Physical characterization of a new CT iterative reconstruction method operating in sinogram space

A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability

Purpose. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images.Method. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between -980 HU (Hounsfield units) and -1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images.Results. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with R2values of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves.Conclusion. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study

OBJECTIVES

To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms.

METHODS

An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions.

RESULTS

NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy.

CONCLUSIONS

DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Benefits and considerations in using a novel computed tomography system optimized for radiotherapy planning

In this study, we evaluated a novel 16-bit computed tomography (CT) system optimized for radiotherapy planning. Over six months, using various protocols, we conducted 616 scans, with an average of four CT series per session imported into our treatment planning software (TPS). The direct density (DD) reconstruction enabled a single CT number calibration curve for multiple tube voltages. Metal artifacts could be effectively reduced. The 16-bit character permitted dose calculation in high-density regions, while TPS integration challenges remained. In conclusion, our findings emphasize the system's potential benefits and considerations in radiotherapy workflows.

Ultra-low-dose non-contrast CT and CT angiography can be used interchangeably for assessing maximal abdominal aortic diameter

Background

Routine CT scans may increasingly be used to document normal aortic size and to detect incidental abdominal aortic aneurysms.

Purpose

To determine whether ultra-low-dose non-contrast CT (ULDNC-CT) can be used instead of the gold standard CT angiography (CTA) for assessment of maximal abdominal aortic diameter.

Materials and Methods

This retrospective study included 50 patients who underwent CTA and a normal-dose non-contrast CT for suspected renal artery stenosis. ULDNC-CT datasets were generated from the normal-dose non-contrast CT datasets using a simulation technique. Using the centerline technique, radiology consultants (n = 4) and residents (n = 3) determined maximal abdominal aortic diameter. The limits of agreement with the mean (LOAM) was used to access observer agreement. LOAM represents how much a measurement by a single observer may plausibly deviate from the mean of all observers on the specific subject.

Results

Observers completed 1400 measurements encompassing repeated CTA and ULDNC-CT measurements. The mean diameter was 24.0 and 25.0 mm for CTA and ULDNC-CT, respectively, yielding a significant but minor mean difference of 1.0 mm. The 95% LOAM reproducibility was similar for CTA and ULDNC-CT (2.3 vs 2.3 mm). In addition, the 95% LOAM and mean diameters were similar for CTA and ULDNC-CT when observers were grouped as consultants and residents.

Conclusions

Ultra-low-dose non-contrast CT exhibited similar accuracy and reproducibility of measurements compared with CTA for assessing maximal abdominal aortic diameter supporting that ULDNC-CT can be used interchangeably with CTA in the lower range of aortic sizes.

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.

Harmonization of technical image quality in computed tomography: comparison between different reconstruction algorithms and kernels from six scanners

Purpose. The radiology department faces a large number of reconstruction algorithms and kernels during their computed tomography (CT) optimization process. These reconstruction methods are proprietary and ensuring consistent image quality between scanners is becoming increasingly difficult. This study contributes to solving this challenge in CT image quality harmonization by modifying and evaluating a reconstruction algorithm and kernel matching scheme.Methods. The Catphan 600 phantom was scanned with six different CT scanners from four vendors. The phantom was scanned with volumetric CT dose indices (CTDIvols) of 10 mGy and 40 mGy, and the data were reconstructed using 1 mm and 5 mm slices with each combination of reconstruction algorithm, body region kernel, and iterative and deep learning reconstruction strength. A matching scheme developed in previous research, which utilizes the noise power spectrum (NPS) and modulation transfer function (MTF), was modified based on our organization's needs and used to identify the matching reconstruction algorithms and kernels between different scanners.Results. The matching paradigm produced good matching results, and the mean ± standard deviation (median) matching function values for the different acquisition settings were (a value of 1 indicates a perfect match): CTDIvol 10 mGy, 1 mm slice: 0.78 ± 0.31 (0.94); CTDIvol 10 mGy, 5 mm slice: 0.75 ± 0.33 (0.93); CTDIvol 40 mGy, 1 mm slice: 0.81 ± 0.28 (0.95); CTDIvol 40 mGy, 5 mm slice: 0.75 ± 0.33 (0.93). In general, soft reconstruction kernels, i.e., noise-reducing kernels that reduce sharpness, of one vendor were matched with the soft kernels of another vendor, and vice versa for sharper kernels. Conclusions. Combined quantitative assessment of NPS and MTF allows effective strategy for harmonization of technical image quality between different CT scanners. A software was also shared to support CT image quality harmonization in other institutions.

Technical note: Evaluation of a V‐Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability and application to patient‐specific CT dosimetry

PURPOSE

This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation.

METHODS

A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation.

RESULTS

Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%.

CONCLUSIONS

Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT dose estimation.

Effect of energy level on the spatial resolution and noise frequency characteristics of virtual monochromatic images: a phantom experiment using four types of CT scanners

PURPOSE

The purpose of the study is to evaluate the effect of energy level on the modulation transfer functions (MTF) and noise power spectra (NPS) of virtual monochromatic images (VMIs) obtained using four types of computed-tomographic (CT) scanners: Revolution, SOMATOM, IQon, and Aquilion.

MATERIALS AND METHODS

VMIs were obtained at 70, 60, and 50 kiloelectron volts (keV), and also at the lowest keV available in each scanner. We evaluated the MTF and NPS in the VMIs obtained at each keV.

RESULTS

No significant effect of the energy level on the MTF was observed in IQon, whereas the spatial resolution decreased as the energy level decreased in the other types of scanners. The NPS curves tended to increase as the energy levels decreased with three types of scanners other than Aquilion.

CONCLUSION

The spatial resolution and noise frequency characteristics of VMIs may be affected by the energy level, and the effects of energy level on these characteristics differ depending on the type of CT scanners.

Scanner dependence of adaptive statistical iterative reconstruction with 3D noise power spectrum central frequency and noise magnitude ratios

PURPOSE

In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed.

METHODS AND MATERIALS

The homogeneous module of the ACR CT phantom was scanned on the 64 and 256 slices CT scanners from the same vendor in the range of 15-40 mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFR_xy ), CFR in the z-direction (CFR_z ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFR_xy and CFR_z should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction.

RESULTS

When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0 mGy, but there was no observable dose-dependence on the 64-slice.

CONCLUSIONS

Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.

Impact of iterative reconstruction algorithms on the applicability of Fourier-based detectability index for x-ray CT imaging

PURPOSE

The increasing application of iterative reconstruction algorithms in clinical computed tomography to improve image quality and reduce radiation dose, elicits strong interest, and needs model observers to optimize CT scanning protocols objectively and efficiently. The current paradigm for evaluating imaging system performance relies on Fourier methods, which presuppose a linear, wide-sense stationary system. Long-range correlations introduced by iterative reconstruction algorithms may narrow the applicability of Fourier techniques. Differences in the implementation of reconstruction algorithms between manufacturers add further complexity. The present work set out to quantify the errors entailed by the use of Fourier methods, which can lead to design decisions that do not correlate with detectability.

METHODS

To address this question, we evaluated the noise properties and the detectability index of the ideal linear observer using the spatial approach and the Fourier-based approach. For this purpose, a homogeneous phantom was imaged on two scanners: the Revolution CT (GE Healthcare) and the Somatom Definition AS+ (Siemens Healthcare) at different exposure levels. Images were reconstructed using different strength levels of IR algorithms available on the systems considered: Adaptative Statistical Iterative Reconstruction (ASIR-V) and Sinogram Affirmed Iterative Reconstruction (SAFIRE).

RESULTS

Our findings highlight that the spatial domain estimate of the detectability index is higher than the Fourier domain estimate. This trend is found to be dependent on the specific regularization used by IR algorithms as well as the signal to be detected. The eigenanalysis of the noise covariance matrix and of its circulant approximation yields explanation about the evoked trends. In particular, this analysis suggests that the predictive power of the Fourier-based ideal linear observer depends on the ability of each basis analyzed to be relevant to the signal to be detected.

CONCLUSION

The applicability of Fourier techniques is dependent on the specific regularization used by IR algorithms. These results argue for verifying the assumptions made when using Fourier methods since Fourier-task-based detectability index does not always correlate with signal detectability.

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study

PURPOSE

To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserve the image texture of conventional filtered back projection (FBP) at reduced dose levels attained by ASIR-V in chest CT.

METHODS

Phantom images were acquired using a clinical chest protocol (7.6 mGy) and two levels of dose reduction (60% and 80%). Images were reconstructed with FBP, ASIR-V (50% and 100% blending) and TrueFidelity (low (DL-L), medium (DL-M) and high (DL-H) strength). Noise (SD), noise power spectrum (NPS) and task-based transfer function (TTF) were calculated. Noise texture was quantitatively compared by computing root-mean-square deviations (RMSD) of NPS with respect to FBP. Four experienced readers performed a contrast-detail evaluation. The dose reducing potential of TrueFidelity compared to ASIR-V was assessed by fitting SD and contrast-detail as a function of dose.

RESULTS

DL-M and DL-H reduced noise and NPS area compared to FBP and 50% ASIR-V, at all dose levels. At 7.6 mGy, NPS of ASIR-V 50/100% was shifted towards lower frequencies (f_peak = 0.22/0.13 mm^-1, RMSD = 0.14/0.38), with respect to FBP (f_peak = 0.30 mm^-1). Marginal difference was observed for TrueFidelity: f_peak = 0.33/0.30/0.30 mm^-1 and RMSD = 0.03/0.04/0.07 for L/M/H strength. Values of TTF_50% were independent of DL strength and higher compared to FBP and ASIR-V, at all dose and contrast levels. Contrast-detail was highest for DL-H at all doses. Compared to 50% ASIR-V, DL-H had an estimated dose reducing potential of 50% on average, without impairing noise, texture and detectability.

CONCLUSIONS

TrueFidelity preserves the image texture of FBP, while outperforming ASIR-V in terms of noise, spatial resolution and detectability at lower doses.

The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting

AIM

To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).

MATERIALS AND METHODS

Thirty-six patients were evaluated retrospectively. All patients underwent contrast-enhanced chest CT and thin-section images were reconstructed using filtered back projection (FBP); ASiR-V (60% and 100% blending setting); and DLIR (low, medium, and high settings). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated objectively. Two independent radiologists evaluated ASiR-V 60% and DLIR subjectively, in comparison with FBP, on a five-point scale in terms of noise, streak artefact, lymph nodes, small vessels, and overall image quality on a mediastinal window setting (width 400 HU, level 60 HU). In addition, image texture of ASiR-Vs (60% and 100%) and DLIR-high was analysed subjectively.

RESULTS

Compared with ASiR-V 60%, DLIR-med and DLIR-high showed significantly less noise, higher SNR, and higher CNR (p<0.0001). DLIR-high and ASiR-V 100% were not significantly different regarding noise (p=0.2918) and CNR (p=0.0642). At a higher DLIR setting, noise was lower and SNR and CNR were higher (p<0.0001). DLIR-high showed the best subjective scores for noise, streak artefact, and overall image quality (p<0.0001). Compared with ASiR-V 60%, DLIR-med and DLIR-high scored worse in the assessment of small vessels (p<0.0001). The image texture of DLIR-high was significantly finer than that of ASIR-Vs (p<0.0001).

CONCLUSIONS

DLIR-high improved the objective parameters and subjective image quality by reducing noise and streak artefacts and providing finer image texture.

CT iterative vs deep learning reconstruction: comparison of noise and sharpness

OBJECTIVES

To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI).

METHODS

Thirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed.

RESULTS

The higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images (p < 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images.

CONCLUSION

TF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations.

KEY POINTS

• Deep learning image reconstruction "TrueFidelity" is superior to iterative reconstruction "ASIR-V" regarding image noise and sharpness. • The high-strength "TrueFidelity" approach generated the best image quality among the examined image reconstruction procedures. • In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.

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.

Comparisons of Hounsfield Unit Linearity between Images Reconstructed using an Adaptive Iterative Dose Reduction (AIDR) and a Filter Back-Projection (FBP) Techniques

Background:

The HU linearity is an essential parameter in a quantitative imaging and the treatment planning systems of radiotherapy.

Objective:

This study aims to evaluate the linearity of Hounsfield unit (HU) in applying the adaptive iterative dose reduction (AIDR) on CT scanner and its comparison to the filtered back-projection (FBP).

Material and Methods:

In this experimental phantom study, a TOS-phantom was scanned using a Toshiba Alexion 6 CT scanner. The images were reconstructed using the FBP and AIDR. Measurements of HU and noise values were performed on images of the “HU linearity” module of the TOS-phantom. The module had five embedded objects, i.e., air, polypropylene, nylon, acrylic, and Delrin. On each object, a circle area of 4.32 cm² was drawn and used to measure HU and noise values. The R² of the relation between mass densities vs. HU values was used to measure HU linearities at four different tube voltages. The Mann-Whitney U test was used to compare unpaired data and p-value < 0.05 was considered statistically significant.

Results:

The AIDR method produced a significant smaller image noise than the FBP method (p-value < 0.05). There were no significant differences in HU values of images reconstructed using FBP and AIDR methods (p-value > 0.05). The HU values acquired by the methods showed the same linearity marked by coinciding linear lines with the same R² value (> 0.999).

Conclusion:

AIDR methods produce the HU linearity as FBP methods with a smaller image noise level.

Comparison of different iterative CT reconstruction techniques and filtered back projection for assessment of the medial clavicular epiphysis in forensic age estimation

PURPOSE

To assess the impact of iterative reconstruction and filtered back projection (FBP) on image quality in computed tomography (CT)-based forensic age estimation of the medial clavicular epiphysis.

METHODS AND MATERIALS

CT of the clavicle was performed in 19 patients due to forensic reasons (70 mAs/140 kVp). Raw data were reconstructed with FBP and with an iterative algorithm at level 4 and 6. Clavicular ossification stage was determined by two radiologists in consensus, firstly on FBP reconstructed images and secondly after reviewing all reconstructions including iDose 4 and 6. In addition, the 3 reconstructions were compared regarding artefacts and delineation of the meta-/epiphyseal interface. Quantitative image noise was measured.

RESULTS

Quantitative noise was lower in iDose 6 reconstructed images than in FBP (P < 0.042), but not significantly lower between iDose 4 and FBP (P = 0.127). Side by side comparison revealed lesser qualitative image noise on both iDose reconstructed images than for FBP. The meta-/epiphyseal interface delineation was rated better on both iDose levels than with FBP. In 3 of 19 patients, the clavicular ossification stage was reclassified after iterative reconstructions had been additionally reviewed.

CONCLUSION

Using iterative CT reconstruction algorithms, a reduction of image noise and an enhancement of image quality regarding the meta-/epiphyseal clavicular interface can be achieved. The study highlights the importance of image standardization as variation of reconstruction technique has impact on forensic age estimation.

Dose Reduction While Preserving Diagnostic Quality in Head CT: Advancing the Application of Iterative Reconstruction Using a Live Animal Model

BACKGROUND AND PURPOSE

Iterative reconstruction has promise in lowering the radiation dose without compromising image quality, but its full potential has not yet been realized. While phantom studies cannot fully approximate the subjective effects on image quality, live animal models afford this assessment. We characterize dose reduction in head CT by applying advanced modeled iterative reconstruction (ADMIRE) in a live ovine model while evaluating preservation of gray-white matter detectability and image texture compared with filtered back-projection.

MATERIALS AND METHODS

A live sheep was scanned on a Force CT scanner (Siemens) at 12 dose levels (82-982 effective mAs). Images were reconstructed with filtered back-projection and ADMIRE (strengths, 1-5). A total of 72 combinations (12 doses × 6 reconstructions) were evaluated qualitatively for resemblance to the reference image (highest dose with filtered back-projection) using 2 metrics: detectability of gray-white matter differentiation and noise-versus-smoothness in image texture. Quantitative analysis for noise, SNR, and contrast-to-noise was also performed across all dose-strength combinations.

RESULTS

Both qualitative and quantitative results confirm that gray-white matter differentiation suffers at a lower dose but recovers when complemented by higher iterative reconstruction strength, and image texture acquires excessive smoothness with a higher iterative reconstruction strength but recovers when complemented by dose reduction. Image quality equivalent to the reference image is achieved by a 58% dose reduction with ADMIRE-5.

CONCLUSIONS

An approximately 60% dose reduction may be possible while preserving diagnostic quality with the appropriate dose-strength combination. This in vivo study can serve as a useful guide for translating the full implementation of iterative reconstruction in clinical practice.

Dose-optimized computed tomography of the cervical spine in patients with shoulder pull-down: Is image quality comparable with a standard dose protocol in an emergency setting?

PURPOSE

Superimposing soft tissue and bony structures in computed tomography (CT) of the cervical spine (C-spine) is a limiting factor in optimizing radiation exposure maintaining an acceptable image quality. Therefore, we assessed image quality of dose-optimized (DO) C-spine CT in patients capable of shoulder pull-down in an emergency setting.

METHODS AND MATERIALS

DO-CT (105mAs/120 kVp) of the C-spine in trauma settings was performed in patients with shoulder pull-down if C5 was not superimposed by soft tissue on the lateral topogram, otherwise standard-dose (SD)-CT (195 mAs/120 kVp) was performed. 34 DO (mean age, 68y ± 21; BMI, 24.2 kg/m² ± 3.2) and 34 SD (mean age 70y ± 19; BMI 25.7 kg/m² ± 4.4) iterative reconstructed CTs were evaluated at C2/3 and C6/7 by two musculoskeletal radiologists. Qualitative image noise and morphological characteristics of bony structures (cortex, trabeculae) were assessed on a Likert scale. Quantitative image noise was measured and effective dose (ED) was recorded. Parameters were compared using Mann-Whitney-U-test (p < 0.05).

RESULTS

At C2/3, DO-CT vs. SD-CT yielded comparable qualitative noise (mean, 1.3 vs. 1.0; p = 0.18) and morphological characteristics, but higher quantitative noise (27.2 ± 8.8HU vs. 19.6 ± 4.5HU; p < 0.001). At C6/7, DO-CT yielded lower subjective noise (1.9; SD-CT 2.2; p = 0.017) and better morphological characteristics with higher visibility scores for cortex (p = 0.001) and trabeculae (p = 0.03). Quantitative noise did not differ (p = 0.24). Radiation dose was 51% lower using DO-CT (ED_DO-CT 0.80 ± 0.1 mSv; ED_SD-CT 1.63 ± 0.2 mSv; p < 0.001).

CONCLUSION

C-spine CT with dose reduction of 51% showed no image quality impairment. Additional pull-down of both shoulders allowed better image quality at lower C-spine segments as compared to a standard protocol.

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.

LOW-DOSE COMPUTED TOMOGRAPHY OF THE PARANASAL SINUSES: PERFORMANCE OF TWO DIFFERENT ITERATIVE RECONSTRUCTION ALGORITHMS

To evaluate the performance of two iterative reconstruction algorithms in low-dose paranasal sinus computed tomography (CT). Sinus CT scans were reconstructed using Adaptive Iterative Dose Reduction 3D (AIDR 3D, n = 36 patients) or Sinogram Affirmed Iterative Reconstruction (SAFIRE, n = 32 patients). Reconstructed images were evaluated regarding subjective image quality, depiction of anatomic landmarks and noise (HU). Dose-length product (DLP), calculated effective dose (ED) and CT dose index (CTDIvol) were documented for each scan. Images were not significantly different in subjective image quality (p = 0.09) and conspicuity of anatomic landmarks (p = 0.28). Noise was significantly lower in images reconstructed with AIDR 3D (p = 0.012). DLP, ED and CTDIvol were significantly lower in the SAFIRE datasets (each p < 0.001). The results indicate that iterative reconstruction, independent of the manufacturer, enables for imaging the paranasal sinuses with an ED below 0.1 mSv while ensuring diagnostic image quality.

Advanced Modeled Iterative Reconstruction (ADMIRE) Facilitates Radiation Dose Reduction in Abdominal CT

RATIONALE AND OBJECTIVES

This study aimed to determine the potential degree of radiation dose reduction achievable using Advanced Modeled Iterative Reconstruction (ADMIRE) in abdominal computed tomography (CT) while maintaining image quality. Moreover, this study compared differences in image noise reduction of this iterative algorithm with radiation dose reduction.

METHODS

Eleven consecutive patients scheduled for abdominal CT were scanned according to our institute's standard protocol (100 kV, 289 reference mAs). Using a proprietary reconstruction software, CT images of these patients were reconstructed as either full-dose weighted filtered back projections or with simulated radiation dose reductions down to 10% of the full-dose level and ADMIRE at either strength 3 or strength 5. Images were marked with arrows pointing on anatomic structures of the abdomen, differing in their contrast to the surrounding tissue. Structures were grouped into high-, medium-, and low-contrast subgroups. In addition, the intrinsic noise of these structures was measured. That followed, image pairs were presented to observers, with five readers assessing image quality using two-alternative-forced-choice comparisons. In total, 3000 comparisons were performed that way.

RESULTS

Both ADMIRE 3 and 5 decreased noise of the anatomic structures significantly compared to the filtered back projection, with an additional significant difference between ADMIRE 3 and 5. Radiation dose reduction potential for ADMIRE ranged from 29.0% to 53.5%, with no significant differences between ADMIRE 3 and 5 within the contrast subgroups.The potential levels of radiation dose reduction for ADMIRE 3 differed significantly between high-, medium-, and low-contrast structures, whereas for ADMIRE 5, there was only a significant difference between the high- and the medium-contrast subgroups.

CONCLUSION

Although ADMIRE 5 permits significantly higher noise reduction potential than ADMIRE 3, it does not facilitate higher levels of radiation dose reduction. ADMIRE nonetheless holds remarkable potential for radiation dose reduction, which features a certain dependency on the contrast of the structure of interest. Applying ADMIRE with a strength of 3 in abdominal CT may permit radiation dose reduction of about 30%.

Application of fractal dimension for quantifying noise texture in computed tomography images

PURPOSE

Evaluation of noise texture information in CT images is important for assessing image quality. Noise texture is often quantified by the noise power spectrum (NPS), which requires numerous image realizations to estimate. This study evaluated fractal dimension for quantifying noise texture as a scalar metric that can potentially be estimated using one image realization.

METHODS

The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120 kV and 25 and 90 mAs. Images were reconstructed using filtered back projection (FBP/ASIR 0%) with varying reconstruction kernels: Soft, Standard, Detail, Chest, Lung, Bone, and Edge. For each kernel, images were also reconstructed using ASIR 50% and ASIR 100% iterative reconstruction (IR) methods. Fractal dimension was estimated using the differential box-counting algorithm applied to images of the uniform section of ACR phantom. The two-dimensional Noise Power Spectrum (NPS) and one-dimensional-radially averaged NPS were estimated using established techniques. By changing the radiation dose, the effect of noise magnitude on fractal dimension was evaluated. The Spearman correlation between the fractal dimension and the frequency of the NPS peak was calculated. The number of images required to reliably estimate fractal dimension was determined and compared to the number of images required to estimate the NPS-peak frequency. The effect of Region of Interest (ROI) size on fractal dimension estimation was evaluated. Feasibility of estimating fractal dimension in an anthropomorphic phantom and clinical image was also investigated, with the resulting fractal dimension compared to that estimated within the uniform section of the ACR phantom.

RESULTS

Fractal dimension was strongly correlated with the frequency of the peak of the radially averaged NPS curve, having a Spearman rank-order coefficient of 0.98 (P-value < 0.01) for ASIR 0%. The mean fractal dimension at ASIR 0% was 2.49 (Soft), 2.51 (Standard), 2.52 (Detail), 2.57 (Chest), 2.61 (Lung), 2.66 (Bone), and 2.7 (Edge). A reduction in fractal dimension was observed with increasing ASIR levels for all investigated reconstruction kernels. Fractal dimension was found to be independent of noise magnitude. Fractal dimension was successfully estimated from four ROIs of size 64 × 64 pixels or one ROI of 128 × 128 pixels. Fractal dimension was found to be sensitive to non-noise structures in the image, such as ring artifacts and anatomical structure. Fractal dimension estimated within a uniform region of an anthropomorphic phantom and clinical head image matched that estimated within the ACR phantom for filtered back projection reconstruction.

CONCLUSIONS

Fractal dimension correlated with the NPS-peak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 × 64-pixel ROIs or one 128 × 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images.

Experience With Iterative Reconstruction Techniques for Abdominopelvic Computed Tomography in Morbidly and Super Obese Patients

OBJECTIVE

The aim of this study was to investigate the diagnostic performance of abdominopelvic computed tomography (CT) images reconstructed using filtered back projection (FBP) and iterative reconstruction (IR) algorithms in morbidly and super obese patients.

MATERIALS AND METHODS

One hundred eighty-seven abdominopelvic CT examinations in portal venous phase were performed between February 2015 and February 2016 in 182 patients (mean age = 52 years, mean body mass index = 45.5). One hundred fourteen of 187 examinations were reconstructed using IR and 73 examinations were processed using FBP. Patients were further stratified based on body mass index. Sixty CT scans were reviewed by a single reader for image quality, image noise, and artifacts. Objective noise and attenuation were also determined. Size-specific dose estimate and CT dose index volume were compared and statistically analyzed.

RESULTS

A diagnostic interpretation was rendered for all 187 examinations. A single-reader review of 60 cases showed greater diagnostic acceptability for IR when compared with FBP (image quality = 4.2 and 3.8 [P = 0.035], noise = 1.5 and 1.6 [P = 0.692], artifact = 1.4 and 1.5 [P = 0.759], respectively). For all examinations, the IR group had lower objective image noise (IR = 9.3 and FBP = 14.3; P < 0.001) and higher contrast-to-noise ratio (IR = 17.2 and FBP = 11.7; P < 0.001) without increase in radiation dose (size-specific dose estimate [IR = 15.1, FBP = 16.5 mGy; P = 0.045] and CT dose index volume [IR = 17.6, FBP = 18 mGy; P = 0.62]).

CONCLUSIONS

In morbidly and super obese patients, diagnostic quality images could be reliably generated with minimal artifacts and noise using newer generation scanners integrated with IR without increasing radiation dose.

Systematic Radiation Dose Reduction in Cervical Spine CT of Human Cadaveric Specimens: How Low Can We Go?

BACKGROUND AND PURPOSE

While the use of cervical spine CT in trauma settings has increased, the balance between image quality and dose reduction remains a concern. The purpose of our study was to compare the image quality of CT of the cervical spine of cadaveric specimens at different radiation dose levels.

MATERIALS AND METHODS

The cervical spine of 4 human cadavers (mean body mass index; 30.5 ± 5.2 kg/m²; range, 24-36 kg/m²) was examined using different reference tube current-time products (45, 75, 105, 135, 150, 165, 195, 275, 355 mAs) and a tube voltage of 120 kV(peak). Data were reconstructed with filtered back-projection and iterative reconstruction. Qualitative image noise and morphologic characteristics of bony structures were quantified on a Likert scale. Quantitative image noise was measured. Statistics included analysis of variance and the Tukey test.

RESULTS

Compared with filtered back-projection, iterative reconstruction provided significantly lower qualitative (mean noise score: iterative reconstruction = 2.10/filtered back-projection = 2.18; P = .003) and quantitative (mean SD of Hounsfield units in air: iterative reconstruction = 30.2/filtered back-projection = 51.8; P < .001) image noise. Image noise increased as the radiation dose decreased. Qualitative image noise at levels C1-4 was rated as either "no noise" or as "acceptable noise." Any shoulder position was at level C5 and caused more artifacts at lower levels. When we analyzed all spinal levels, scores for morphologic characteristics revealed no significant differences between 105 and 355 mAs (P = .555), but they were worse in scans at 75 mAs (P = .025).

CONCLUSIONS

Clinically acceptable image quality of cervical spine CTs for evaluation of bony structures of cadaveric specimens with different body habitus can be achieved with a reference mAs of 105 at 120 kVp with iterative reconstruction. Pull-down of shoulders during acquisition could improve image quality but may not be feasible in trauma patients with unknown injuries.

Impact of iterative reconstruction on image quality of low-dose CT of the lumbar spine

Background Iterative reconstruction (IR) is a recent reconstruction algorithm for computed tomography (CT) that can be used instead of the standard algorithm, filtered back projection (FBP), to reduce radiation dose and/or improve image quality. Purpose To evaluate and compare the image quality of low-dose CT of the lumbar spine reconstructed with IR to conventional FBP, without further reduction of radiation dose. Material and Methods Low-dose CT on 55 patients was performed on a Siemens scanner using 120 kV tube voltage, 30 reference mAs, and automatic dose modulation. From raw CT data, lumbar spine CT images were reconstructed with a medium filter (B41f) using FBP and four levels of IR (levels 2-5). Five reviewers scored all images on seven image quality criteria according to the European guidelines on quality criteria for CT, using a five-grade scale. A side-by-side comparison was also performed. Results There was significant improvement in image quality for IR (levels 2-4) compared to FBP. According to visual grading regression, odds ratios of all criteria with 95% confidence intervals for IR2, IR3, IR4, and IR5 were: 1.59 (1.39-1.83), 1.74 (1.51-1.99), 1.68 (1.46-1.93), and 1.08 (0.94-1.23), respectively. In the side-by-side comparison of all reconstructions, images with IR (levels 2-4) received the highest scores. The mean overall CTDI_vol was 1.70 mGy (SD 0.46; range, 1.01-3.83 mGy). Image noise decreased in a linear fashion with increased strength of IR. Conclusion Iterative reconstruction at levels 2, 3, and 4 improves image quality of low-dose CT of the lumbar spine compared to FPB.

Reducing radiation dose and enhancing imaging quality of 4DCT for radiation therapy using iterative reconstruction algorithms

Purpose

Four-dimensional computed tomography (CT) images are typically used to quantify the necessary internal target volumes for thoracic and abdominal tumors. However, 4-dimensional CT is typically associated with excessive imaging dose to patients and the situation is exacerbated when using repeat 4-dimensional CT imaging on a weekly or daily basis throughout fractionated therapy. The aim of this work is to evaluate an iterative reconstruction (IR) algorithm that helps reduce the imaging dose to the patient while maintaining imaging quality as quantified by point spread function and contrast-to-noise ratios (CNRs).

Methods and materials

An IR algorithm, SAFIRE, was applied to CT data of a phantom and patients with varying CT doses and reconstruction kernels. Phantom data enable measurements of spatial resolution, contrast, and noise. The impact of SAFIRE on 4-dimensional CT was assessed with patient data acquired at 2 different dose levels during image guided radiation therapy with an in-room CT.

Results

Phantom data demonstrate that IR reduces noise approximately in proportion to the number of iterations indicated by the strength (SAFIRE 1 to SAFIRE 5). Spatial resolution and contrast are conserved independent of dose and reconstruction parameters. The CNR increases with an increase of imaging dose or an increase in the number of iterations. The use of IR on CT sets confirms the results that were derived from phantom scans. The IR significantly enhances single breathing phase CTs in 4-dimensional CT sets as assessed by CT number discrimination. Furthermore, the IR of the low dose 4-dimensional CT features a 45% increase in the CNR in comparison with the standard dose 4-dimensional CT.

Conclusions

The use of IR algorithms reduces noise while preserving spatial resolution and contrast, as evaluated from both phantom and patient CT data sets. For 4-dimensional CT, the IR can significantly improve image quality and reduce imaging dose without compromising image quality.

Evaluation of automatic image quality assessment in chest CT - A human cadaver study

PURPOSE

The evaluation of clinical image quality (IQ) is important to optimize CT protocols and to keep patient doses as low as reasonably achievable. Considering the significant amount of effort needed for human observer studies, automatic IQ tools are a promising alternative. The purpose of this study was to evaluate automatic IQ assessment in chest CT using Thiel embalmed cadavers.

METHODS

Chest CT's of Thiel embalmed cadavers were acquired at different exposures. Clinical IQ was determined by performing a visual grading analysis. Physical-technical IQ (noise, contrast-to-noise and contrast-detail) was assessed in a Catphan phantom. Soft and sharp reconstructions were made with filtered back projection and two strengths of iterative reconstruction. In addition to the classical IQ metrics, an automatic algorithm was used to calculate image quality scores (IQs). To be able to compare datasets reconstructed with different kernels, the IQs values were normalized.

RESULTS

Good correlations were found between IQs and the measured physical-technical image quality: noise (ρ=-1.00), contrast-to-noise (ρ=1.00) and contrast-detail (ρ=0.96). The correlation coefficients between IQs and the observed clinical image quality of soft and sharp reconstructions were 0.88 and 0.93, respectively.

CONCLUSIONS

The automatic scoring algorithm is a promising tool for the evaluation of thoracic CT scans in daily clinical practice. It allows monitoring of the image quality of a chest protocol over time, without human intervention. Different reconstruction kernels can be compared after normalization of the IQs.

CT image quality in sinogram affirmed iterative reconstruction phantom study - is there a point of diminishing returns?

BACKGROUND

In our pediatric practice, we have observed qualitatively limited improvement in the image quality of images generated with sinogram affirmed iterative reconstruction (SAFIRE) compared to series generated with filtered back projection (FBP), particularly in cases near or below a CT dose index volume (CTDI_vol) of 1-mGy.

OBJECTIVE

To determine whether the image quality advantage of SAFIRE remains constant across clinically used CT dose levels in an American College of Radiology (ACR) CT accreditation phantom including the lower dose range used in pediatric imaging.

MATERIALS AND METHODS

An exemption from institutional review board approval was obtained for this phantom-based study. An ACR quality phantom was scanned in incremental kV steps and effective tube current intervals. Acquisitions were reconstructed with FBP and SAFIRE strengths of 1, 3 and 5. Image quality measures were calculated including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), low-contrast resolution and high-contrast resolution. Peak SNR was also calculated. Descriptive and nonparametric statistics were used to compare these image quality metrics while normalizing to CT dose index (CTDI).

RESULTS

The percent improvement in SNR and peak SNR of SAFIRE reconstructions compared to FBP decreased from about 70% for image sets acquired above a 1.42 mGy CTDI to 25% at a 0.25 mGy CTDI. CNR improvement with SAFIRE did not vary with dose. No significant difference was seen in the low-contrast resolution or high-contrast resolution of SAFIRE images compared to FBP.

CONCLUSION

SNR did not improve equally after applying SAFIRE across a spectrum clinically used CTDIs. Below a threshold CTDI, the incremental improvement of SAFIRE compared to FBP decreased.

Effect of Radiation Dose Reduction and Reconstruction Algorithm on Image Noise, Contrast, Resolution, and Detectability of Subtle Hypoattenuating Liver Lesions at Multidetector CT: Filtered Back Projection versus a Commercial Model-based Iterative Reconstruction Algorithm

Purpose To determine the effect of radiation dose and iterative reconstruction (IR) on noise, contrast, resolution, and observer-based detectability of subtle hypoattenuating liver lesions and to estimate the dose reduction potential of the IR algorithm in question. Materials and Methods This prospective, single-center, HIPAA-compliant study was approved by the institutional review board. A dual-source computed tomography (CT) system was used to reconstruct CT projection data from 21 patients into six radiation dose levels (12.5%, 25%, 37.5%, 50%, 75%, and 100%) on the basis of two CT acquisitions. A series of virtual liver lesions (five per patient, 105 total, lesion-to-liver prereconstruction contrast of -15 HU, 12-mm diameter) were inserted into the raw CT projection data and images were reconstructed with filtered back projection (FBP) (B31f kernel) and sinogram-affirmed IR (SAFIRE) (I31f-5 kernel). Image noise (pixel standard deviation), lesion contrast (after reconstruction), lesion boundary sharpness (average normalized gradient at lesion boundary), and contrast-to-noise ratio (CNR) were compared. Next, a two-alternative forced choice perception experiment was performed (16 readers [six radiologists, 10 medical physicists]). A linear mixed-effects statistical model was used to compare detection accuracy between FBP and SAFIRE and to estimate the radiation dose reduction potential of SAFIRE. Results Compared with FBP, SAFIRE reduced noise by a mean of 53% ± 5, lesion contrast by 12% ± 4, and lesion sharpness by 13% ± 10 but increased CNR by 89% ± 19. Detection accuracy was 2% higher on average with SAFIRE than with FBP (P = .03), which translated into an estimated radiation dose reduction potential (±95% confidence interval) of 16% ± 13. Conclusion SAFIRE increases detectability at a given radiation dose (approximately 2% increase in detection accuracy) and allows for imaging at reduced radiation dose (16% ± 13), while maintaining low-contrast detectability of subtle hypoattenuating focal liver lesions. This estimated dose reduction is somewhat smaller than that suggested by past studies. ^© RSNA, 2017 Online supplemental material is available for this article.

Objective performance assessment of five computed tomography iterative reconstruction algorithms

OBJECTIVE

Iterative algorithms are gaining clinical acceptance in CT. We performed objective phantom-based image quality evaluation of five commercial iterative reconstruction algorithms available on four different multi-detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back-projection (FBP) reconstruction.

METHODS

Using the Catphan500 phantom, we evaluated image noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF) and noise-power spectrum (NPS). The algorithms were evaluated over a CTDIvol range of 0.75-18.7 mGy on four major MDCT scanners: GE DiscoveryCT750HD (algorithms: ASIR™ and VEO™); Siemens Somatom Definition AS+ (algorithm: SAFIRE™); Toshiba Aquilion64 (algorithm: AIDR3D™); and Philips Ingenuity iCT256 (algorithm: iDose4™). Images were reconstructed using FBP and the respective iterative algorithms on the four scanners.

RESULTS

Use of iterative algorithms decreased image noise and increased CNR, relative to FBP. In the dose range of 1.3-1.5 mGy, noise reduction using iterative algorithms was in the range of 11%-51% on GE DiscoveryCT750HD, 10%-52% on Siemens Somatom Definition AS+, 49%-62% on Toshiba Aquilion64, and 13%-44% on Philips Ingenuity iCT256. The corresponding CNR increase was in the range 11%-105% on GE, 11%-106% on Siemens, 85%-145% on Toshiba and 13%-77% on Philips respectively. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. A shift in the peak of the NPS curve towards lower frequencies and a decrease in NPS amplitude were obtained with all iterative algorithms. VEO™ required long reconstruction times, while all other algorithms produced reconstructions in real time. Compared to FBP, iterative algorithms reduced image noise and increased CNR.

CONCLUSIONS

The iterative algorithms available on different scanners achieved different levels of noise reduction and CNR increase while spatial resolution improvements were obtained only with VEO™. This study is useful in that it provides performance assessment of the iterative algorithms available from several mainstream CT manufacturers.

Ultra-low-dose chest CT with iterative reconstruction does not alter anatomical image quality

PURPOSE

To evaluate the effect of dose reduction with iterative reconstruction (IR) on image quality of chest CT scan.

MATERIALS AND METHODS

Eighteen human cadavers had chest CT with one reference CT protocol (RP-CT; 120kVp/200mAs) and two protocols with dose reduction: low-dose-CT (LD-CT; 120kVp/40mAs) and ultra-low-dose CT (ULD-CT; 120kVp/10mAs). Data were reconstructed with filter-back-projection (FBP) for RP-CT and with FBP and IR (sinogram affirmed iterative reconstruction [SAFIRE^®]) algorithm for LD-CT and ULD-CT. Volume CT dose index (CTDIvol) were recorded. The signal-to-noise (SNR), contrast-to-noise (CNR) ratios of LD-CT and ULD-CT and quantitative parameters were compared to RP-CT. Two radiologists reviewed the CT examinations assessed independently the quality of anatomical structures and expressed a confidence level using a 2-point scale (50% and 95%).

RESULTS

CTDIvol was 2.69 mGy for LD-CT (-80%; P<0.01) and 0.67 mGy for ULD-CT (-95%; P<0.01) as compared to 13.42 mGy for RP-CT. SNR and CNR were significantly decreased (P<0.01) for LD-CT and ULD-CT, but IR improved these values satisfactorily. No significant differences were observed for quantitative measurements. Radiologists rated excellent/good the RP-CT and LD-CT images, whereas good/fair the ULD-CT images. Confidence level for subjective anatomical analysis was 95% for all protocols.

CONCLUSIONS

Dose reduction with a dose lower than 1 mGy, used in conjunction with IR allows performing chest CT examinations that provide a high quality of anatomical structures.

Practical considerations for noise power spectra estimation for clinical CT scanners

Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose⁴ iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ∼0.15 mm−1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low‐frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task‐based image quality assessment.

PACS number(s): 87.57.C‐, 87.57.Q‐

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

PURPOSE

The purpose of this work was to assess the inherent image quality characteristics of a new multidetector computed tomography system in terms of noise, resolution, and detectability index as a function of image acquisition and reconstruction for a range of clinically relevant settings.

METHODS

A multisized image quality phantom (37, 30, 23, 18.5, and 12 cm physical diameter) was imaged on a SOMATOM Force scanner (Siemens Medical Solutions) under variable dose, kVp, and tube current modulation settings. Images were reconstructed with filtered back projection (FBP) and with advanced modeled iterative reconstruction (ADMIRE) with iterative strengths of 3, 4, and 5. Image quality was assessed in terms of the noise power spectrum (NPS), task transfer function (TTF), and detectability index for a range of detection tasks (contrasts of approximately 45, 90, 300, -900, and 1000 HU, and 2-20 mm diameter) based on a non-prewhitening matched filter model observer with eye filter.

RESULTS

Image noise magnitude decreased with decreasing phantom size, increasing dose, and increasing ADMIRE strength, offering up to 64% noise reduction relative to FBP. Noise texture in terms of the NPS was similar between FBP and ADMIRE (<5% shift in peak frequency). The resolution, based on the TTF, improved with increased ADMIRE strength by an average of 15% in the TTF 50% frequency for ADMIRE-5. The detectability index increased with increasing dose and ADMIRE strength by an average of 55%, 90%, and 163% for ADMIRE 3, 4, and 5, respectively. Assessing the impact of mA modulation for a fixed average dose over the length of the phantom, detectability was up to 49% lower in smaller phantom sections and up to 26% higher in larger phantom sections for the modulated scan compared to a fixed tube current scan. Overall, the detectability exhibited less variability with phantom size for modulated scans compared to fixed tube current scans.

CONCLUSIONS

Image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose. The use of tube current modulation resulted in more consistent image quality with changing phantom size.

Effect of reconstruction methods and x-ray tube current-time product on nodule detection in an anthropomorphic thorax phantom: A crossed-modality JAFROC observer study

Purpose:

To evaluate nodule detection in an anthropomorphic chest phantom in computed tomography (CT) images reconstructed with adaptive iterative dose reduction 3D (AIDR^3D) and filtered back projection (FBP) over a range of tube current–time product (mAs).

Methods:

Two phantoms were used in this study: (i) an anthropomorphic chest phantom was loaded with spherical simulated nodules of 5, 8, 10, and 12 mm in diameter and +100, −630, and −800 Hounsfield units electron density; this would generate CT images for the observer study; (ii) a whole-body dosimetry verification phantom was used to ultimately estimate effective dose and risk according to the model of the BEIR VII committee. Both phantoms were scanned over a mAs range (10, 20, 30, and 40), while all other acquisition parameters remained constant. Images were reconstructed with both AIDR^3D and FBP. For the observer study, 34 normal cases (no nodules) and 34 abnormal cases (containing 1–3 nodules, mean 1.35 ± 0.54) were chosen. Eleven observers evaluated images from all mAs and reconstruction methods under the free-response paradigm. A crossed-modality jackknife alternative free-response operating characteristic (JAFROC) analysis method was developed for data analysis, averaging data over the two factors influencing nodule detection in this study: mAs and image reconstruction (AIDR^3D or FBP). A Bonferroni correction was applied and the threshold for declaring significance was set at 0.025 to maintain the overall probability of Type I error at α = 0.05. Contrast-to-noise (CNR) was also measured for all nodules and evaluated by a linear least squares analysis.

Results:

For random-reader fixed-case crossed-modality JAFROC analysis, there was no significant difference in nodule detection between AIDR^3D and FBP when data were averaged over mAs [F(1, 10) = 0.08, p = 0.789]. However, when data were averaged over reconstruction methods, a significant difference was seen between multiple pairs of mAs settings [F(3, 30) = 15.96, p < 0.001]. Measurements of effective dose and effective risk showed the expected linear dependence on mAs. Nodule CNR was statistically higher for simulated nodules on images reconstructed with AIDR^3D (p < 0.001).

Conclusions:

No significant difference in nodule detection performance was demonstrated between images reconstructed with FBP and AIDR^3D. mAs was found to influence nodule detection, though further work is required for dose optimization.

Correlation of clinical and physical-technical image quality in chest CT: a human cadaver study applied on iterative reconstruction

Background

The first aim of this study was to evaluate the correlation between clinical and physical-technical image quality applied to different strengths of iterative reconstruction in chest CT images using Thiel cadaver acquisitions and Catphan images. The second aim was to determine the potential dose reduction of iterative reconstruction compared to conventional filtered back projection based on different clinical and physical-technical image quality parameters.

Methods

Clinical image quality was assessed using three Thiel embalmed human cadavers. A Catphan phantom was used to assess physical-technical image quality parameters such as noise, contrast-detail and contrast-to-noise ratio (CNR).

Both Catphan and chest Thiel CT images were acquired on a multislice CT scanner at 120 kVp and 0.9 pitch. Six different refmAs settings were applied (12, 30, 60, 90, 120 and 150refmAs) and each scan was reconstructed using filtered back projection (FBP) and iterative reconstruction (SAFIRE) algorithms (1,3 and 5 strengths) using a sharp kernel, resulting in 24 image series. Four radiologists assessed the clinical image quality, using a visual grading analysis (VGA) technique based on the European Quality Criteria for Chest CT.

Results

Correlation coefficients between clinical and physical-technical image quality varied from 0.88 to 0.92, depending on the selected physical-technical parameter. Depending on the strength of SAFIRE, the potential dose reduction based on noise, CNR and the inverse image quality figure (IQF_inv) varied from 14.0 to 67.8 %, 16.0 to 71.5 % and 22.7 to 50.6 % respectively. Potential dose reduction based on clinical image quality varied from 27 to 37.4 %, depending on the strength of SAFIRE.

Conclusion

Our results demonstrate that noise assessments in a uniform phantom overestimate the potential dose reduction for the SAFIRE IR algorithm. Since the IQF_inv based dose reduction is quite consistent with the clinical based dose reduction, an optimised contrast-detail phantom could improve the use of contrast-detail analysis for image quality assessment in chest CT imaging. In conclusion, one should be cautious to evaluate the performance of CT equipment taking into account only physical-technical parameters as noise and CNR, as this might give an incomplete representation of the actual clinical image quality performance.

Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

PURPOSE

Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE).

METHODS

Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms.

RESULTS

In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was reduced by an average of 60% in SAFIRE images compared to FBP. However, for edge pixels, noise magnitude ranged from 20% higher to 40% lower in SAFIRE images compared to FBP. SAFIRE images of the lung phantom exhibited distinct regions with varying noise texture (i.e., noise autocorrelation/power spectra).

CONCLUSIONS

Quantum noise properties observed in uniform phantoms may not be representative of those in actual patients for nonlinear reconstruction algorithms. Anatomical texture should be considered when evaluating the performance of CT systems that use such nonlinear algorithms.

Potential of combining iterative reconstruction with noise efficient detector design: aggressive dose reduction in head CT

OBJECTIVE

With further increase of CT numbers and their dominant contribution to medical exposure, there is a recent quest for more effective dose control. While reintroduction of iterative reconstruction (IR) has proved its potential in many applications, a novel focus is placed on more noise efficient detectors. Our purpose was to assess the potential of IR in combination with an integrated circuit detector (ICD) for aggressive dose reduction in head CT.

METHODS

Non-contrast low-dose head CT [190 mAs; weighted volume CT dose index (CTDIvol), 33.2 mGy] was performed in 50 consecutive patients, using a new noise efficient detector and IR. Images were assessed in terms of quantitative and qualitative image quality and compared with standard dose acquisitions (320 mAs; CTDIvol, 59.7 mGy) using a conventional detector and filtered back projection.

RESULTS

By combining ICD and IR in low-dose examinations, the signal to noise was improved by about 13% above the baseline level in the standard-dose control group. Both, contrast-to-noise ratio (2.02 ± 0.6 vs 1.88 ± 0.4; p = 0.18) and objective measurements of image sharpness (695 ± 84 vs 705 ± 151 change in Hounsfield units per pixel; p = 0.79) were fully preserved in the low-dose group. Likewise, there was no significant difference in the grading of several subjective image quality parameters when both noise-reducing strategies were used in low-dose examinations.

CONCLUSION

Combination of noise efficient detector with IR allows for meaningful dose reduction in head CT without compromise of standard image quality.

ADVANCES IN KNOWLEDGE

Our study demonstrates the feasibility of almost 50% dose reduction in head CT dose (1.1 mSv per scan) through combination of novel dose-reducing strategies.

Dose reduction with iterative reconstruction: Optimization of CT protocols in clinical practice

OBJECTIVES

To create an adaptable and global approach for optimizing MDCT protocols by evaluating the influence of acquisition parameters and Iterative Reconstruction (IR) on dose reduction and image quality.

MATERIALS AND METHODS

MDCT acquisitions were performed on quality image phantom by varying kVp, mAs, and pitch for the same collimation. The raw data were reconstructed by FBP and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with different reconstruction kernel and thickness. A total of 4032 combinations of parameters were obtained. Indices of quality image (image noise, NCT, CNR, SNR, NPS and MTF) were analyzed. We developed a software in order to facilitate the optimization between dose reduction and image quality. Its outcomes were verified on an adult anthropomorphic phantom.

RESULTS

Dose reduction resulted in the increase of image noise and the decrease of SNR and CNR. The use of IR improved these indices for the same dose without affecting NCT and MTF. The image validation was performed by the anthropomorphic phantom. The software proposed combinations of parameters to reduce doses while keeping indices of the image quality adequate. We observed a CTDIvol reduction between -44% and -83% as compared to the French diagnostic reference levels (DRL) for different anatomical localization.

CONCLUSION

The software developed in this study may help radiologists in selecting adequate combinations of parameters that allows to obtain an appropriate image with dose reduction.