Noise Power Spectrum Curves Research Articles

Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images

A bilateral filter is a non-linear denoising algorithm that can reduce noise while preserving the edges. This study explores the characteristics of a bilateral filter in changing the noise and texture within computed tomography (CT) images in an iterative implementation. We collected images of a homogeneous Neusoft phantom scanned with tube currents of 77, 154, and 231 mAs. The images for each tube current were filtered five times with a configuration of sigma space (σd) = 2 pixels, sigma intensity (σr) = noise level, and a kernel of 5 × 5 pixels. To observe the noise texture in each filter iteration, the noise power spectrum (NPS) was obtained for the five slices of each dataset and averaged to generate a stable curve. The modulation-transfer function (MTF) was also measured from the original and the filtered images. Tests on an anthropomorphic phantom image were carried out to observe their impact on clinical scenarios. Noise measurements and visual observations of edge sharpness were performed on this image. Our results showed that the bilateral filter was effective in suppressing noise at high frequencies, which is confirmed by the sloping NPS curve for different tube currents. The peak frequency was shifted from about 0.2 to about 0.1 mm−1 for all tube currents, and the noise magnitude was reduced by more than 50% compared to the original images. The spatial resolution does not change with the number of iterations of the filter, which is confirmed by the constant values of MTF50 and MTF10. The test results on the anthropomorphic phantom image show a similar pattern, with noise reduced by up to 60% and object edges remaining sharp.

Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.

Dual-energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise and artifacts due to material decomposition used in synthesizing VNCa images. In this work, we aim to improve VNCa image quality for the assessment of focal multiple myeloma, using an Artificial intelligence based Generalizable Algorithm for mulTi-Energy CT (AGATE) method. AGATE method used a custom dual-task convolutional neural network (CNN) that concurrently carries out material classification and quantification. The material classification task provided an auxiliary regularization to the material quantification task. CNN parameters were optimized using custom loss functions that involved cross-entropy, physics-informed constraints, structural redundancy in spectral and material images, and texture information in spectral images. For training data, CT phantoms (diameters 30 to 45cm)withtissue-mimicking inserts were scanned on a third generation dual-source CT system. Scans were performed at routine dose and half of the routine dose. Small image patches (i.e., 40 × 40 pixels) of tissue-mimicking inserts with known basis material densities were extracted for training samples. Numerically simulated insert materials with various shapes increased diversity of training samples. Generalizability of AGATE was evaluated using CT images from phantoms and patients. In phantoms, material decomposition accuracy was estimated using mean-absolute-percent-error (MAPE), using physical inserts that were not used during the training. Noise power spectrum (NPS) and modulation transfer function (MTF) were compared across phantom sizes and radiation dose levels. Five patients with multiple myeloma underwent dual-energy CT, with VNCa images generated using a commercial method and AGATE. Two fellowship-trained musculoskeletal radiologists reviewed the VNCa images (commercial and AGATE) side-by-side using a dual-monitor display, blinded to VNCa type, rating the image quality for focal multiple myeloma lesion visualization using a 5-level Likert comparison scale (-2=worse visualization and diagnostic confidence, -1=worse visualization but equivalent diagnostic confidence, 0=equivalent visualization and diagnostic confidence, 1=improved visualization but equivalent diagnostic confidence, 2=improved visualization and diagnostic confidence). A post hoc assignment of comparison ratings was performed to rank AGATE images in comparison to commercial ones. AGATE demonstrated consistent material quantification accuracy across phantom sizes and radiation dose levels, with MAPE ranging from 0.7% to 4.4% across all testing materials. Compared to commercial VNCa images, the AGATE-synthesized VNCa images yielded considerably lower image noise (50-77% noise reduction) without compromising noise texture or spatial resolution across different phantom sizes and two radiation doses. AGATE VNCa images had markedly reduced area under NPS curves and maintained NPS peak frequency (0.7 lp/cm to 1.0 lp/cm), with similar MTF curves (50% MTF at 3.0 lp/cm). In patients, AGATE demonstrated reduced image noise and artifacts with improved delineation of focal multiple myeloma lesions (all readers comparison scores indicating improved overall diagnostic image quality [scores 1 or 2]). AGATE demonstrated reduced noise and artifacts in VNCa images and ability to improve visualization of bone marrow lesions for assessing multiple myeloma.

A tin filter’s dose reduction effect revisited: Using the detectability index in low-dose computed tomography for the chest

PurposeTo reevaluate a tin filter's (TF) dose reduction effect in computed tomography (CT) using a combination of an anthropomorphic chest phantom and a rod-shaped phantom. Methods and materialsA third-generation dual-source CT system equipped with a built-in TF was employed. A chest phantom was scanned under low-dose conditions of 0.2 to 1.0 mGy with the TF at 100 kV (TF100kV) and without it at 100 kV and 120 kV (NF100kV and NF120kV). To eliminate effects other than that of the TF, only filtered back projection (FBP) was used for image reconstruction. On the images of the rod phantom placed inside the lung field, the CT number and the spatial resolution using the modulation transfer function (MTF) were measured. Using these indices plus the noise power spectrum (NPS) that was also measured, the detectability index based on the non-prewhitening model observer (d'NPW) was calculated. ResultsThe CT numbers and MTFs were almost identical across the three conditions. The area under the NPS curve was decreased by 13–17% with the TF compared with non-TF conditions. NPS increases at low frequencies of < 0.06 mm−1 observed in NF120kV and NF100kV were eliminated by TF100kV. The potential dose reduction by the TF, estimated using the d'NPW values, turned out to be 22 to 25%. ConclusionBased on the analysis of the FBP images of a chest phantom, the dose reduction attributable only to the TF was estimated at 22–25%, notably lower than those reported in previous studies.

A phantom study comparing low-dose CT physical image quality from five different CT scanners.

To systematically evaluate the physical image quality of low-dose computed tomography (LDCT) on CT scanners from 5 different manufacturers using a phantom model. CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA). The CT number linearity of 4 insert materials were as follows: acrylic (95% CI: 120.35 to 121.27; P=0.134), low-density polyethylene (95% CI: -98.43 to -97.43; P=0.070), air (95% CI: -996.16 to -994.51; P=0.018), and Teflon (95% CI: 984.40 to 986.87; P=0.883). The image uniformity values of GE Healthcare (95% CI: 3.24 to 3.83; P=0.138), Philips (95% CI: 2.62 to 3.70; P=0.299), Siemens (95% CI: 2.10 to 3.59; P=0.054), Minfound (95% CI: 2.35 to 3.65; P=0.589), and Neusoft (95% CI: 2.63 to 3.37; P=0.900) were evaluated and found to be within ±4 Hounsfield units (HU), with a range of 0.99-2.76 HU for standard deviations. There was no statistically significant difference in CT number linearity and image uniformity across the 5 CT scanners under different radiation doses with IR and DLIR algorithms (P>0.05). The resolution level at 10% MTF was 6.98 line-pairs-per-centimeter (lp/cm) on average, which was similar to the subjective evaluation results (mostly up to 7 lp/cm). DLIR at all 3 levels had the highest 50% MTF values among all reconstruction algorithms. For image low-contrast detectability, the minimum diameter of distinguishable contrast holes reached 4 mm at a 0.5% resolution. Increasing the radiation dose and IR strength reduced the image noise and NPS curve peak frequency while improving image low-contrast detectability. This study demonstrated that the image quality of CT scanners from 5 different manufacturers in LDCT is comparable and that the CT number linearity is unbiased and can contribute to accurate bone mineral density quantification.

Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study.

To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm. Acquisitions on the CT ACR 464 phantom were performed at five dose levels (CTDIvol : 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data were reconstructed using the filtered-back projection (FBP), the enhanced level of AIDR 3D (AIDR 3De), and the three levels of AiCE (Mild, Standard, and Strong) for the two versions (AiCE V8 vs AiCE V10). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone (high-contrast insert) and acrylic (low-contrast insert) inserts were computed. To quantify the changes of noise magnitude and texture, the square root of the area under the NPS curve and the average spatial frequency (fav ) of the NPS curve were measured. The detectability index (d') was computed to model the detectability of either a large mass in the liver or lung, or a small calcification or high contrast tissue boundaries. The noise magnitude was lower with both AiCE versions than with AIDR 3De. The noise magnitude was lower with AiCE V10 than with AiCE V8 (-4±6% for Mild, -13±3% for Standard, and -48±0% for Strong levels). fav and TTF50% values for both inserts shifted towards higher frequencies with AiCE than with AIDR 3De. Compared to AiCE V08, fav shifted towards higher frequencies with AiCE V10 (45 ±4%, 36±3%, and 5± 4% for all levels, respectively). The TTF50% values shifted towards higher frequencies with AiCE V10 as compared with AiCE V8 for both inserts, except for the Strong level for the acrylic insert. Whatever the dose and AiCE levels, d' values were higher with AiCE V10 than with AiCE V8 for the small object/calcification and for the large object/lesion. As compared to AIDR 3De, lower noise magnitude and higher spatial resolution and detectability index were found with both versions of AiCE. As compared to AiCE V8, AiCE V10 reduced noise and improved spatial resolution and detectability without changing the noise texture in a simple geometric phantom, except for the Strong level. AiCE V10 seems to have a greater potential for dose reduction than AiCE V8.

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

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. The homogeneous module of the ACR CT phantom was scanned on the 64 and 256slices CT scanners from the same vendor in the range of 15-40mA. 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 (CFRxy ), CFR in the z-direction (CFRz ), 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 CFRxy and CFRz 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. 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 2.0mGy, but there was no observable dose-dependence on the 64-slice. 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.

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study.

Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDIvol range: 54.7–57.5 mGy) employed by the staff of various Radiology/Neuroradiology departments of our institution for routine head examinations. CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. Noise values ranged from 3.5 HU to 5.7 HU across scanners/acquisition protocols. NPS curves differed in terms of peak position (range: 0.21–0.30 mm-1). A substantial variation of CNR values with scanner/acquisition protocol was observed for different contrast inserts. The coefficient of variation (standard deviation divided by mean value) of CNR values across scanners/acquisition protocols was 18.3%, 31.4%, 34.2%, 30.4% and 30% for teflon, delrin, LDPE, polystyrene and acrylic insert, respectively. An appreciable difference in MTF curves across scanners/acquisition protocols was revealed, with a coefficient of variation of f50%/f10% of MTF curves across scanners/acquisition protocols of 10.1%/7.4%. A relevant difference in LCD performance of different scanners/acquisition protocols was found. The range of contrast threshold for a typical object size of 3 mm was 3.7–5.8 HU. Moreover, appreciable differences in terms of NUI values (range: 4.1%-8.3%) were found. The analysis of several quality indices showed a non-negligible variability in head CT imaging capabilities across different scanners/acquisition protocols. This highlights the importance of a physical in-depth characterization of image quality for each CT scanner as clinically used, in order to optimize CT imaging procedures.

Deep learning-based reconstruction in ultra-high-resolution computed tomography: Can image noise caused by high definition detector and the miniaturization of matrix element size be improved?

This study aimed to assess the noise characteristics of ultra-high-resolution computed tomography (UHRCT) with deep learning-based reconstruction (DLR). Two different diameters of water phantom were scanned with three different resolution acquisition modes. Images were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (hybrid-IR), and DLR. Image noise analysis was performed with noise magnitude, peak frequency (fp) of the noise power spectrum (NPS), and the square root of the area under the curve (√AUCNPS) for the NPS curve. The noise magnitude was up to 3.30 times higher for the FBP acquired in SHR mode than that for the NR mode. The fp values of the FBP were 0.20-0.21, 0.34-0.36, and 0.34-0.37 cycles/mm for normal resolution (NR), high resolution (HR), and super high resolution (SHR) mode, respectively. The fp of hybrid-IR was 0.16-0.19, 0.21-0.26, and 0.23-0.26 cycles/mm for NR, HR, and SHR mode, respectively. The fp of DLR was 0.21-0.32 and 0.22-0.33 cycles/mm for HR and SHR mode, respectively. √AUCNPS showed that the highest value in FBP images of the SHR mode was up to 1.89 times that of the NR mode. DLR in the HR and SHR modes showed high noise reduction while suppressing fp shift with respect to FBP. The new DLR algorithm could be a solution to the noise increase due to the high-definition detector elements and the small reconstruction matrix element size.

ANALYSIS OF THE EFFECT OF CARE DOSE 4D SOFTWARE USE ON IMAGE QUALITY AND RADIATION DOSE ON THE CT SCAN ABDOMEN

The CT Scan is the most significant contributor to radiation dose on radiological examination, although the frequency of the examination is far below other modalities. In order to control this radiation dose, manufactures of CT Scan have equipped their units with built-in software called Automatic Exposure Control (AEC). This study aims to analyze the effect of AEC software, CARE Dose 4D, on image quality, and CTDIvol. Objects used in this study were three water phantoms, each with a diameter of 165 mm, 230 mm, and 305 mm. The image quality-analyzed was CT Number and noise. Measurement of image quality was carried out following Bapeten's provisions. Noise Power Spectrum (NPS) graphics were also used to further observes noise texture. The CT Number accuracy, CT Number, and noise uniformity obtained with and without CARE Dose 4D, on the three phantoms were still within Bapeten's threshold. This indicates that the use of CARE Dose 4D can still image a homogeneous object accurately. The results of the NPS curve showed that the two modes, in three phantoms, were having the same noise texture. The NPS curves also showed that the use of CARE Dose 4D produces higher noise than the non-CARE Dose 4D mode. Meanwhile, there were significant differences from the CTDIvol obtained from the two modes. The use of CARE Dose 4D software reduced dose of up to 54.34%. From this, the use of CARE Dose 4D software can reduce the radiation dose while maintaining image quality.

Technical Note: Impact on central frequency and noise magnitude ratios by advanced CT image reconstruction techniques.

We use central frequency ratio and noise magnitude ratio from noise power spectrum (NPS) to evaluate the noise reduction techniques of ASiR and ASiR-V of GE, SAFIRE and ADMIRE of Siemens, and PixelShine of AlgoMedica. ASiR, ASiR-V, SAFIRE and ADMIRE use a combination of image and projection data whereas PixelShine uses artificial intelligence neural network for noise reduction. The homogeneous module of the ACR computed tomography (CT) phantom was scanned on a GE Revolution HD 64-slice CT for ASiR and ASiR-V, a Siemens Somatom Force for ADMIRE, and a Siemens Definition AS 64-slice for SAFIRE for NPS calculation. The baseline filtered back-projection (FBP) reconstructions were derived from the standard kernel on Revolution HD, Hr44f on Force and D40s on Definition AS. The central frequency ratio (CFR) indicates the degree of shift in the central frequency of NPS after noise reduction. A smaller CFR means a larger shift of the NPS curve, or a larger degree of image blurring. The noise magnitude ratio (NMR) indicates the amount of noise removed. A smaller NMR means a larger degree of noise reduction. An ideal noise reduction shall maintain a CFR close to 1 and a NMR close to 0. The ideal noise reduction by increasing radiation exposure did not shift the central frequency when the image noise was reduced. PixelShine was the closest to the ideal noise reduction in CFR, and was followed by SAFIRE, ASiR-V, ADMIRE and ASiR, in sequence. Similarly, PixelShine had the smallest NMR, and was followed by SAFIRE, ASiR-V, ADMIRE and ASiR in sequence. Overall, PixelShine had the least central frequency shift for the same amount of noise reduction or the most noise reduction for the same amount of central frequency shift. For the same CFR, ASiR-V reduced more noise than ASiR; and SAFIRE reduced more noise than ADMIRE. We introduced two new parameters of CFR and NMR from NPS to compare the reconstructions from different manufacturers. PixelShine had the least central frequency shift for the same amount of noise reduction or the most noise reduction for the same amount of central frequency shift. For the same central frequency shift, ASiR-V reduced more noise than ASiR, and SAFIRE reduced more noise than ADMIRE.

Increasing the precision in the estimates of noise power spectrum in digital x-ray imaging systems

The noise power spectrum (NPS) of a digital x-ray imaging device is usually estimated from the average of periodograms of ROIs in images obtained with uniform radiation fields. The purpose of this work was developing a new estimator for calculating the NPS and comparing its uncertainties with those of the smoothed periodogram.The new estimator is built by removing those addends in the summation of the periodogram that do not contain information on stochastic noise. This was carried out by applying a short-length lag window to the autocorrelation function of noise. The length of the window was obtained from the support of this function. It has to be large enough not to eliminate information on noise autocorrelation and it has be as short as possible to minimize uncertainty. In this work, this length was set to three times the support of the autocorrelation function of noise.The new truncated sum (TS) estimator is shown to be unbiased and to have a much higher precision than that of the periodogram. The combined process of applying lag windows to the autocorrelation function of noise and removing addends with null expected values from the periodogram summation has a double effect on NPS curves. On the one hand, the curves are smoothed and, on the other hand, the uncertainties in the calculated values are highly reduced.

Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality

PurposeTo optimize image quality and radiation dose of chest CT with respect to various iterative reconstruction levels, detector collimations and body sizes. MethodA Kyoto Kagaku Lungman with and without extensions was scanned using fixed ultra-low doses of 0.25, 0.49 and 0.74 mGy CTDIvol, and collimations of 40 and 80 mm. Images were reconstructed with the lung kernel, filtered back projection (FBP) and different ASIR-V levels (10–100%). Contrast-to-noise ratios (CNR) were calculated for 12 mm simulated lesions of different densities in the lung. Image noise, signal-to-noise ratios (SNR), variations in Hounsfield units (HU), noise power spectrum (NPS) and noise texture deviations (NTD) were evaluated for all reconstructions. NTD was calculated as percentage of pixels outside 3 standard deviations to evaluate IR-specific artefacts. ResultsCompared to the FBP, image noise reduced (5–55%) with ASIR-V levels irrespective of dose or collimation. SNR correlated positively (r ≥ 0.925, p ≤ 0.001) with ASIR-V levels at all doses, collimations, and phantom sizes. ASIR-V enhanced the CNR of the lesion with the lowest contrast from 12.7–42.1 (0–100% ASIR-V) at 0.74 mGy with 40 mm collimation. As expected, higher SNR and CNR were measured in the smaller phantom than the bigger phantom. Uniform HU were observed between FBP and ASIR-V levels at all doses, collimations, and phantom sizes. NPS curves left-shifted towards lower frequencies at increasing levels of ASIR-V irrespective of collimation. A positive correlation (r ≥ 0.946, p ≥ 0.001) was observed between NTD and ASIR-V levels. NTD of the FBP was not significantly (p ≤ 0.087) different from NTD of ASIR-V ≤ 20%. The data from the NPS and NTD indicates a blotchier and coarser noise texture at higher levels of ASIR-V, especially at 100% ASIR-V. ConclusionIn comparison with the FBP technique, ASIR-V enhanced quantitative image quality parameters at all ultra-low doses tested. Moreover, the use of ASIR-V showed consistency with body size and collimation. Hence, ASIR-V may be useful for improving image quality of chest CT at ultra-low doses.

High-precision noise power spectrum measurements in digital radiography imaging.

Accurately and precisely estimating the noise power spectrum (NPS) is important for characterizing the performance of a radiography detector and helpful for improving the performance when developing radiography detectors. In order to produce an accurate estimate, the frequency resolution should be sufficiently high, and for a precise estimate, the sample size for the sample mean should also be large enough. However, there is a trade-off between the frequency resolution and the sample size if the available samples are limited. To improve the precision of the estimate, a radial averaging technique is employed in the IEC standard without sacrificing the frequency resolution or the estimate accuracy. In the radial averaging technique, directional NPS curves of a range are averaged from the two-dimensional NPS, and thus, directional error and poor precision problems occur, especially at low frequencies. This problem also leads to uncertainties in calculating the detective quantum efficiency (DQE). Therefore, the purpose of this study is to develop algorithms that can improve the precisions in estimating NPS to replace the radial averaging technique or to add additional precision. The horizontal or vertical NPS curve can be estimated using the sample mean of the summation of directional cross periodograms with various distances from the two-dimensional NPS. In practical x-ray imaging, the amplitude response of the cross periodograms decreases rapidly as the distance increases. Hence, a partial summation of the cross periodograms can provide an accurate estimate of the NPS. This partial summation can increase the sample size and thus improve the estimate precision for the entire frequency range without causing directional errors. This paper proposes two estimate algorithms under the notion of the partial use of cross periodograms. In order to evaluate the precisions from the proposed algorithms, a relative precision, which is defined as the standard deviation of the estimate divided by its average, was employed. The relative precisions were calculated using 100 x-ray images acquired from a general radiography detector. For the detector, we were able to achieve a better precision compared to using the radial averaging technique. For an image of 900×900 pixels and the region of interest size 256 in a direction with a half overlap, the conventional approach of the IEC standard yielded an average relative precision of 6.96% with the worst precision of 36.1% at the zero frequency. However, the proposed algorithms could yield an average relative precision of 4.14% with the zero-frequency precision of 5.79%. Without using the radial averaging technique, the proposed algorithms in this paper could improve the estimate precisions for the entire frequency range under the notion of a partial summation of the cross periodograms. Especially for low frequencies including the zero frequency,the proposed algorithms could achieve a high-precision to estimate the NPS.

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

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. The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120kV and 25 and 90mAs. 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. 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. 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.

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction.

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

Image quality characteristics for virtual monoenergetic images using dual-layer spectral detector CT: Comparison with conventional tube-voltage images

PurposeTo investigate the image quality characteristics for virtual monoenergetic images compared with conventional tube-voltage image with dual-layer spectral CT (DLCT). MethodsHelical scans were performed using a first-generation DLCT scanner, two different sizes of acrylic cylindrical phantoms, and a Catphan phantom. Three different iodine concentrations were inserted into the phantom center. The single-tube voltage for obtaining virtual monoenergetic images was set to 120 or 140 kVp. Conventional 120- and 140-kVp images and virtual monoenergetic images (40–200-keV images) were reconstructed from slice thicknesses of 1.0 mm. The CT number and image noise were measured for each iodine concentration and water on the 120-kVp images and virtual monoenergetic images. The noise power spectrum (NPS) was also calculated. ResultsThe iodine CT numbers for the iodinated enhancing materials were similar regardless of phantom size and acquisition method. Compared with the iodine CT numbers of the conventional 120-kVp images, those for the monoenergetic 40-, 50-, and 60-keV images increased by approximately 3.0-, 1.9-, and 1.3-fold, respectively. The image noise values for each virtual monoenergetic image were similar (for example, 24.6 HU at 40 keV and 23.3 HU at 200 keV obtained at 120 kVp and 30-cm phantom size). The NPS curves of the 70-keV and 120-kVp images for a 1.0-mm slice thickness over the entire frequency range were similar. ConclusionVirtual monoenergetic images represent stable image noise over the entire energy spectrum and improved the contrast-to-noise ratio than conventional tube voltage using the dual-layer spectral detector CT.

Objective image characterization of a spectral CT scanner with dual-layer detector

This work evaluated the performance of a detector-based spectral CT system by obtaining objective reference data, evaluating attenuation response of iodine and accuracy of iodine quantification, and comparing conventional CT and virtual monoenergetic images in three common phantoms. Scanning was performed using the hospital’s clinical adult body protocol. Modulation transfer function (MTF) was calculated for a tungsten wire and visual line pair targets were evaluated. Image noise power spectrum (NPS) and pixel standard deviation were calculated. MTF for monoenergetic images agreed with conventional images within 0.05 lp cm−1. NPS curves indicated that noise texture of 70 keV monoenergetic images is similar to conventional images. Standard deviation measurements showed monoenergetic images have lower noise except at 40 keV. Mean CT number and CNR agreed with conventional images at 75 keV. Measured iodine concentration agreed with true concentration within 6% for inserts at the center of the phantom. Performance of monoenergetic images at detector based spectral CT is the same as, or better than, that of conventional images. Spectral acquisition and reconstruction with a detector based platform represents the physical behaviour of iodine as expected and accurately quantifies the material concentration.

Objective performance assessment of five computed tomography iterative reconstruction algorithms.

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. 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. 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. 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.

CT dose reduction using Automatic Exposure Control and iterative reconstruction: A chest paediatric phantoms study

PurposeTo evaluate the impact of Automatic Exposure Control (AEC) on radiation dose and image quality in paediatric chest scans (MDCT), with or without iterative reconstruction (IR). MethodsThree anthropomorphic phantoms representing children aged one, five and 10-year-old were explored using AEC system (CARE Dose 4D) with five modulation strength options. For each phantom, six acquisitions were carried out: one with fixed mAs (without AEC) and five each with different modulation strength. Raw data were reconstructed with Filtered Back Projection (FBP) and with two distinct levels of IR using soft and strong kernels. Dose reduction and image quality indices (Noise, SNR, CNR) were measured in lung and soft tissues. Noise Power Spectrum (NPS) was evaluated with a Catphan 600 phantom. ResultsThe use of AEC produced a significant dose reduction (p<0.01) for all anthropomorphic sizes employed. According to the modulation strength applied, dose delivered was reduced from 43% to 91%. This pattern led to significantly increased noise (p<0.01) and reduced SNR and CNR (p<0.01). However, IR was able to improve these indices. The use of AEC/IR preserved image quality indices with a lower dose delivered. Doses were reduced from 39% to 58% for the one-year-old phantom, from 46% to 63% for the five-year-old phantom, and from 58% to 74% for the 10-year-old phantom. In addition, AEC/IR changed the patterns of NPS curves in amplitude and in spatial frequency. ConclusionsIn chest paediatric MDCT, the use of AEC with IR allows one to obtain a significant dose reduction while maintaining constant image quality indices.

TU‐EF‐204‐12: Quantitative Evaluation of Spectral Detector CT Using Virtual Monochromatic Images: Initial Results

PurposeTo evaluate the image quality and spectral information of a spectral detector CT (SDCT) scanner using virtual monochromatic (VM) energy images.MethodsThe SDCT scanner (Philips Healthcare) was equipped with a dual‐layer detector and spectral iterative reconstruction (IR), which generates conventional 80–140 kV polychromatic energy (PE) CT images using both detector layers, PE images from the low‐energy (upper) and high‐energy (lower) detector layers and VM images. A solid water phantom with iodine (2.0–20.0 mg I/ml) and calcium (50.0–600.0 mg Ca/ml) rod inserts was used to evaluate effective energy estimate (EEE) and iodine contrast to noise ratio (CNR). The EEE corresponding to an insert CT number in a PE image was calculated from a CT number fit to the VM image set. Since PE image is prone to beam‐hardening artifact EEE may underestimate the actual energy separation from two layers of the detector. A 30‐cm‐diameter water phantom was used to evaluate noise power spectrum (NPS). The phantoms were scanned at 120 and 140 kV with the same CTDIvol.ResultsThe CT number difference for contrast inserts in VM images (50–150 keV) was 1.3±6% between 120 and 140 kV scans. The difference of EEE calculated from low‐ and high‐energy detector images was 11.5 and 16.7 keV for 120 and 140 kV scans, respectively. The differences calculated from 140 and 100 kV conventional PE images were 12.8, and 20.1 keV from 140 and 80 kV conventional PE images. The iodine CNR increased monotonically with decreased keV. Compared to conventional PE images, the peak of NPS curves from VM images were shifted to lower frequency.ConclusionThe EEE results indicates that SDCT at 120 and 140 kV may have energy separation comparable to 100/140 kV and 80/140 kV dual‐kV imaging. The effects of IR on CNR and NPS require further investigation for SDCT.Author YY and AD are Philips Healthcare employees.