACS number(s): 87.57.Q‐