Characteristic image quality of a third generation dual-source MDCT scanner: Noise, resolution, and detectability

Photon-Counting Detector CT for Liver Lesion Detection-Optimal Virtual Monoenergetic Energy for Different Simulated Patient Sizes and Radiation Doses

OBJECTIVES

The aim of this study was to evaluate the optimal energy level of virtual monoenergetic images (VMIs) from photon-counting detector computed tomography (CT) for the detection of liver lesions as a function of phantom size and radiation dose.

MATERIALS AND METHODS

An anthropomorphic abdominal phantom with liver parenchyma and lesions was imaged on a dual-source photon-counting detector CT at 120 kVp. Five hypoattenuating lesions with a lesion-to-background contrast difference of -30 HU and -45 HU and 3 hyperattenuating lesions with +30 HU and +90 HU were used. The lesion diameter was 5-10 mm. Rings of fat-equivalent material were added to emulate medium- or large-sized patients. The medium size was imaged at a volume CT dose index of 5, 2.5, and 1.25 mGy and the large size at 5 and 2.5 mGy, respectively. Each setup was imaged 10 times. For each setup, VMIs from 40 to 80 keV at 5 keV increments were reconstructed with quantum iterative reconstruction at a strength level of 4 (QIR-4). Lesion detectability was measured as area under the receiver operating curve (AUC) using a channelized Hotelling model observer with 10 dense differences of Gaussian channels.

RESULTS

Overall, highest detectability was found at 65 and 70 keV for both hypoattenuating and hyperattenuating lesions in the medium and large phantom independent of radiation dose (AUC range, 0.91-1.0 for the medium and 0.94-0.99 for the large phantom, respectively). The lowest detectability was found at 40 keV irrespective of the radiation dose and phantom size (AUC range, 0.78-0.99). A more pronounced reduction in detectability was apparent at 40-50 keV as compared with 65-75 keV when radiation dose was decreased. At equal radiation dose, detection as a function of VMI energy differed stronger for the large size as compared with the medium-sized phantom (12% vs 6%).

CONCLUSIONS

Detectability of hypoattenuating and hyperattenuating liver lesions differed between VMI energies for different phantom sizes and radiation doses. Virtual monoenergetic images at 65 and 70 keV yielded highest detectability independent of phantom size and radiation dose.

Development of a separability index for task specific characterization of spectral computed tomography

PURPOSE

In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions.

METHOD

Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated.

RESULTS

Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability.

CONCLUSION

The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

The impact of automatic tube current modulation related settings of a modern GE CT scanner on image quality and patient dose; details do matter

PURPOSE

To investigate the operation principles of the automatic tube current modulation (ATCM) of a modern GE healthcare CT scanner, and the impact of related settings on image quality and patient dose.

MATERIAL & METHODS

A dedicated phantom (Mercury 4.0) was scanned using two of the most frequently used clinical scanning protocols (chest and abdomen-pelvis). The preset protocol settings were used as starting points (reference conditions). Scan direction, scan mode (helical vs. axial), total beam width, tube potential (kVp), and ATCM settings were then modified individually to understand their impact on radiation dose and image quality. Regarding the ATCM settings, the SmartmA minimum and maximum mA limits, and the noise index (NI) values were varied. As surrogates of patient dose, the CTDIvol and DLP values of each scan were used. As surrogates of image quality were used the image noise and the detectability index (d') of five different materials (air, solid water, polystyrene, iodine, and bone) embedded in the Mercury phantom calculated with the ImQuest software.

RESULTS

The scanning direction did not have any effect on ATCM curves, unlike what has been observed in CT scanners from other manufacturers. Total beam width does matter, however, the SmartmA limit settings and kVp selection had the greatest impact on image quality and dose. It was seen that improper minimum mA limit settings practically invalidated the ATCM operation. In contrast, when full modulation was allowed without restrictions, noise standard deviation, and detectability index became much more consistent across the wide range of phantom diameters. For lower kVp settings an impressive dose reduction was observed that requires further investigation.

CONCLUSION

SmartmA is a tool that if not properly used may increase the patient doses considerably. Therefore, its settings should be carefully adjusted for each preset different clinical protocol.

Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography

OBJECTIVE

Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging.

METHODS

Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics.

RESULTS

For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics.

CONCLUSION

The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.

The effect of a pre-reconstruction process in a filtered back projection reconstruction on an image quality of a low tube voltage computed tomography

This study aims to evaluate the effect of pre-reconstruction process for low tube voltage computed tomography (CT) on image quality of filtered back projection (FBP) reconstruction. Small and large quality assurance water phantoms (19 and 33 cm diameter) were scanned on a third-generation dual-source CT with 70 kVp and 120 kVp at various dose levels. Image quality was assessed in terms of the noise power spectrum (NPS) and task-based transfer function (TTF). NPSs and TTFs in the small phantom were comparable between 70 and 120 kVp protocols. In the large phantom, the curves of the NPS changed and the TTF decreased even at the high-dose levels for 70 kVp protocol compared to 120 kVp protocol. Our results indicated that the pre-reconstruction process is performed in low tube voltage CT for large objects even for the FBP reconstruction and has an effect on the image quality.

A systematic task-based image quality assessment of photon-counting and energy integrating CT as a function of reconstruction kernel and phantom size

BACKGROUND

Image quality of photon-counting and energy integrating CT scanners changes with object size, dose to the object, and kernel selection.

PURPOSE

To comprehensively compare task-generic image quality of photon-counting CT (PCCT) and energy integrating CT (EICT) systems as a function of phantom size, dose, and reconstruction kernel.

METHODS

A size-variant phantom (Mercury Phantom 3.0) was used to characterize the image quality of PCCT and EICT systems as a function of object size. The phantom contained five cylinders attached by slanted tapered sections. Each cylinder contained two sections: one uniform for noise, and the other with inserts for spatial resolution and contrast measurements. The phantom was scanned on Siemens' SOMATOM Force and NAEOTOM Alpha at 1.18 and 7.51 mGy without tube current modulation. CTDIvol was matched across two systems by setting the required tube currents, else, all other acquisition settings were fixed. CT sinograms were reconstructed using FBP and iterative (ADMIRE2 - Force; QIR2 - Alpha) algorithms with Body regular (Br) kernels. Noise Power Spectrum (NPS), Task Transfer Function (TTF), contrast-to-noise ratio (CNR), and detectability index (d') for a task of identifying 2-mm disk were evaluated based on AAPM TG-233 metrology using imQuest, an open-source software package. Averaged noise frequency (fav ) and 50% cut-off frequency for TTF (f50 ) were used as scalar metrics to quantify noise texture and spatial resolution, respectively. The difference between image quality metrics' measurements was calculated as IQPCCT - IQEICT .

RESULTS

From Br40 (soft) to Br64 (sharp), f50 for air insert increased from 0.35 mm-1  ± 0.04 (standard deviation) to 0.76 mm-1  ± 0.17, 0.34 mm-1  ± 0.04 to 0.77 mm-1  ± 0.17, 0.37 mm-1  ± 0.02 to 0.95 mm-1  ± 0.17 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively, when averaged over all sizes and dose levels. Similarly, from Br40 to Br64, noise magnitude increased from 10.86 HU ± 7.12 to 38.61 HU ± 18.84, 10.94 HU ± 7.08 to 38.82 HU ± 18.70, 13.74 HU ± 11.02 to 52.11 HU ± 26.22 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. The difference in fav was consistent across all sizes and dose levels. PCCT-70keV-VMI showed better consistency in contrast measurements for iodine and bone inserts than PCCT-T3D and EICT; however, PCCT-T3D had higher contrast for both inserts. From Br40 to Br64, considering all sizes and dose levels, CNR for iodine insert decreased from 52.30 ± 46.44 to 12.18 ± 10.07, 40.42 ± 33.42 to 9.48 ± 7.16, 39.94 ± 37.60 to 7.84 ± 6.67 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively.

CONCLUSIONS

Both PCCT image types, T3D and 70-keV-VMI exhibited similar or better noise, contrast, CNR than EICT when comparing kernels with similar names. For 512 × 512 matrix, PCCT's sharp kernels had lower resolution than EICT's sharp kernels. For all image quality metrics, except extreme low, every dose condition had a similar set of IQ-matching kernels. It suggests that considering patient size and dose level to determine IQ-matching kernel pairs across PCCT and EICT systems may not be imperative while translating protocols, except when the signal to the detector is extremely low.

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 of0.4102 × 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.

High Spatial-Resolution Skull Base Imaging With Photon-Counting Computed Tomography and Energy-Integrating Computed Tomography: A Comparative Phantom Study

ABSTRACT

Photon-counting computed tomography (PCCT) offers better high-resolution and noise performance than energy integrating detector (EID) CT. In this work, we compared both technologies for imaging of the temporal bone and skull base. A clinical PCCT system and 3 clinical EID CT scanners were used to image the American College of Radiology image quality phantom using a clinical imaging protocol with matched CTDI vol (CT dose index-volume) of 25 mGy. Images were used to characterize the image quality of each system across a series of high-resolution reconstruction options. Noise was calculated from the noise power spectrum, whereas resolution was quantified with a bone insert by calculating a task transfer function. Images of an anthropomorphic skull phantom and 2 patient cases were examined for visualization of small anatomical structures. Across measured conditions, PCCT had a comparable or smaller average noise magnitude (120 Hounsfield units [HU]) to the EID systems (144-326 HU). Photon-counting CT also had comparable resolution (task transfer function f25 : 1.60 mm -1 ) to the EID systems (1.34-1.77 mm -1 ). Imaging results supported quantitative findings as PCCT more clearly showed the 12-lp/cm bars from the fourth section of the American College of Radiology phantom and better represented the vestibular aqueduct and oval and round windows when compared with the EID scanners. A clinical PCCT system was able to image the temporal bone and skull base with improved spatial resolution and lower noise than clinical EID CT systems at matched dose.

Image Quality Analysis of Photon-Counting CT Compared with Dual-Source CT: A Phantom Study for Chest CT Examinations

A comparison was made between the image quality of a photon-counting CT (PCCT) and a dual-source CT (DSCT). The evaluation of image quality was performed using a Catphan CT phantom, and the physical metrics, such as the noise power spectrum and task transfer function, were measured for both PCCT and DSCT at three CT dose indices (1, 5 and 10 mGy). Polyenergetic and virtual monoenergetic reconstructions were used to evaluate the performance differences by simulating a Gaussian spot with a radius of 5 mm and calculating the detectability index. The highest iterative reconstruction level was able to decrease the noise by about 70% compared with the filtered back projection using a parenchyma reconstruction kernel. The PCCT task transfer functions remained constant, while those of the DSCT increased with the reconstruction strength level. At monoenergetic 70 keV, a 50% decrease in noise was observed for DSCT with image smoothing, while PCCT had the same 50% decrease in noise without any smoothing. The PCCT detectability index at a reconstruction strength level of two was equivalent to the highest level of ADMIRE 5 for DSCT. The PCCT showed its superiority over the DSCT, especially for lung nodule detection.

Intravenous Contrast Material for Cardiac Computed Tomography: Results From the Open-label Multicenter, Multivendor Italian Registry of Contrast Material Use in Cardiac Computed Tomography

PURPOSE

The Italian Registry of Contrast Material use in Cardiac Computed Tomography (iRCM-CCT) is a multicenter, multivendor, observational study on the use of contrast media (CM) in patients undergoing cardiac computed tomography (CCT). The aim of iRCM-CCT is to assess image quality and safety profile of intravenous CM compounds.

MATERIALS AND METHODS

iRCM-CCT enrolled 1842 consecutive patients undergoing CCT (≥50 per site) at 20 cluster sites with the indication of suspected coronary artery disease. Demographic characteristics, CCT, and CM protocols, clinical indications, safety markers, radiation dose reports, qualitative (ie, poor vascular enhancement) and quantitative (ie, HU attenuation values) image parameters were recorded. A centralized coordinating center collected and assessed all image parameters.

RESULTS

The cohort included 891 men and 951 women (age: 63±14 y, body mass index: 26±4 kg/m2) studied with ≥64 detector rows computed tomography scanners and different iodinated intravenous CM protocols and compounds (iodixanol, iopamidol, iohexol, iobitridol, iopromide, and iomeprol). The following vascular attenuation was reported: 504±147 HU in the aorta, 451±146 HU in the right coronary artery, 474±146 HU in the left main, 451±146 HU in the left anterior descending artery, and 441±149 HU in the circumflex artery. In 4% of cases the image quality was not satisfactory due to poor enhancement. The following adverse reactions to CM were recorded: 6 (0.3%) extravasations and 17 (0.9%) reactions (11 mild, 4 moderate, 2 severe).

CONCLUSIONS

In a multicenter registry on CM use during CCT the prevalence of CM-related adverse reactions was very low. The appropriate use of CM is a major determinant of image quality.

Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study

PURPOSE

To compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT).

METHODS

A multipurpose chest phantom containing artificial spherical pulmonary nodules with 5-, 8-, 10-, and 12-mm diameters and Hounsfield units (HUs) of -630 and +100 HU was scanned 20 times at a standard dose, based on a low-dose screening CT trial, and at 1/2, 1/6, and 1/12 of the standard dose. To assess noise reduction performance and volumetric accuracy, the standard deviations (SDs) of the pixel values and volumetric percentage errors (PEs) were compared among FBP, hybrid IR, and DLR. The noise non-stationarity index (NNSI) was calculated from 20 image replicates and compared among FBP, hybrid IR, and DLR to evaluate noise stationarity.

RESULTS

The SD reduction rates for FBP in hybrid IR and DLR were 62 %-85 % and 79 %-90 %, respectively. For the four nodules with +100 HU, the PE of all reconstruction methods was <±25 % (not clinically relevant). For the four nodules with -630 HU, the PEs were equivalent or lower for hybrid IR and DLR than for FBP, and the PE difference between hybrid IR and DLR ranged from 0 % to 7%. The NNSI was significantly higher for DLR than for FBP and hybrid IR (p < 0.01).

CONCLUSIONS

Greater noise suppression was achieved with DLR than with hybrid IR without compromising nodule volumetric accuracy in LDCT despite the representative noise non-stationarity.

[Evaluation of Noise Properties at Nonuniformity Area and Resolution Properties of CT Image Using Iterative Reconstruction Method]

PURPOSE

The purpose of this study was to investigate the resolution property and the noise properties at the nonuniformity area in the slice plane of iterative reconstruction (IR)-CT image.

METHODS

CT images of a phantom with nonuniformity areas including multiple contrast (medium, high, and ultra-high contrast) signals were acquired at various scan dose conditions and reconstructed with different iterative intensity levels. The noise properties at the nonuniformity and the uniformity areas were evaluated by measuring normalized noise power spectra (nNPSs) using subtracted images from sequential scanning data under the same scan conditions. We investigated the relationship between the noise properties and the resolution properties evaluated by measuring task transfer function (TTF) using multiple contrast signals before the subtraction.

RESULTS

There was a correlation between the nNPS at the nonuniformity area and the TTF because the nNPS values at high spatial frequency were increased with superior TTF (higher dose, mild iterative intensity, and higher contrast level). The differences in the high spatial frequency component of the nNPSs among each task were decreased with the inferior TTF tasks.

CONCLUSION

We conclude that the noise properties in the slice plane at the nonuniformity area and resolution properties of the IR-CT image were correlated similar to image quality properties of the linear imaging system due to the dependence of the nNPSs curve on the TTF value under each task.

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image ‘patchiness’, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation.

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

PURPOSE

To compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.

METHODS

Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa.

RESULTS

DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader.

CONCLUSION

The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

Correction for Systematic Bias in Radiomics Measurements Due to Variation in Imaging Protocols

RATIONALE AND OBJECTIVES

The accuracy of measured radiomics features is affected by CT imaging protocols. This study aims to ascertain if applying bias corrections can improve the classification performance of the radiomics features.

MATERIALS AND METHODS

A cohort of 144 Non-Small Cell Lung Cancer patient CT images was used to calculate radiomics features for use in predictive models of patient pathological stage. Simulation models of the tumors, matched to patient lesion qualities of size, contrast, and degree of spiculation, were used to both create and assess protocol-specific correction factors. The usefulness of correction was first assessed by applying the corrections to simulated lesion phantoms with known properties using a corrected paired Student's t-test. The sensitivity of radiomics features to correction factors was assessed by applying a library of possible theoretical correction factors to the uncorrected radiomics from the patient data. The data were then used to assess the effect of the correction on prediction performance (AUC) from a logistic regression radiomics model across the patient cases.

RESULTS

The correction factors were shown to reduce the bias of radiomics features, caused by protocols, provided that the correction factors were derived from lesion models with similar properties. The sensitivity of the radiomics features to changes due to protocol effects was on average 89% among all features. The corrections applied to patient data resulted in a small increase of 0.0074 in AUC that was not statistically significant (p=0.60).

CONCLUSION

Protocol-specific correction factors can be applied to radiomics studies to control for biases introduced by different imaging protocols. The correction factors should ideally be lesion-specific, derived using lesion models that echo patient lesion characteristics in terms of size, contrast, and degree of spiculation. Small corrections in the 10% range offers only a small improvement in the predictability of radiomics.

Framelet tensor sparsity with block matching for spectral CT reconstruction

PURPOSE

Spectral computed tomography (CT) based on the photon-counting detection system has the capability to produce energy-discriminative attenuation maps of objects with a single scan. However, the insufficiency of photons collected into the narrow energy bins results in high quantum noise levels causing low image quality. This work aims to improve spectral CT image quality by developing a novel regularization based on framelet tensor prior.

METHODS

First, similar patches are extracted from highly correlated inter-channel images in spectral and spatial domains, and stacked to form a third-order tensor after vectorization along the energy channels. Second, the framelet tensor nuclear norm (FTNN) is introduced and applied to construct the regularization to exploit the sparsity embedded in nonlocal similarity of spectral images, and thus the reconstruction problem is modeled as a constrained optimization. Third, an iterative algorithm is proposed by utilizing the alternating direction method of multipliers framework in which efficient solvers are developed for each subproblem.

RESULTS

Both numerical simulations and real data verifications were performed to evaluate and validate the proposed FTNN based method. Compared to the analytic, TV-based, and the state-of-the-art tensor-based methods, the proposed method achieves higher numerical accuracy on both reconstructed CT images and decomposed material maps in the mouse data indicating the capability in noise suppression and detail preservation of the proposed method.

CONCLUSIONS

A framelet tensor sparsity-based iterative algorithm is proposed for spectral reconstruction. The qualitative and quantitative comparisons show a promising improvement of image quality, indicating its promising potential in spectral CT imaging. This article is protected by copyright. All rights reserved.

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Background

New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm.

Methods

Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU).

Results

NPS peaks were lower with AiCE than with AIDR 3D (-41%±6% for all levels) or FIRST (-15%±6% for Strong level and -41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (-9%±2% using Mild and -3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (-6%±6% and -13%±3%, respectively). For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (-19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; -21%±14%). Using the Standard level, dose could be reduced by -79% for the small calcification and -57% for the large mass using AICE compared to AIDR 3D.

Conclusions

The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance

Background

To compare task-based image quality (TB-IQ) among virtual monoenergetic images (VMI) and linear-blended images (LBI) from dual-energy CT as a function of contrast task, radiation dose, size, and lesion diameter.

Methods

A TB-IQ phantom (Mercury Phantom 4.0, Sun Nuclear Corporation) was imaged on a third-generation dual-source dual-energy CT with 100/Sn150 kVp at three volume CT dose levels (5, 10, 15 mGy). Three size sections (diameters 16, 26, 36 cm) with subsections for image noise and spatial resolution analysis were used. High-contrast tasks (e.g., calcium-containing stone and vascular lesion) were emulated using bone and iodine inserts. A low-contrast task (e.g., low-contrast lesion or hematoma) was emulated using a polystyrene insert. VMI at 40-190 keV and LBI were reconstructed. Noise power spectrum (NPS) determined the noise magnitude and texture. Spatial resolution was assessed using the task-transfer function (TTF) of the three inserts. The detectability index (d') served as TB-IQ metric.

Results

Noise magnitude increased with increasing phantom size, decreasing dose, and decreasing VMI-energy. Overall, noise magnitude was higher for VMI at 40-60 keV compared to LBI (range of noise increase, 3-124%). Blotchier noise texture was found for low and high VMIs (40-60 keV, 130-190 keV) compared to LBI. No difference in spatial resolution was observed for high contrast tasks. d' increased with increasing dose level or lesion diameter and decreasing size. For high-contrast tasks, d' was higher at 40-80 keV and lower at high VMIs. For the low-contrast task, d' was higher for VMI at 70-90 keV and lower at 40-60 keV.

Conclusions

Task-based image quality differed among VMI-energy and LBI dependent on the contrast task, dose level, phantom size, and lesion diameter. Image quality could be optimized by tailoring VMI-energy to the contrast task. Considering the clinical relevance of iodine, VMIs at 50-60 keV could be proposed as an alternative to LBI.

Comparison of Low Dose Performance of Photon-Counting and Energy Integrating CT

RATIONALE AND OBJECTIVES

The purpose of this study was to investigate the potential of photon-counting CT (PCCT) to improve quantitative image quality for low dose imaging compared to energy-integrating detector CT (EID CT).

MATERIALS AND METHODS

An investigational scanner (Siemens, Germany) with PCCT and EID CT subsystems was used to compare image quality performance at four dose levels: 1.7, 2, 4, 6 mGy CTDIvol, all at or below current dose values used for conventional abdominal CT. A CT quality control phantom with a homogeneous section for noise measurements and a section with cylindrical inserts of air (-910 HU), polystyrene (50 HU), acrylic (205 HU), and Teflon (1000 HU) was imaged and characterized in terms of noise, resolution, contrast-to-noise ratio (CNR), and detectability index. A second phantom with a 30 cm diameter was also imaged containing iodine solutions ranging from 0.125 to 8 mg I/mL. CNR of the iodine vials was computed as a function of CT dose and iodine concentration.

RESULTS

With resolution unaffected by dose in both PCCT and EID CT, PCCT images exhibited 22.1-24.0% improvement in noise across dose levels evaluated. This noise improvement translated into a 29-41% improvement in CNR and 20-36% improvement in detectability index. For iodine contrast, PCCT images had a higher CNR for all combinations of iodine contrast and dose evaluated.

CONCLUSION

For the conditions studied, PCCT exhibited superior image quality compared to EID CT. For iodine detection, PCCT offered a notable advantage with improved CNR at all doses and iodine concentration levels.

High frequency residues: A new set of signals for detectability studies of an X-ray imaging system

A new set of signals for studying detectability of an X-ray imaging system is presented. The results obtained with these signals are intended to complement the NEQ results. The signals are generated from line spread profiles by progressively removing their lower frequency components and the resulting high frequency residues (HFRs) form the set of signals to be used in detectability studies. Detectability indexes for these HFRs are obtained using a non-prewhitening (NPW) observer and a series of edge images are used to obtain the HFRs, the covariance matrices required by the NPW model and the MTF and NPS used in NEQ calculations. The template used in the model is obtained by simulating the processes of blurring and sampling of the edge images. Comparison between detectability indexes for the HFRs and NEQ are carried out for different acquisition techniques using different beam qualities and doses. The relative sensitivity shown by detectability indexes using HFRs is higher than that of NEQ, especially at lower doses. Also, the different observers produce different results at high doses: while the ideal Bayesian observer used by NEQ distinguishes between beam qualities, the NPW used with the HFRs produces no differences between them. Delta functions used in HFR are the opposite of complex exponential functions in terms of their support in the spatial and frequency domains. Since NEQ can be interpreted as detectability of these complex exponential functions, detectability of HFRs is presented as a natural complement to NEQ in the performance assessment of an imaging system.

A scanner-specific framework for simulating CT images with tube current modulation

Although tube current modulation (TCM) is routinely implemented in modern computed tomography (CT) scans, no existing CT simulator is capable of generating realistic images with TCM. The goal of this study was to develop such a framework to (1) facilitate patient-specific optimization of TCM parameters and (2) enable future virtual imaging trials (VITs) with more clinically realistic image quality and x-ray flux distributions. The framework was created by developing a TCM module and integrating it with an existing CT simulator (DukeSim). The developed module utilizes scanner-calibrated TCM parameters and two localizer radiographs to compute the mAs for each simulated CT projection. This simulation pipeline was validated in two parts. First, DukeSim was validated in the context of a commercial scanner with TCM (SOMATOM Force, Siemens Healthineers) by imaging a physical CT phantom (Mercury, Sun Nuclear) and its computational analogue. Second, the TCM module was validated by imaging a computational anthropomorphic phantom (ATOM, CIRS) using DukeSim with real and module-generated TCM profiles. The validation demonstrated DukeSim's realism in terms of noise magnitude, noise texture, spatial resolution, and image contrast (with average differences of 0.38%, 6.31%, 0.43%, and -9 HU, respectively). It also demonstrated the TCM module's realism in terms of projection-level mAs and resulting noise magnitude (2.86% and -2.60%, respectively). Finally, the framework was applied to a pilot VIT simulating images of three computational anthropomorphic phantoms (XCAT, with body mass indices (BMIs) of 24.3, 28.2, and 33.0) under five different TCM settings. The optimal TCM for each phantom was characterized based on various criteria, such as minimizing mAs or maximizing image quality. 'Very Weak' TCM minimized noise for the 24.3 BMI phantom, while 'Very Strong' TCM minimized noise for the 33.0 BMI phantom. This illustrates the utility of the developed framework for future optimization studies of TCM parameters and, more broadly, large-scale VITs with scanner-specific TCM.

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence

OBJECTIVE

To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm.

METHODS

A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics.

RESULTS

For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence.

CONCLUSIONS

The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

Detectability assessment of an x-ray imaging system using the nodes in a wavelet packet decomposition of a star-bar object

PURPOSE

Using linear transformations of the acquired data can expand the study of detectability in an imaging system. From one image, an appropriate transformation will produce a set of signals with different contrast and different frequency contents. In this work this strategy is explored to present a task-based test for the detectability of an x-ray imaging system.

METHODS

Images of a new star-bar phantom are acquired with different entrance air KERMA and with different beam qualities. Then, after a wavelet packet is applied to both input and output of the system, statistical decision theory is applied to determine detectability of the different images or nodes resulting from the transformation. An ideal Bayesian observer (IBO) is applied to the data in the spatial domain to perform ROC analysis and to determine a detectability index for each of the nodes. In addition, image quality is characterized in terms of noise equivalent quanta (NEQ) and a 5 mm nodule detection task is performed.

RESULTS

AUC maps resulting from the analysis show the area under the ROC curve over the whole 2D frequency space for the different doses and beam qualities. Also, AUC curves, obtained by radially averaging the AUC maps, allow comparing detectability of the different techniques as a function of the frequency in a single figure. The results obtained show differences between images acquired with different doses for each of the beam qualities analyzed. Classifying image quality by means of detectability indexes agrees with that of the AUC curves and the nodule detection task but differs from the NEQ for the low air KERMA images.

CONCLUSIONS

Combining a star-bar as test object, a wavelet packet as linear transformation and ROC analysis provides an appropriate task-based test for detectability performance of an imaging system. The test presented in this work produces maps and curves quantifying system detectability as a function of the frequency characterizing the signal to detect and allows calculating detectability differences between different acquisition techniques.

Low-dose dual-energy CT for stone characterization: a systematic comparison of two generations of split-filter single-source and dual-source dual-energy CT

PURPOSE

To compare noise texture and accuracy to differentiate uric acid from non-uric acid urinary stones among four different single-source and dual-source DECT approaches in an ex vivo phantom study.

METHODS

Thirty-two urinary stones embedded in gelatin were mounted on a Styrofoam disk and placed into a water-filled phantom. The phantom was imaged using four different DECT approaches: (A) dual-source DECT (DS-DE); (B) 1st generation split-filter single-source DECT (SF1-TB); (C) 2nd generation split-filter single-source DECT (SF2-TB) and (D) 2nd generation split-filter single-source DECT using serial acquisitions (SF2-TS). Two different radiation doses (3 mGy and 6 mGy) were used. Noise texture was compared by assessing the average spatial frequency (f_av) of the normalized noise power spectrum (nNPS). ROC curves for stone classification were computed and the accuracy for different dual-energy ratio cutoffs was derived.

RESULTS

NNPS demonstrated comparable noise texture among A, C, and D (f_av-range 0.18-0.19) but finer noise texture for B (f_av = 0.27). Stone classification showed an accuracy of 96.9%, 96.9%, 93.8%, 93.8% for A, B, C, D for low-dose, respectively, and 100%, 96.9%, 96.9%, 100% for routine dose. The vendor-specified cutoff for the dual-energy ratio was optimal except for the low-dose scan in D for which the accuracy was improved from 93.8 to 100% using an optimized cutoff.

CONCLUSION

Accuracy to differentiate uric acid from non-uric acid stones was high among four single-source and dual-source DECT approaches for low- and routine dose DECT scans. Noise texture differed only slightly for the first-generation split-filter approach.

Quantitative imaging of bone remodeling in patients with a unicompartmental joint unloading knee implant (ATLAS Knee System)-effect of metal artifacts on a SPECT-CT-based quantification

Background

SPECT-CT using radiolabeled phosphonates is considered a standard for assessing bone metabolism (e.g., in patients with osteoarthritis of knee joints). However, SPECT can be influenced by metal artifacts in CT caused by endoprostheses affecting attenuation correction. The current study examined the effects of metal artifacts in CT of a specific endoprosthesis design on quantitative hybrid SPECT-CT imaging.

The implant was positioned inside a phantom homogenously filled with activity (955 MBq ^99mTc). CT imaging was performed for different X-ray tube currents (I = 10, 40, 125 mA) and table pitches (p = 0.562 and 1.375). X-ray tube voltage (U = 120 kVp) and primary collimation (16 × 0.625 mm) were kept constant for all scans. The CT reconstruction was performed with five different reconstruction kernels (slice thickness, 1.25 mm and 3.75 mm, each 512 × 512 matrix). Effects from metal artifacts were analyzed for different CT scans and reconstruction protocols. ROI analysis of CT and SPECT data was performed for two slice positions/volumes representing the typical locations for target structures relative to the prosthesis (e.g., femur and tibia). A reference region (homogenous activity concentration without influence from metal artifacts) was analyzed for comparison.

Results

Significant effects caused by CT metal artifacts on attenuation-corrected SPECT were observed for the different slice positions, reconstructed slice thicknesses of CT data, and pitch and CT-reconstruction kernels used (all, p < 0.0001). Based on the optimization, a set of three protocols was identified minimizing the effect of CT metal artifacts on SPECT data. Regarding the reference region, the activity concentration in the anatomically correlated volume was underestimated by 8.9–10.1%. A slight inhomogeneity of the reconstructed activity concentration was detected inside the regions with a median up to 0.81% (p < 0.0001). Using an X-ray tube current of 40 mA showed the best result, balancing quantification and CT exposure.

Conclusion

The results of this study demonstrate the need for the evaluation of SPECT-CT protocols in prosthesis imaging. Phantom experiments demonstrated the possibility for quantitative SPECT-CT of bone turnover in a specific prosthesis design. Meanwhile, a systematic bias caused by metal implants on quantitative SPECT data has to be considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00360-z.

Task-dependent estimability index to assess the quality of cardiac computed tomography angiography for quantifying coronary stenosis

Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA.

Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index (e′) as a task-based measure of image quality in cardiac CTA. The e′ index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA).

Results: The e′ index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e′ threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training–test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective.

Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.

Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study

Purpose: To assess the physical performance of deep learning image reconstruction (DLIR) compared with those of filtered back projection (FBP) and iterative reconstruction (IR) and to estimate the dose reduction potential of the technique. Approach: A cylindrical water bath phantom with a diameter of 300 mm including two rods composed of acrylic and soft tissue-equivalent material was scanned using a clinical computed tomography (CT) scanner at four dose levels (CT dose index of 20, 15, 10, and 5 mGy). Phantom images were reconstructed using FBP, DLIR, and IR. The in-plane and z axis task transfer functions (TTFs) and in-plane noise power spectrum (NPS) were measured. The dose reduction potential was estimated by evaluating the system performance function calculated from TTF and NPS. The visibilities of a bar pattern phantom placed in the same water bath phantom were compared. Results: The use of DLIR resulted in a notable decrease in noise magnitude. The shift in peak NPS frequency was reduced compared with IR. Preservation of in-plane TTF was superior using DLIR than using IR. The estimated dose reduction potentials of DLIR and IR were 39% to 54% and 19% to 29%, respectively. However, the z axis resolution was decreased with DLIR by 6% to 21% compared with FBP. The bar pattern visibilities were approximately consistent with the TTF results in both planes. Conclusions: The in-plane edge-preserving noise reduction performance of DLIR is superior to that of IR. Moreover, DLIR enables approximately half-dose acquisitions with no deterioration in noise texture in cases that permit some z axis resolution reduction.

Technical Note: Evaluating automatic tube current modulation in CT using the standard CTDI dosimetry phantom

PURPOSE

To assess the utility of the standard body CTDI phantom in characterizing the operation scheme of tube current modulation (TCM) systems in CT.

METHODS

The body CDTI phantom was used to characterize two TCM systems: TCM₁ and TCM₂ , implemented in scanners from different vendors. The phantom was aligned at the gantry isocenter in two configurations. In configuration A, the facet planes of the phantom were parallel to the patient table, while in configuration B they were vertical to the patient table and parallel to the patient's long axis. Acquisitions were performed using the routine abdominal examination protocol. mA(z) profiles were recorded from images' DICOM header. The water equivalent diameter (d_w ) and oval ratio (OR) were calculated as a function of z-axis location. Image noise was defined as the standard deviation (SD) of the mean Hounsfield unit value measured in a region of interest at the center of the phantom's image. Regression analysis was performed to modulated mA and SD vs d_w and OR. The spatial concordance between the change in phantom size and change in mA (SC_mA ) was calculated as the percent difference in the slope of mA(z) change between the 1st and 2nd half of the phantom. The corresponding spatial concordance between the change in phantom size and change in image noise (SC_noise ) was calculated.

RESULTS

Modulated mA(z) along the z-axis did not substantially differentiate between configurations A and B. Correlation between ln(mA) and OR was found to be higher compared to correlation between ln(mA) and d_w . SC_mA was 48% for TCM₁ and 33% for TCM₂ . The corresponding SC_noise was 29% for TCM₁ and 16% for TCM₂ .

CONCLUSION

Apart from routine CT dosimetry evaluations, the standard CTDI phantom positioned in configuration A or B may additionally be used by medical physicists to evaluate the performance of TCM operational characteristics.

Role of Coronary CT Angiography in Spontaneous Coronary Artery Dissection

Spontaneous coronary artery dissection (SCAD) is more common than previously thought and is present in up to 4% of patients presenting with acute coronary syndrome. SCAD predominantly occurs in relatively young women and is an important cause of myocardial infarction in young patients without traditional risk factors of atherosclerotic coronary artery disease. There have been substantial improvements in spatial and temporal resolution and reduction in ionizing radiation dose with new generation scanners. The risk of dissection propagation with an invasive coronary angiogram, improved CT scanner parameters, and predominantly conservative management of SCAD make coronary CT angiography a useful noninvasive imaging modality for the assessment of SCAD. © RSNA, 2020.

A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks

Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: abdominal soft tissue imaging, where differentiating low contrast features is important; vascular imaging, where iodine detectability is critical; and, high-resolution skeletal and lung imaging. A multi-tiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultra-high resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.

CHARACTERIZATION OF TWO CT SYSTEMS USING A CHANNELIZED HOTELLING OBSERVER AND NPS METRIC

We investigated the performances of two computed tomography (CT) systems produced by the same manufacturers (Somatom Flash and Edge Siemens) with different detector technologies (Ultrafast Ceramic and Stellar) and different generation of iterative reconstruction (IR) algorithms (SAFIRE and ADMIRE). A homemade phantom was scanned and the images were reconstructed with filtered back-projection (FBP) and IR algorithms. In terms of image quality, the performances of the systems were checked using the low-contrast detectability, evaluated by a Channelized Hotelling Observer (CHO), and the noise power spectrum (NPS). The analysis with CHO showed the best performance of Edge respect to Flash system for both FBP and IR algorithms. This better behavior, which reaches 20%, has been ascribed to the Stellar detector. From the NPS analysis, the noise reduction due to Stellar detector was 57%, moreover ADMIRE algorithm preserves a more traditional CT image texture appearance versus SAFIRE due to a lower NPS peak shift.

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm

PURPOSE

To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm.

METHODS

Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDI_vol : 0.9, 1.2, 3.6, 7.0, and 22.3 mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (f_av ) and frequency at which the TTF was reduced to 50% (f_50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDI_vol : 1.0, 2.1, 3.0, 6.0, and 10.0 mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, η , following AAPM TG233 methods. All measurements were compared among the algorithms.

RESULTS

Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74 ± 6% and 68 ± 4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55 ± 4% lower NPS f_av compared to FBP while True Fidelity had only marginally different noise frequency content with 9 ± 5% lower NPS f_av compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0 mGy dose level η values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f_50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f_50% of up to 42% and 36%, respectively.

CONCLUSIONS

The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

1024-pixel image matrix for chest CT - Impact on image quality of bronchial structures in phantoms and patients

Objectives

To compare objective and subjective image quality of bronchial structures between a 512-pixel and a 1024-pixel image matrix for chest CT in phantoms and in patients.

Materials and methods

First, a two-size chest phantom was imaged at two radiation doses on a 192-slice CT scanner. Datasets were reconstructed with 512-, 768-, and 1024-pixel image matrices and a sharp reconstruction kernel (Bl64). Image sharpness and normalized noise power spectrum (nNPS) were quantified. Second, chest CT images of 100 patients were reconstructed with 512- and 1024-pixel matrices and two blinded readers independently assessed objective and subjective image quality. In each patient dataset, the highest number of visible bronchi was counted for each lobe of the right lung. A linear mixed effects model was applied in the phantom study and a Welch’s t-test in the patient study.

Results

Objective image sharpness and image noise increased with increasing matrix size and were highest for the 1024-matrix in phantoms and patients (all, P<0.001). nNPS was comparable among the three matrices. Objective image noise was on average 16% higher for the 1024-matrix compared to the 512-matrix in patients (P<0.0001). Subjective evaluation in patients yielded improved sharpness but increased image noise for the 1024- compared to the 512-matrix (both, P<0.001). There was no significant difference between highest-order visible bronchi (P>0.07) and the overall bronchial image quality between the two matrices (P>0.22).

Conclusion

Our study demonstrated superior image sharpness and higher image noise for a 1024- compared to a 512-pixel matrix, while there was no significant difference in the depiction and subjective image quality of bronchial structures for chest CT.

The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values

To evaluate the impact of an iterative reconstruction (IR) algorithm (advanced modeled iterative reconstruction, ADMIRE) on machine learning-based coronary computed tomography angiography-derived fractional flow reserve (CT-FFR_ML) measurements compared with filtered back projection (FBP). 170 plaque-containing vessels in 107 patients were included. CT-FFR_ML values were measured and compared among 5 imaging reconstruction algorithms (FBP and ADMIRE at strength levels of 1, 2, 3 and 5). The plaques were classified as, 'calcified" or "noncalcified" and "≥ 50% stenosis" or "< 50% stenosis', a total of four subgroups by consensus. There were no significant differences of CT-FFR_ML values among the FBP and ADMIRE 1, 2, 3 and 5 groups wherever comparisons were done at the level of subgroups (P = 0.676, 0.414, 0.849, 0.873, respectively) or overall (P = 0.072). There were 20, 21, 19, 19 and 29 vessels with lesion-specific ischemia (CT-FFR_ML ≤ 0.80) in FBP and ADMIRE 1, 2, 3 and 5 datasets, respectively, but no statistical differences were found (P = 0.437). Compared with CT-FFR_ML value of FBP dataset, the CT-FFR_ML values of 9 (5.3%) vessels from 8 patients (7.5%) in ADMIRE5 dataset switched from above 0.8 to below or equal to 0.8. There were no significant differences of the CT-FFR_ML values among the FBP and IR image algorithms at different strength levels. However, high iterative strength level (ADMIRE 5) was not recommended, which might have an impact on diagnosis of lesion-specific ischemia, although changes only occurred in a modest number of subjects.

Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data

PURPOSE

Phantoms are useful tools in diagnostic CT, but practical limitations reduce phantoms to being only a limited patient surrogate. Furthermore, a phantom with a single cross sectional area cannot be used to evaluate scanner performance in modern CT scanners that use dose reduction techniques such as automated tube current modulation (ATCM) and iterative reconstruction (IR) algorithms to adapt x-ray flux to patient size, reduce radiation dose, and achieve uniform image noise. A new multisized phantom (Mercury Phantom, MP) has been introduced, representing multiple diameters. This work aimed to ascertain if measurements from MP can predict radiation dose and image noise in clinical CT images to prospectively inform protocol design.

METHODS

The adult MP design included four different physical diameters (18.5, 23.0, 30.0, and 37.0 cm) representing a range of patient sizes. The study included 1457 examinations performed on two scanner models from two vendors, and two clinical protocols (abdominopelvic with and chest without contrast). Attenuating diameter, radiation dose, and noise magnitude (average pixel standard deviation in uniform image) was automatically estimated in patients and in the MP using a previously validated algorithm. An exponential fit of CTDI_vol and noise as a function of size was applied to patients and MP data. Lastly, the fit equations from the phantom data were used to fit the patient data. In each patient distribution fit, the normalized root mean square error (nRMSE) values were calculated in the residuals' plots as a metric to indicate how well the phantom data can predict dose and noise in clinical operations as a function of size.

RESULTS

For dose across patient size distributions, the difference between nRMSE from patient fit and MP model data prediction ranged between 0.6% and 2.0% (mean 1.2%). For noise across patient size distributions, the nRMSE difference ranged between 0.1% and 4.7% (mean 1.4%).

CONCLUSIONS

The Mercury Phantom provided a close prediction of radiation dose and image noise in clinical patient images. By assessing dose and image quality in a phantom with multiple sizes, protocol parameters can be designed and optimized per patient size in a highly constrained setup to predict clinical scanner and ATCM system performance.

Improving Low-Dose Pediatric Abdominal CT by Using Convolutional Neural Networks

Purpose

To evaluate the efficacy of convolutional neural networks (CNNs) to improve the image quality of low-dose pediatric abdominal CT images.

Materials and Methods

Images from 11 pediatric abdominal CT examinations acquired between June and July 2018 were reconstructed with filtered back projection (FBP) and an iterative reconstruction (IR) algorithm. A residual CNN was trained using the FBP image as the input and the difference between FBP and IR as the target such that the network was able to predict the residual image and simulate the IR. CNN-based postprocessing was applied to 20 low-dose pediatric image datasets acquired between December 2016 and December 2017 on a scanner limited to reconstructing FBP images. The FBP and CNN images were evaluated based on objective image noise and subjective image review by two pediatric radiologists. For each of five features, readers rated images on a five-point Likert scale and also indicated their preferred series. Readers also indicated their "overall preference" for CNN versus FBP. Preference and Likert scores were analyzed for individual and combined readers. Interreader agreement was assessed.

Results

The CT number remained unchanged between FBP and CNN images. Image noise was reduced by 31% for CNN images (P < .001). CNN was preferred for overall image quality for individual and combined readers. For combined Likert scores, at least one of the two score types (Likert or binary preference) indicated a significant favoring of CNN over FBP for low contrast, image noise, artifacts, and high contrast, whereas the reverse was true for spatial resolution.

Conclusion

FBP images can be improved in image space by a well-trained CNN, which may afford a reduction in dose or improvement in image quality on scanners limited to FBP reconstruction.© RSNA, 2019.

Development of a scanner-specific simulation framework for photon-counting computed tomography

The aim of this study was to develop and validate a simulation platform that generates photon-counting CT images of voxelized phantoms with detailed modeling of manufacturer-specific components including the geometry and physics of the x-ray source, source filtrations, anti-scatter grids, and photon-counting detectors. The simulator generates projection images accounting for both primary and scattered photons using a computational phantom, scanner configuration, and imaging settings. Beam hardening artifacts are corrected using a spectrum and threshold dependent water correction algorithm. Physical and computational versions of a clinical phantom (ACR) were used for validation purposes. The physical phantom was imaged using a research prototype photon-counting CT (Siemens Healthcare) with standard (macro) mode, at four dose levels and with two energy thresholds. The computational phantom was imaged with the developed simulator with the same parameters and settings used in the actual acquisition. Images from both the real and simulated acquisitions were reconstructed using a reconstruction software (FreeCT). Primary image quality metrics such as noise magnitude, noise ratio, noise correlation coefficients, noise power spectrum, CT number, in-plane modulation transfer function, and slice sensitivity profiles were extracted from both real and simulated data and compared. The simulator was further evaluated for imaging contrast materials (bismuth, iodine, and gadolinium) at three concentration levels and six energy thresholds. Qualitatively, the simulated images showed similar appearance to the real ones. Quantitatively, the average relative error in image quality measurements were all less than 4% across all the measurements. The developed simulator will enable systematic optimization and evaluation of the emerging photon-counting computed tomography technology.

DukeSim: A Realistic, Rapid, and Scanner-Specific Simulation Framework in Computed Tomography

The purpose of this study was to develop a CT simulation platform that is: 1) compatible with voxel-based computational phantoms; 2) capable of modeling the geometry and physics of commercial CT scanners; and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques. DukeSim includes detailed models for the detector quantum efficiency, quantum and electronic noise, detector crosstalk, subsampling of the detector and focal spot areas, focal spot wobbling, and the bowtie filter. DukeSim was accelerated using GPU computing. The platform was validated using physical and computational versions of a phantom (Mercury phantom). Clinical and simulated CT scans of the phantom were acquired at multiple dose levels using a commercial CT scanner (Somatom Definition Flash; Siemens Healthcare). The real and simulated images were compared in terms of image contrast, noise magnitude, noise texture, and spatial resolution. The relative error between the clinical and simulated images was less than 1.4%, 0.5%, 2.6%, and 3%, for image contrast, noise magnitude, noise texture, and spatial resolution, respectively, demonstrating the high realism of DukeSim. The runtime, dependent on the imaging task and the hardware, was approximately 2-3 minutes per rotation in our study using a computer with 4 GPUs. DukeSim, when combined with realistic human phantoms, provides the necessary toolset with which to perform large-scale and realistic virtual clinical trials in a patient and scanner-specific manner.

Quality evaluation of image-based iterative reconstruction for CT: Comparison with hybrid iterative reconstruction

The purpose of this study is to evaluate the physical image quality of a commercially available image‐based iterative reconstruction (IIR) system for two object contrasts to resemble a soft tissue (60 HU) and an enhanced vessel (270 HU), and compare the results with those of filtered back projection (FBP) and iterative reconstruction (IR). A 192‐slice computed tomography (CT) scanner was used for data acquisitions. IIR images were processed from the FBP images. Task‐based in‐plane transfer function (TTF) and slice sensitivity profile (SSP_task) were measured from rod objects inside of a 25‐cm diameter water phantom at four dose levels (2.5, 5, 10, and 20 mGy). Noise power spectrum (NPS) was measured from the water‐only part. System performance (SP) function was calculated as TTF²/NPS over FBP, IR, and IIR for comparison. In addition, an image subtraction was performed using images of rod objects, a bar‐pattern phantom, and a clinical abdomen case to observe the noise reduction performance of IIR. As a results, IIR mostly preserved TTF and SSP_task of FBP, whereas IR exhibited enhanced TTF at 10 and 20 mGy for 60 HU contrast and at all doses for 270 HU contrast. SP of IIR at 2.5, 5, 10 mGy (half doses) were similar to those of FBP at 5, 10, 20 mGy, respectively. IR exhibited enhanced SP at medium to high frequencies. The subtracted images showed weak remained edge signals in the bar‐pattern and abdominal images. In conclusion, IIR uniformly improved the task‐based image quality of FBP over the entire frequency range, whereas IR improved the characteristics over medium to high frequencies. The dose reduction potential of IIR estimated from SP is approximately 50%, when allowing the slight signal reductions.

Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography

We propose to characterize the bias and variability of quantitative morphology features of lung lesions across a range of computed tomography (CT) imaging conditions. A total of 15 lung lesions were simulated (five in each of three spiculation classes: low, medium, and high). For each lesion, a series of simulated CT images representing different imaging conditions were synthesized by applying three-dimensional blur and adding correlated noise based on the measured noise and resolution properties of five commercial multislice CT systems, representing three dose levels ( CTDI vol of 1.90, 3.75, 7.50 mGy), three slice thicknesses (0.625, 1.25, 2.5 mm), and 33 clinical reconstruction kernels from five clinical scanners. The images were segmented using three segmentation algorithms and each algorithm was evaluated by computing a Sørensen-Dice coefficient between the ground truth and the segmentation. A series of 21 shape-based morphology features were extracted from both "ground truth" (i.e., preblur without noise) and "image rendered" lesions (i.e., postblur and with noise). For each morphology feature, the bias was quantified by comparing the percentage relative error in the morphology metric between the imaged lesions and the ground-truth lesions. The variability was characterized by calculating the average coefficient of variation averaged across repeats and imaging conditions. The active contour segmentation had the highest average Dice coefficient of 0.80 followed by 0.63 for threshold, and 0.39 for fuzzy c-means. The bias of the features was segmentation algorithm and feature-dependent, with sharper kernels being less biased and smoother kernels being more biased in general. The feature variability from simulated images ranged from 0.30% to 10% for repeats of the same condition and from 0.74% to 25.3% for different lesions in the same spiculation class. In conclusion, the bias of morphology features is dependent on the acquisition protocol in combination with the segmentation algorithm used and the variability is primarily dependent on the segmentation algorithm.

Characterization of office laser printers for 3-D printing of soft tissue CT phantoms

The purpose of our study is to develop and evaluate a method for radiopaque 3-D printing (R3P) of soft tissue computed tomography (CT) phantoms with office laser printers. Five laser printers from different vendors are tested for toner CT attenuation. A liver phantom is created by printing CT images of a patient liver on office paper. One thousand eight hundred sixty paper sheets are printed with three repeated prints per page, resulting in a stack of 18.6 cm. The phantom is examined with 12 tube current settings. Images are reconstructed using filtered back projection (FBP) and iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Seven radiologists rated image quality of all acquisitions. Toner attenuation of all investigated printers increased linearly with the print template grayscale. The liver phantom reproduced anatomic detail and attenuation values of the patient ( mean ± SD HU difference 12.68 ± 7.74 ). Image quality scores increased with dose but did not vary significantly above a threshold dose for AIDR 3D. Overall, AIDR 3D reconstructed images are rated superior to FBP reconstructions ( p < 0.001 ). In conclusion, R3P with standard office laser printers can generate soft tissue CT phantoms without hardware manipulations but with limited flexibility regarding attenuation properties of the printed toner material.

High-Pitch Wide-Coverage Fast-Kilovoltage-Switching Dual-Energy CT: Impact of Pitch on Noise, Spatial Resolution, and Iodine Quantification in a Phantom Study

OBJECTIVE

The purpose of this study was to assess the impact of high pitch values on image noise, spatial resolution, and iodine quantification in single-source wide-coverage fast-kilovoltage-switching dual-energy CT (DECT).

MATERIALS AND METHODS

Two phantom experiments were conducted. First, image noise and spatial resolution in the x-, y-, and z-directions were assessed. Second, the accuracy of iodine quantification was investigated with multiple-size phantoms with pure iodine and blood-iodine inserts. Both phantoms were scanned repeatedly with a third-generation fast-kilovoltage-switching DECT scanner with a collimation width of 80 mm at four different pitch values (0.5, 0.99, 1.375, 1.53) and three different gantry rotation times (0.6, 0.8, 1.0 second). Image noise, spatial resolution, and absolute error of iodine concentration (E) were measured. A linear mixed effects model was used to determine the effect of pitch, rotation time, and size on the error of iodine concentration.

RESULTS

Image noise and xy spatial resolution were comparable among the four pitch values. Spatial resolution in the z-direction was inferior and had higher variance at a low pitch of 0.5 compared with pitches of 0.99, 1.375, and 1.53. Error of iodine concentration was significantly affected by pitch and rotation time (p < 0.001). E decreased with increasing pitch and decreasing rotation time. In detail, mean E was 0.91 ± 0.47 mg I/mL for a pitch of 0.5, 0.52 ± 0.29 mg I/mL for 0.99, 0.44 ± 0.25 mg I/mL for 1.375, and 0.40 ± 0.25 mg I/mL for 1.53.

CONCLUSION

High-pitch wide-coverage fast-kilovoltage-switching DECT can be performed without impairing image quality or iodine quantification, and the results are superior to those of imaging at a low pitch of 0.5.

Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT

Purpose To evaluate colorectal cancer hepatic metastasis detection and characterization between reduced radiation dose (RD) and standard dose (SD) contrast material-enhanced CT of the abdomen and to qualitatively compare between filtered back projection (FBP) and iterative reconstruction algorithms. Materials and Methods In this prospective study (from May 2017 through November 2017), 52 adults with biopsy-proven colorectal cancer and suspected hepatic metastases at baseline CT underwent two portal venous phase CT scans: SD and RD in the same breath hold. Three radiologists, blinded to examination details, performed detection and characterization of 2-15-mm lesions on the SD FBP and RD adaptive statistical iterative reconstruction (ASIR)-V 60% series images. Readers assessed overall image quality and lesions between SD FBP and seven different iterative reconstructions. Two nonblinded consensus reviewers established the reference standard using the picture archiving and communication system lesion marks of each reader, multiple comparison examinations, and clinical data. Results RD CT resulted in a mean dose reduction of 54% compared with SD. Of the 260 lesions (233 metastatic, 27 benign), 212 (82%; 95% confidence interval [CI]: 76%, 86%) were detected with RD CT, whereas 252 (97%; 95% CI: 94%, 99%) were detected with SD (P < .001); per-lesion sensitivity was 79% (95% CI: 74%, 84%) and 94% (95% CI: 90%, 96%) (P < .001), respectively. Mean qualitative scores ranked SD images as higher quality than RD series images, and ASIR-V ranked higher than ASIR and Veo 3.0. Conclusion CT evaluation of colorectal liver metastases is compromised with modest radiation dose reduction, and the use of iterative reconstructions could not maintain observer performance. © RSNA, 2018.

CT automated exposure control using a generalized detectability index

PURPOSE

Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index ( d gen ' ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.

METHODS

This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index ( d gen ' ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d gen ' metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between d gen ' and noise standard deviation. The performance of the proposed d gen ' -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images.

RESULTS

The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the d gen ' predicted by the lookup table and d ' measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the d gen ' -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the d gen ' -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P > 0.35). The d gen ' -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans.

CONCLUSIONS

A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed d gen ' -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.