Noise Power Spectrum Research Articles

Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence.

The image quality of computed tomography (CT) can be adversely affected by a low radiation dose, and reconstruction algorithms of an appropriate level may be useful in reducing this impact. Eight sets of CT images of a phantom were reconstructed with filtered back projection (FBP); adaptive statistical iterative reconstruction-Veo (ASiR-V) at 30% (AV-30), 50% (AV-50), 80% (AV-80), and 100% (AV-100); and deep learning image reconstruction (DLIR) at low (DL-L), medium (DL-M), and high (DL-H) levels. The noise power spectrum (NPS) and task transfer function (TTF) were measured. Thirty consecutive patients underwent low-dose radiation contrast-enhanced abdominal CT scans that were reconstructed using FBP, AV-30, AV-50, AV-80, and AV-100, and three levels of DLIR. The standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle were evaluated. Two radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. In the phantom study, both a higher DLIR and ASiR-V strength and a higher radiation dose led less noise. The NPS peak and average spatial frequency of the DLIR algorithms were closer to those of FBP, as the tube current increased and declined as the level of ASiR-V and DLIR strengthened. The NPS average spatial frequency of DL-L were higher than those of AISR-V. In clinical studies, AV-30 demonstrated a higher SD and lower SNR and CNR compared to DL-M and DL-H (P<0.05). For qualitative assessment, DL-M produced the highest qualitative image quality scores, with the exception of overall image noise (P<0.05). The NPS peak, average spatial frequency, and SD were the highest and the SNR, CNR, and subjective scores were the lowest with FBP. Compared with FBP and ASiR-V, DLIR provided better image quality and noise texture both in the phantom and clinical studies, and DL-M maintained the best image quality and lesion diagnostic confidence in low-dose radiation abdominal CT.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved.

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20mGy AV-40 images, the DECT 10mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

Discrimination tasks in simulated low-dose CT noise.

This study reports the results of a set of discrimination experiments using simulated images that represent the appearance of subtle lesions in low-dose computed tomography (CT) of the lungs. Noise in these images has a characteristic ramp-spectrum before apodization by noise control filters. We consider three specific diagnostic features that determine whether a lesion is considered malignant or benign, two system-resolution levels, and four apodization levels for a total of 24 experimental conditions. The goal of the investigation is to better understand how well human observers perform subtle discrimination tasks like these, and the mechanisms of that performance. We use a forced-choice psychophysical paradigm to estimate observer efficiency and classification images. These measures quantify how effectively subjects can read the images, and how they use images to perform discrimination tasks across the different imaging conditions. The simulated CT images used as stimuli in the psychophysical experiments are generated from high-resolution objects passed through a modulation transfer function (MTF) before down-sampling to the image-pixel grid. Acquisition noise is then added with a ramp noise-power spectrum (NPS), with subsequent smoothing through apodization filters. The features considered are lesion size, indistinct lesion boundary, and a nonuniform lesion interior. System resolution is implemented by an MTF with resolution (10% max.) of 0.47 or 0.58 cyc/mm. Apodization is implemented by a Shepp-Logan filter (Sinc profile) with various cutoffs. Six medically naïve subjects participated in the psychophysical studies, entailing training and testing components for each condition. Training consisted of staircase procedures to find the 80% correct threshold for each subject, and testing involved 2000 psychophysical trials at the threshold value for each subject. Human-observer performance is compared to the Ideal Observer to generate estimates of task efficiency. The significance of imaging factors is assessed using ANOVA. Classification images are used to estimate the linear template weights used by subjects to perform these tasks. Classification-image spectra are used to analyze subject weights in the spatial-frequency domain. Overall, average observer efficiency is relatively low in these experiments (10%-40%) relative to detection and localization studies reported previously. We find significant effects for feature type and apodization level on observer efficiency. Somewhat surprisingly, system resolution is not a significant factor. Efficiency effects of the different features appear to be well explained by the profile of the linear templates in the classification images. Increasingly strong apodization is found to both increase the classification-image weights and to increase the mean-frequency of the classification-image spectra. A secondary analysis of "Unapodized" classification images shows that this is largely due to observers undoing (inverting) the effects of apodization filters. These studies demonstrate that human observers can be relatively inefficient at feature-discrimination tasks in ramp-spectrum noise. Observers appear to be adapting to frequency suppression implemented in apodization filters, but there are residual effects that are not explained by spatial weighting patterns. The studies also suggest that the mechanisms for improving performance through the application of noise-control filters may require further investigation.

A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment.

The performance of a CT scanner for detectability tasks is difficult to precisely measure. Metrics such as contrast-to-noise ratio, modulation transfer function, and noise power spectrum do not predict detectability in the context of nonlinear reconstruction. We propose to measure detectability using a dense search challenge: a phantom is embedded with hundreds of target objects at random locations, and a human or numerical observer analyzes the reconstruction and reports on suspected locations of all target objects. The reported locations are compared to ground truth to produce a figure of merit, such as area under the curve (AUC), that is sensitive to the acquisition dose and the dose efficiency of the CT scanner. We used simulations to design such a dense search challenge phantom and found that detectability could be measured with precision better than 5%. Test 3D prints using the PixelPrint technique showed the feasibility of this technique.

Harmonizing CT Images via Physics-based Deep Neural Networks.

The rendition of medical images influences the accuracy and precision of quantifications. Image variations or biases make measuring imaging biomarkers challenging. The objective of this paper is to reduce the variability of computed tomography (CT) quantifications for radiomics and biomarkers using physics-based deep neural networks (DNNs). With the proposed framework, it is possible to harmonize the different renditions of a single CT scan (with variations in reconstruction kernel and dose) into an image that is in close agreement with the ground truth. To this end, a generative adversarial network (GAN) model was developed where the generator is informed by the scanner's modulation transfer function (MTF). To train the network, a virtual imaging trial (VIT) platform was used to acquire CT images, from a set of forty computational models (XCAT) serving as the patient model. Phantoms with varying levels of pulmonary disease, such as lung nodules and emphysema, were used. We scanned the patient models with a validated CT simulator (DukeSim) modeling a commercial CT scanner at 20 and 100 mAs dose levels and then reconstructed the images by twelve kernels representing smooth to sharp kernels. An evaluation of the harmonized virtual images was conducted in four different ways: 1) visual quality of the images, 2) bias and variation in density-based biomarkers, 3) bias and variation in morphological-based biomarkers, and 4) Noise Power Spectrum (NPS) and lung histogram. The trained model harmonized the test set images with a structural similarity index of 0.95±0.1, a normalized mean squared error of 10.2±1.5%, and a peak signal-to-noise ratio of 31.8±1.5 dB. Moreover, emphysema-based imaging biomarkers of LAA-950 (-1.5±1.8), Perc15 (13.65±9.3), and Lung mass (0.1±0.3) had more precise quantifications.

Adaptive-weighted high order TV algorithm for sparse-view CT reconstruction.

With the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data. However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach. In this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the Structural similarity (SSIM), Root Mean Square Error (RMSE), Contrast to noise ratio (CNR), and modulation transform function (MTF) curves are utilized to evaluate the quality of reconstructed images in statism, structure, spatial resolution, and contrast, respectively. In the noisy data experiment, we further use the noise power spectrum (NPS) curve to evaluate the reconstructed images and compare it with other three algorithms. We use the 2D slice in the XCAT phantom, 2D slice in TCIA Challenge data and FORBILD phantom as simulation phantoms and use real bird data for real verification. The results show that, compared with the traditional TV and FBP algorithms, the awHOTV has better performance in terms of RMSE, SSIM, and Pearson correlation coefficient (PCC) under the projected data with different sparsity. In addition, the awHOTV algorithm is robust against the noise of different intensities. The proposed awHOTV method can reconstruct the images with high accuracy under sparse or noisy data. The awHOTV solves the strip artifacts caused by sparse data in the FBP method. Compared with the TV method, the awHOTV can effectively suppress block artifacts and has good detail protectionability.

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.

Image-quality characteristics in the longitudinal direction from different image-reconstruction algorithms during single-rotation volume acquisition on head computed tomography: A phantom study

A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms. Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200mA (standard dose) and 50mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction (Z-axis). The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center. Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis.

Study on the filters of atmospheric contamination in ground based CMB observation

The atmosphere is one of the most significant sources of contamination in ground-based Cosmic Microwave Background (CMB) observations. Atmospheric emission increases the additional optical loading on the detector, resulting in higher photon noise. Additionally, atmospheric fluctuations cause spatial and temporal variations in detected power, leading to additional correlations between detectors and in the time stream of individual detectors. This correlated signal, known as the 1/f noise, can interfere with the detection of CMB signals, severely hindering the probing of CMB signals. In this paper, we study three types of filters: the polynomial fitting, high-pass filter, and Wiener filter. We evaluate the filters based on their ability to remove atmospheric noise, and investigate the impact of the filters on the data analytic process through end-to-end simulations of CMB experiments. We track their performance by analyzing the response of different components of the data, including both signal and noise. In the time domain, the high-pass filter is found to have the smallest root mean square error and achieves high filtering efficiency, followed by the Wiener filter and polynomial fitting. We adopt two map making methods, namely naive map making and Minimum Variance map making, to study the effects of filters on the map level. The results show that the polynomial fitting gives a high noise residual at low frequency, resulting in significant leakage to small scales in the map domain, while the high-pass and Wiener filters do not have significant leakage. We compare the filters' effects on the power spectra domain by estimating the angular power spectra of residual noise and input signal, and estimating the standard deviation of the signal recovered power spectra. At low noise level, the three filters give almost comparable standard deviations on medium and small scales. However, at high noise level, the standard deviation of the polynomial fitting is significantly larger. These studies can be used for reducing atmospheric noise in future ground-based CMB data processing.

Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study

PurposeTo assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT). Materials and methodsAcquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d′) was calculated for a lesion with an iodine concentration of 10 mg/mL. ResultsThe noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d’ values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by −39.82 ± 9.32%, similar at 31 cm diameter −4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images. ConclusionsThe ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

Characterization of a CdTe single-photon-counting detector for biomedical imaging applications

PurposeThe Eiger 2X CdTe 1 M−W (Dectris ltd, Baden, Switzerland) single photon counting detector was characterized for imaging applications at the biomedical beamline ID17 of the European Synchrotron Radiation Facility. MethodsLinearity, Modulation Transfer Function, Noise Power Spectrum and Detective Quantum Efficiency were measured as a function of photon energy and flux in the range 26–80 keV. ResultsThe linearity was confirmed in the flux range specified by Dectris and a detection efficiency higher than 60 % was measured for energies up to 80 keV. The spatial resolution was inferred from the Modulation Transfer Function and was found to be compatible with the pixel size of the detector (75 μm), except at energies just above the K-edge of Cd and Te where it reached 150 μm. The study of the Noise Power Spectrum showed a time-dependency in the response of the sensor, which is mitigated at low photon fluxes (<2⨯108 ph mm−2 s−1). ConclusionsThis work was the first characterization of the Eiger 2X CdTe 1 M−W for imaging applications with monochromatic synchrotron radiation. The spatial resolution and the quantum efficiency are compatible with low-dose imaging applications.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT. Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations. Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images. Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.

Virtual monochromatic images for coronary artery imaging with a spectral photon-counting CT in comparison to dual-layer CT systems: a phantom and a preliminary human study

ObjectivesTo evaluate the quality of virtual monochromatic images (VMIs) from spectral photon-counting CT (SPCCT) and two energy-integrating detector dual-energy CT (EID-DECT) scanners from the same manufacturer, for the coronary lumen.MethodsA 21-cm section of the Mercury v4.0 phantom was scanned using a cardiac CT protocol. VMIs from 40 to 90 keV were reconstructed using high-resolution (HR) parameters for EID-DECT and SPCCT (CB and HRB kernels at 0.67 mm slice thickness, respectively). Ultra-high-resolution (UHR) parameters were used in addition to SPCCT (detailed-2 kernel, 0.43 mm slice thickness). Noise-power-spectrum (NPS), task-based transfer function (TTF), and detectability index (d′) were computed for 2-mm-diameter lumen detection. In consensus, two radiologists analyzed the quality of the images from 8 patients who underwent coronary CTA on both CT systems.ResultsFor all keV images, fpeak, f50, and d′ were higher with SPCCT. The fpeak and f50 were higher with UHR-SPCCT with greater noise and lower d′ compared to those of the HR-SPCCT images. Noise magnitude was constant for all energy levels (keV) with both systems, and lower with HR images, and d′ decreased as keV decreased. Subjective analysis showed greater lumen sharpness and overall quality for HR and UHR-SPCCT images using all keV, with a greater difference at low keV compared to HR-EID-DECT images.ConclusionHR and UHR-SPCCT images gave greater detectability of the coronary lumen for 40 to 90 keV VMIs compared to two EID-DECT systems, with benefits of higher lumen sharpness and overall quality.Key Points• Compared with 2 dual-energy CT systems, spectral photon-counting CT (SPCCT) improved spatial resolution, noise texture, noise magnitude, and detectability of the coronary lumen.• Use of ultra-high-resolution parameters with SPCCT improved spatial resolution and noise texture and provided high detectability of the coronary lumen, despite an increase in noise magnitude.• In eight patients, radiologists found greater overall image quality with SPCCT for all virtual monochromatic images with a greater difference at low keV, compared with dual-energy CT systems.

Clinical commissioning of the first point-of-care spectral photon-counting CT for the upper extremities.

Acceptance testing and quality assurance (QA) of computed tomography (CT) scans are of great importance to ensure the appropriate performance of the systems. However, current standards and guidelines do not include a dedicated QA program for spectral photon-counting CT (SPCCT), nor adapted tolerancelevels. To evaluate the technical performance, in terms of image quality and radiation dose, of the first point-of-care SPCCT for the upper extremities (MARS Extremity 5X120, MARS Bioimaging Ltd., Christchurch, New Zealand) and to establish a comprehensive QAprogram. The specific dimensions of the scanner with a 125mm diameter gantry and a small voxel size of 0.1×0.1×0.1mm3 require the use of suitable phantoms and evaluation techniques. Indicators such as CT number accuracy, image noise, uniformity, and slice thickness were assessed to characterize the image quality. The in-plane and longitudinal spatial resolutions were evaluated by means of the modulation transfer function (MTF). Noise power spectra (NPS) were calculated to further evaluate the image noise. Material identification capabilities were assessed using clinically relevant high-Z materials (iodine, gold, gadolinium, and calcium). A 100-mm diameter CTDI-like phantom was used to measure the dose indices. A complete radiation survey was carried out to measure the radiation exposure at different points around thescanner. The proposed QA program is based on international and local recommendations as well as practical experience. It includes standardised CT tests and SPCCT-specific methods. Additional methodologies to further assess the system performance are also presented. Tolerance levels are discussed and revised when appropriate. Both in-plane and longitudinal high spatial resolutions were evidenced by the MTF measurements with 1.8lp· mm-1 and 5.0lp· mm-1 at 10%, respectively. The calculated effective slice thickness ranged between 0.15and 0.16mm for the five energy bins and for a reconstructed voxel size of 0.1×0.1×0.1mm3 . Reference values of the linear attenuation coefficient of water have been calculated and used to assess the CT number uniformity of water. Evaluation of the CT number accuracy and stability of various clinically relevant materials showed excellent spectral correlation and linearity between HU values and concentrations (r2 > 0.99). The NPS showed less noise correlation between slices than within transverse slice, as well as a systematic increase at low spatial frequencies. The volume CT dose index (CTDI ) for a custom-made 100mm diameter phantom was 9.32mGy. Radiation measurements around the scanner showed that it is completely shielded except for the access port, and that no additional protective measures are necessary for thepatient. A routine QA framework for SPCCT systems has been proposed. Image quality and radiation dose were assessed using newly designed phantoms, relevant metrics, and automated algorithms. Baseline values were established and tolerance levels discussed for the MARS SPCCT scanner based on collected data and internationalrecommendations.

Virtual monochromatic images of dual-energy CT as an alternative to single-energy CT: performance comparison using a detectability index for different acquisition techniques.

To investigate the performance of virtual monochromatic (VM) images with the same dose and iodine contrast as those for single-energy (SE) images using five dual-energy (DE) scanners with DE techniques: two generations of fast kV switching (FKS), two generations of dual source (DS), and one split filter (SF). A water-bath phantom with a diameter of 300mm, which contains one rod-shaped phantom made of a material equivalent to soft-tissue and two rod-shaped phantoms made of diluted iodine (2 and 12mg/mL), was scanned using both SE (120, 100, and 80kV) and DE techniques with the same CT dose index in each scanner. The VM energy at which the CT number of the iodine rod is closest to that of each SE tube voltage was determined as the equivalent energy (Eeq). A detectability index (d') was calculated from the noise power spectrum, the task transfer functions, and a task function corresponding to each rod. The percentage of the d' value of the VM image to that of the corresponding SE image was calculated for performance comparison. The average percentages of d' of FKS1, FKS2, DS1, DS2, and SF were 84.6%, 96.2%, 94.3%, 107%, and 104% for 120kV-Eeq; 75.9%, 91.2%, 88.2%, 99.2%, and 82.6% for 100kV-Eeq; 71.6%, 88.9%, 82.6%, 85.2%, and 62.3% for 80kV-Eeq, respectively. The performance of VM images was on the whole inferior to that of SE images especially at low equivalent energy levels, depending on the DE techniques and their generations. • This study evaluated the performance of VM images with the same dose and iodine contrast as those for SE images using five DE scanners. • The performance of VM images varied with the DE techniques and their generations and was mostly inferior at low equivalent energy levels. • The results highlight the importance of distribution of available dose over the two energy levels and spectral separation for the performance improvement of VM images.

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

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.

Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study

PurposeTo compare quantitatively and qualitatively brain image quality acquired in helical and axial modes on two wide collimation CT systems according to the dose level and algorithm used. MethodsAcquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDIvol: 45/35/25 mGy) on two wide collimation CT systems (GE Healthcare and Canon Medical Systems) in axial and helical modes. Raw data were reconstructed using iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms. The noise power spectrum (NPS) was computed on both phantoms and the task-based transfer function (TTF) on the image quality phantom. The subjective quality of images from an anthropomorphic brain phantom was evaluated by two radiologists including overall image quality. ResultsFor the GE system, noise magnitude and noise texture (average NPS spatial frequency) were lower with DLR than with IR. For the Canon system, noise magnitude values were lower with DLR than with IR for similar noise texture but the opposite was true for spatial resolution. For both CT systems, noise magnitude was lower with the axial mode than with the helical mode for similar noise texture and spatial resolution. Radiologists rated the overall quality of all brain images as “satisfactory for clinical use”, whatever the dose level, algorithm or acquisition mode. ConclusionsUsing 16-cm axial acquisition reduces image noise without changing the spatial resolution and image texture compared to helical acquisitions. Axial acquisition can be used in clinical routine for brain CT examinations with an explored length of less than 16 cm.

Non-Markovian effects in stochastic resonance in a two-level system

Stochastic resonance is a phenomenon where the response signal to external driving is enhanced by environmental noise. In quantum regime, the effect of the environment is often intrinsically non-Markovian. Due to the combination of such non-Markovian quantum noise and external driving force, it is difficult to evaluate the correlation function and hence the power spectrum. Nevertheless, a recently developed time-evolving matrix product operators (TEMPO) method and its extensions provide an efficient and numerically exact approach for this task. Using TEMPO, we investigate non-Markovian effects in quantum stochastic resonance in a two-level system. The periodic signal and the time-averaged asymptotic correlation function, along with the power spectrum, are calculated. From the power spectrum, the signal-to-noise ratio is evaluated. It is shown that both signal strength and signal-to-noise ratio are enhanced by non-Markovian effects, which indicates the importance of non-Markovian effects in quantum stochastic resonance. In addition, we show that the non-Markovian effects can shift the peak position of the background noise power spectrum.