Samuel Richard

Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm--initial clinical experience.

PURPOSE To investigate whether an adaptive statistical iterative reconstruction (ASIR) algorithm improves the image quality at low-tube-voltage (80-kVp), high-tube-current (675-mA) multidetector abdominal computed tomography (CT) during the late hepatic arterial phase. MATERIALS AND METHODS This prospective, single-center HIPAA-compliant study was institutional review board approved. Informed patient consent was obtained. Ten patients (six men, four women; mean age, 63 years; age range, 51-77 years) known or suspected to have hypervascular liver tumors underwent dual-energy 64-section multidetector CT. High- and low-tube-voltage CT images were acquired sequentially during the late hepatic arterial phase of contrast enhancement. Standard convolution FBP was used to reconstruct 140-kVp (protocol A) and 80-kVp (protocol B) image sets, and ASIR (protocol C) was used to reconstruct 80-kVp image sets. The mean image noise; contrast-to-noise ratio (CNR) relative to muscle for the aorta, liver, and pancreas; and effective dose with each protocol were assessed. A figure of merit (FOM) was computed to normalize the image noise and CNR for each protocol to effective dose. Repeated-measures analysis of variance with Bonferroni adjustment for multiple comparisons was used to compare differences in mean CNR, image noise, and corresponding FOM among the three protocols. The noise power spectra generated from a custom phantom with each protocol were also compared. RESULTS When image noise was normalized to effective dose, protocol C, as compared with protocols A (P = .0002) and B (P = .0001), yielded an approximately twofold reduction in noise. When the CNR was normalized to effective dose, protocol C yielded significantly higher CNRs for the aorta, liver, and pancreas than did protocol A (P = .0001 for all comparisons) and a significantly higher CNR for the liver than did protocol B (P = .003). Mean effective doses were 17.5 mSv +/- 0.6 (standard error) with protocol A and 5.1 mSv +/- 0.3 with protocols B and C. Compared with protocols A and B, protocol C yielded a small but quantifiable noise reduction across the entire spectrum of spatial frequencies. CONCLUSION Compared with standard FBP reconstruction, an ASIR algorithm improves image quality and has the potential to decrease radiation dose at low-tube-voltage, high-tube-current multidetector abdominal CT during the late hepatic arterial phase.

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

PURPOSE To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast. METHODS 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 MTFTask . 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 MTFTask dependencies on those factors. RESULTS For the FBP reconstructed images, the MTFTask measured with the inserts were the same as the MTF measured from the wire-based method. For the ASIR and MBIR data, the MTFTask using the high contrast insert was similar to the wire-based MTF and equal or superior to that of FBP. However, results for the MTFTask measured using the low-contrast inserts, the MTFTask for ASIR and MBIR data was lower than for the FBP, which was constant throughout all measurements. Similarly, as a function of mA, the MTFTask for ASIR and MBIR varied as a function of noise--with MTFTask being proportional to mA. Overall greater variability of MTFTask across dose and contrast was observed for MBIR than for ASIR. CONCLUSIONS 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 MTFTask generated by this method is expected to provide a more comprehensive assessment of image resolution across different reconstruction algorithms and imaging tasks.PURPOSE To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast. METHODS 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. RESULTS 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. CONCLUSIONS 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.

A methodology for image quality evaluation of advanced CT systems.

PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

Radiation dose reduction in abdominal computed tomography during the late hepatic arterial phase using a model-based iterative reconstruction algorithm: how low can we go?

ObjectiveThe aim of this study was to compare the image quality of abdominal computed tomography scans in an anthropomorphic phantom acquired at different radiation dose levels where each raw data set is reconstructed with both a standard convolution filtered back projection (FBP) and a full model-based iterative reconstruction (MBIR) algorithm. Materials and MethodsAn anthropomorphic phantom in 3 sizes was used with a custom-built liver insert simulating late hepatic arterial enhancement and containing hypervascular liver lesions of various sizes. Imaging was performed on a 64-section multidetector-row computed tomography scanner (Discovery CT750 HD; GE Healthcare, Waukesha, WI) at 3 different tube voltages for each patient size and 5 incrementally decreasing tube current–time products for each tube voltage. Quantitative analysis consisted of contrast-to-noise ratio calculations and image noise assessment. Qualitative image analysis was performed by 3 independent radiologists rating subjective image quality and lesion conspicuity. ResultsContrast-to-noise ratio was significantly higher and mean image noise was significantly lower on MBIR images than on FBP images in all patient sizes, at all tube voltage settings, and all radiation dose levels (P < 0.05). Overall image quality and lesion conspicuity were rated higher for MBIR images compared with FBP images at all radiation dose levels. Image quality and lesion conspicuity on 25% to 50% dose MBIR images were rated equal to full-dose FBP images. ConclusionThis phantom study suggests that depending on patient size, clinically acceptable image quality of the liver in the late hepatic arterial phase can be achieved with MBIR at approximately 50% lower radiation dose compared with FBP.

Quantitative imaging in breast tomosynthesis and CT: Comparison of detection and estimation task performance

PURPOSE This work investigates a framework for modeling volumetric breast imaging to compare detection and estimation task performance and optimize quantitative breast imaging. METHODS Volumetric reconstructions of a breast phantom, which incorporated electronic, quantum, and anatomical noise with embedded spherical lesions, were simulated over a range of acquisition angles varying from 4° to 204° with a constant total acquisition dose of 1.5 mGy. A maximum likelihood estimator was derived in terms of the noise power spectrum, which yielded figures of merit for quantitative imaging performance in terms of accuracy and precision. These metrics were computed for estimation of lesion area, volume, and location. Estimation task performance was optimized as a function of acquisition angle and compared to the performance of a more conventional lesion detection task. RESULTS Results revealed tradeoffs between electronic, quantum, and anatomical noise. The detection of a 4 mm sphere was optimal at an acquisition angle of 84°, where reconstructed images using a smaller acquisition angle exhibited increased anatomical noise and reconstructed images using a larger acquisition angle exhibited increased quantum and electronic noise. For all estimation tasks, accuracy was found to be fairly constant as a function acquisition angle indicating adequate system calibration, whereas a more significant dependence on acquisition angle was observed for precision performance. Precision for the 2D area estimation task was optimal at ∼104°, while precision of the 3D volume estimation task was optimal at larger angles (∼124°). Precision for the localization task showed orientation dependence where localization was significantly inferior in the depth direction. Overall, precision for localization was optimal at larger angles (i.e., >125°) compared to the size estimation tasks. Results suggested that for quantitative imaging tasks, the acquisition angle should be larger than currently used in conventional breast tomosynthesis for lesion detection. CONCLUSIONS Analysis of quantitative imaging performance using Fourier-based metrics highlights the difference between estimation and detection task in volumetric breast imaging and provides a meaningful framework for optimizing the performance of breast imaging systems for quantitative imaging applications.

An Anthropomorphic Breast Model for Breast Imaging Simulation and Optimization

RATIONALE AND OBJECTIVES Optimization studies for x-ray-based breast imaging systems using computer simulation can greatly benefit from a phantom capable of modeling varying anatomical variability across different patients. This study aimed to develop a three-dimensional phantom model with realistic and randomizable anatomical features. MATERIALS AND METHODS A voxelized breast model was developed consisting of an outer layer of skin and subcutaneous fat, a mixture of glandular and adipose, stochastically generated ductal trees, masses, and microcalcifications. Randomized realization of the breast morphology provided a range of patient models. Compression models were included to represent the breast under various compression levels along different orientations. A Monte Carlo (MC) simulation code was adapted to simulate x-ray based imaging systems for the breast phantom. Simulated projections of the phantom at different angles were generated and reconstructed with iterative methods, simulating mammography, breast tomosynthesis, and computed tomography (CT) systems. Phantom dose maps were further generated for dosimetric evaluation. RESULTS Region of interest comparisons of simulated and real mammograms showed strong similarities in terms of appearance and features. Noise-power spectra of simulated mammographic images demonstrated that the phantom provided target properties for anatomical backgrounds. Reconstructed tomosynthesis and CT images and dose maps provided corresponding data from a single breast enabling optimization studies. Dosimetry result provided insight into the dose distribution difference between modalities and compression levels. CONCLUSION The anthropomorphic breast phantom, combined with the MC simulation platform, generated a realistic model for a breast imaging system. The developed platform is expected to provide a versatile and powerful framework for optimizing volumetric breast imaging systems.

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR)

PURPOSE Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. METHODS Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. RESULTS Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. CONCLUSIONS The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstructions diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.

Quantitative CT: technique dependence of volume estimation on pulmonary nodules

Current estimation of lung nodule size typically relies on uni- or bi-dimensional techniques. While new three-dimensional volume estimation techniques using MDCT have improved size estimation of nodules with irregular shapes, the effect of acquisition and reconstruction parameters on accuracy (bias) and precision (variance) of the new techniques has not been fully investigated. To characterize the volume estimation performance dependence on these parameters, an anthropomorphic chest phantom containing synthetic nodules was scanned and reconstructed with protocols across various acquisition and reconstruction parameters. Nodule volumes were estimated by a clinical lung analysis software package, LungVCAR. Precision and accuracy of the volume assessment were calculated across the nodules and compared between protocols via a generalized estimating equation analysis. Results showed that the precision and accuracy of nodule volume quantifications were dependent on slice thickness, with different dependences for different nodule characteristics. Other parameters including kVp, pitch, and reconstruction kernel had lower impact. Determining these technique dependences enables better volume quantification via protocol optimization and highlights the importance of consistent imaging parameters in sequential examinations.

Predictive models for observer performance in CT: applications in protocol optimization

The relationship between theoretical descriptions of imaging performance (Fourier-based) and the performance of real human observers was investigated for detection tasks in multi-slice CT. The detectability index for the Fisher-Hotelling model observer and non-prewhitening model observer (with and without internal noise and eye filter) was computed using: 1) the measured modulation transfer function (MTF) and noise-power spectrum (NPS) for CT; and 2) a Fourier description of imaging task. Based upon CT images of human patients with added simulated lesions, human observer performance was assessed via an observer study in terms of the area under the ROC curve (Az). The degree to which the detectability index correlated with human observer performance was investigated and results for the non-prewhitening model observer with internal noise and eye filter (NPWE) were found to agree best with human performance over a broad range of imaging conditions. Results provided initial validation that CT image acquisition and reconstruction parameters can be optimized for observer performance rather than system performance (i.e., contrast-to-noise ratio, MTF, and NPS). The NPWE model was further applied for the comparison of FBP with a novel modelbased iterative reconstruction algorithm to assess its potential for dose reduction.

Precision of Iodine Quantification in Hepatic CT: Effects of Iterative Reconstruction With Various Imaging Parameters

OBJECTIVE The objective of this study was to evaluate the feasibility of using iterative reconstructions in hepatic CT to improve the precision of Hounsfield unit quantification, which is the degree to which repeated measurements under unchanged conditions provide consistent results. MATERIALS AND METHODS An anthropomorphic liver phantom with iodinated lesions designed to simulate the enhancement of hypervascular tumors during the late hepatic arterial phase was imaged, and images were reconstructed with both filtered back projection (FBP) and iterative reconstructions, such as adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR). This protocol was further expanded into various dose levels, tube voltages, and slice thicknesses to investigate the effect of iterative reconstructions under all these conditions. The iodine concentrations of the lesions were quantified, with their precision calculated in terms of repeatability coefficient. RESULTS ASIR reduced image noise by approximately 35%, and improved the quantitative precision by approximately 5%, compared with FBP. MBIR reduced noise by more than 65% and improved the precision by approximately 25% compared with the routine protocol. MBIR consistently showed better precision across a thinner slice thickness, lower tube voltage, and larger patient, achieving the target precision level at a dose lower (≥ 40%) than that of FBP. CONCLUSION ASIR blended with 50% of FBP indicated a moderate gain in quantitative precision compared with FBP but could achieve more with a higher percentage. A higher gain was achieved by MBIR. These findings may be used to reduce the dose required for reliable quantification and may further serve as a basis for protocol optimization in terms of iodine quantification.