2D Noise Power Spectrum Research Articles

Computed tomography system performance for different iterative reconstruction algorithms

The number of the Computed Tomography (CT) examinations has increased greatly over the past decade; This has made CT doses the largest source of the individual effective dose from medical exposures worldwide. Evaluation of image quality in CT is an important issue to ensure diagnostic accuracy, while keeping the radiation dose especially to the pediatric patient as low as reasonably possible. The purpose of this study was to assess the physical and psychophysical image quality in pediatric CT for different iterative reconstruction (IR) algorithms. The image quality was measured on each CT system using pediatric QRM abdomen and thorax phantoms for clinical thorax and abdomen examination scanning parameters and using the Catphan 600 phantom for head examination scanning parameters. The performance of each CT systems was evaluated individually using conventional filtered back-projection (FBP) and IR algorithm employed in that system. Noise, contrast-to-noise ratio (CNR), inverse image quality figure (IQFinv), modulation transfer function (MTF) and noise power spectrum (NPS) were assessed for FBP and wide range of iteration level of different IR algorithms. Figure of Merit (FOM) parameter was calculated to characterize the dose efficiency of each CT system. The CTDIvol values were changed from 20.6 to 45.7, 0.9 to 7.4 and 2.4–9.6 mGy for pediatric head, thorax and abdomen scanning protocols between CT systems, respectively. With increasing iteration level, image noise decreased while CNR and IQFinv increased. NPS peak was decreased with increasing iteration level for both two (2D) and three dimensional (3D) NPS and the ratio of the 3D NPS to 2D NPS varied from 4.5 to 8.3 for pediatric head, from 4.6 to 8.7 for pediatric thorax and from 4.0 to 14.0 for pediatric abdomen scanning protocols. For all IR algorithms employed with CT systems used in this study, the FOM values were increased with increasing iteration level.

Objective image quality assurance in cone-beam CT: Test methods, analysis, and workflow in longitudinal studies.

Standards for image quality evaluation in multi-detector CT (MDCT) and cone-beam CT (CBCT) are evolving to keep pace with technological advances. A clear need is emerging for methods that facilitate rigorous quality assurance (QA) with up-to-date metrology and streamlined workflow suitable to a range of MDCT and CBCT systems. To evaluate the feasibility and workflow associated with image quality (IQ) assessment in longitudinal studies for MDCT and CBCT with a single test phantom and semiautomated analysis of objective, quantitative IQ metrology. A test phantom (CorgiTM Phantom, The Phantom Lab, Greenwich, New York, USA) was used in monthly IQ testing over the course of 1 year for three MDCT scanners (one of which presented helical and volumetric scan modes) and four CBCT scanners. Semiautomated software analyzed image uniformity, linearity, contrast, noise, contrast-to-noise ratio (CNR), 3D noise-power spectrum (NPS), modulation transfer function (MTF) in axial and oblique directions, and cone-beam artifact magnitude. The workflow was evaluated using methods adapted from systems/industrial engineering, including value stream process modeling (VSPM), standard work layout (SWL), and standard work control charts (SWCT) to quantify and optimize test methodology in routine practice. The completeness and consistency of DICOM data from each system was also evaluated. Quantitative IQ metrology provided valuable insight in longitudinal quality assurance (QA), with metrics such as NPS and MTF providing insight on root cause for various forms of system failure-for example, detector calibration and geometric calibration. Monthly constancy testing showed variations in IQ test metrics owing to system performance as well as phantom setup and provided initial estimates of upper and lower control limits appropriate to QA action levels. Rigorous evaluation of QA workflow identified methods to reduce total cycle time to ∼10min for each system-viz., use of a single phantom configuration appropriate to all scanners and Head or Body scan protocols. Numerous gaps in the completeness and consistency of DICOM data were observed for CBCT systems. An IQ phantom and test methodology was found to be suitable to QA of MDCT and CBCT systems with streamlined workflow appropriate to busy clinical settings.

Method to estimate fan-beam CT noise power spectrum using two basis functions with a limited number of noise realizations.

The noise power spectrum (NPS) plays a key role in image quality (IQ) evaluation as it can be used for predicting detection performance or calculating detective quantum efficiency (DQE). Traditionally, the NPS is estimated by ensemble averaging multiple realizations of noise-only images. However, the estimation error increases when there are a limited number of images. Since the estimation error directly affects the image quality (IQ) index, an accurate NPS estimation method is required. Recent works have proposed NPS estimation methods using the radial one-dimensional (1D) NPS as the basis; however, when sharp kernels are used during image reconstruction, these methods cannot accurately estimate the amplitude of each angular spoke of the 2D NPS composed of different cutoff frequencies determined from the complementary projection magnification factors for different spatial regions. In this work, we propose a 2D NPS estimation method that reflects the accurate amplitude of each angular spoke for fan-beam CTimages. An angular spoke of the 2D NPS is composed of two basis functions with different cutoff frequencies determined from the complementary projection magnification factors. The proposed method estimates these two weighting factors for each basis function by minimizing the mean-squared error (MSE) between the 2D NPS estimated from 10 noise realizations. Two noise profiles and two types of apodization filters (i.e., rectangular and Hanning) were used to reconstruct the noise-only images. To examine the nonstationary noise property of fan-beam CT images, the 2D NPS was estimated at three different local regions. The estimation accuracy of the proposed method was further improved by estimating the approximate weighting factors with sinusoidal functions, considering that the weighting factors vary slowly throughout the view angles. Regression orders of 1 to 4 were used during these estimations. The approximate weighting factors were then multiplied with each of the basis functions to estimate the 2D NPS. The normalized mean-squared error (NMSE) was used as an index to compare the performance of each NPS estimation method, with the analytical 2D NPS as the reference. Further validation was performed using XCAT phantomdata. We observed that the 2D NPS estimated using two basis functions reflected the accurate amplitude of each angular spoke, whereas the 2D NPS estimated using the radial 1D NPS as the basis could not. The 2D NPS estimated by applying the approximate weighting factors showed improved performance compared with that estimated using two basis functions. In addition, unlike the view-independent noise cases, where a lower regression order showed higher estimation performance, a higher regression order showed higher estimation performance in the view-dependent noisecases. In this work, we propose a 2D NPS estimation method that reflects the accurate amplitude of each angular spoke for fan-beam CT images using two basis functions. We observed that the proposed 2D NPS estimation method using two basis functions achieved better estimation performance compared with the method using the radial 1D NPS as the basis.

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.

Noise power spectrum in compressed sensing magnetic resonance imaging.

Compressed sensing magnetic resonance imaging (CS-MRI) uses random undersampling and nonlinear iterative reconstruction. This study was conducted to clarify the noise power spectrum (NPS) characteristics of CS-MRI. We measured two-dimensional (2D) NPS of CS-MRI with various acceleration factors (AF) and denoising factors (DF) and compared their appearance to those of conventional parallel MR images. Results showed that the 2D NPS of CS-MRI exhibited the following characteristics: (1) local decrease in the low-frequency region, (2) gradual decrease in the high-frequency region, and (3) a stripe pattern aligned at unequal intervals in the phase-encoding direction. Specifically, the 2D NPS of CS-MRI reflects the random undersampling pattern of k-space data. Additionally, 2D NPS allowed visualization of AF-dependent noise characteristics of CS-MRI. Furthermore, 1D NPS graph shapes clarified the CS-MRI noise characteristic dependence on AF and DF.

Central Slice Theorem-based Relationship between 1D-NPS Obtained by the Slit Method and 2D-NPS for CT Images

The method using a numerical slit (slit method) is used commonly to obtain the one-dimensional (1D) noise power spectrum (NPS) in computed tomography. However, the relationship between the 1D-NPS obtained by the slit method and the original two-dimensional (2D) NPS derived by the 2D Fourier transformation has not been elucidated clearly. The purpose of this study was to clarify their relationship based on the well-known central slice theorem (projection slice theorem) and validate it using computer simulation analysis. With the application of the central slice theorem, we described that the 1D-NPS obtained by the slit method was equal to the central slice (profile) in the 2D-NPS when we set the slit length to the maximum (i.e. the matrix size of the noise image). To verify this, we generated computer-simulated noise images with the known 2D-NPS (true 2D-NPS). From those images, we obtained the 1D-NPS that was obtained by the slit method and compared it with the central slice in the true 2D-NPS. When we set the slit length to the maximum, the 1D-NPS obtained by the slit method showed good agreement with the central slice in the true 2D-NPS. We clarified the relationship between the 1D-NPS obtained by the slit method and the 2D-NPS using a theoretical approach and the computer simulation. We had to maximize the slit length to achieve the accurate measurement of the 1D-NPS using the slit method.

A data-efficient method for local noise power spectrum (NPS) estimation in FDK-reconstructed 3D cone-beam CT.

For computed tomography (CT) systems in which noise is nonstationary, a local noise power spectrum (NPS) is often needed to characterize its noise property. We have previously developed a data-efficient radial NPS method to estimate the two-dimensional (2D) local NPS for filtered back projection (FBP)-reconstructed fan-beam CT utilizing the polar separability of CT NPS. In this work, we extend this method to estimate three-dimensional (3D) local NPS for feldkamp-davis-kress (FDK)-reconstructed cone-beam CT (CBCT) volumes. Starting from the 2D polar separability, we analyze the CBCT geometry and FDK image reconstruction process to derive the 3D expression of the polar separability for CBCT local NPS. With the polar separability, the 3D local NPS of CBCT can be decomposed into a 2D radial NPS shape function and a one-dimensional (1D) angular amplitude function with certain geometrical transforms. The 2D radial NPS shape function is a global function characterizing the noise correlation structure, while the 1D angular amplitude function is a local function reflecting the varying local noise amplitudes. The 3D radial local NPS method is constructed from the polar separability. We evaluate the accuracy of the 3D radial local NPS method using simulated and real CBCT data by comparing the radial local NPS estimates to a reference local NPS in terms of normalized mean squared error (NMSE) and a task-based performance metric (lesion detectability). In both simulated and physical CBCT examples, a very small NMSE (<5%) was achieved by the radial local NPS method from as few as two scans, while for the traditional local NPS method, about 20 scans were needed to reach this accuracy. The results also showed that the detectability-based system performances computed using the local NPS estimated with the NPS method developed in this work from two scans closely reflected the actual system performance. The polar separability greatly reduces the data dimensionality of the 3D CBCT local NPS. The radial local NPS method developed based on this property is shown to be capable of estimating the 3D local NPS from only two CBCT scans with acceptable accuracy. The minimum data requirement indicates the potential utility of local NPS in CBCT applications even for clinical situations.

OA139] Evaluation of two model based iterative reconstruction algorithms in computed tomography

Purpose To investigate the mode of action of two commercial model based (MB) CT reconstruction algorithms by studying several standard Fourier and alternative metrics. Methods Three CT systems featuring two statistical and two MB reconstruction algorithms were studied: Brilliance Elite iCT 256 (iDose4/IMR; Philips, The Netherlands), Somatom AS64 (Safire) and Somatom Drive (Admire; Siemens, Erlangen). Three phantoms were used for acquisitions (Catphan, The Phantom Laboratory, USA; body-shaped commercial density phantom; cylindrical water-filled phantom) varying dose (CTDIvol) and kV. Images were reconstructed at 5 different thicknesses with Filtered Back Projection (FBP), statistical and MB iterative reconstruction algorithms of different type (soft, medium, sharp) and strength. Spatial resolution was evaluated with Modulation Transfer Function (MTF) and Target Transfer Function (TTF), noise with Standard Deviation. Noise behaviour was analysed with 3D and 2D Noise Power Spectrum. Furthermore, Blur and Low Contrast Detectability (LCD) were investigated. Image quality of MB reconstruction algorithms has been compared to the clinically corresponding FBP and statistical reconstruction algorithms. Results Both MB reconstruction algorithms exhibited a slight improvement for MTF, being the difference larger for IMR. For Admire the difference is kernel independent and increases slightly with iterative strength. TTF curves and Blur confirm this trend for high density objects end present a sharpness reduction for low density details. For Admire noise reduction is dose neutral, increases only slightly with kernel sharpness (difference Conclusions MB reconstruction algorithms produce an improvement in image quality and allow dose reduction. To optimize image quality each algorithm requires vendor specific reconstruction parameters. Although MB reconstruction algorithms are non-linear, LCD evaluation demonstrated partially meaningful. Further investigation regarding model observer applications and other metrics as the detectability index d’ should be carried out.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part I: Assessment of spatial resolution and noise performance).

Low-signal correction (LSC) in the raw counts domain has been shown to effectively reduce noise streaks in CT because the data inconsistency associated with photon-starved regions may be mitigated prior to the log transformation step. However, a systematic study of the performance of these raw data correction methods is still missing in literature. The purpose of this work was to provide such a systematic study for two well-known low-signal correction schemes using either the adaptive trimmed mean (ATM) filter or the anisotropic diffusion (AD) filter in the raw counts domain. Image data were acquired experimentally using an anthropomorphic chest phantom and a benchtop cone-beam CT (CBCT) imaging system. Phantom scans were repeated 50 times at a reduced dose level of 0.5 mGy and a reference level of 1.9 mGy. The measured raw counts at 0.5 mGy underwent LSC using the ATM and AD filters. Two relevant parameters were identified for each filter and approximately one hundred operating points in each parameter space were analyzed. Following LSC and log transformation, FDK reconstruction was performed for each case. Noise and spatial resolution properties were assessed across the parameter spaces that define each LSC filter; the results were summarized through 2D contour maps to better understand the trade-offs between these competing image quality features. 2D noise power spectrum (NPS) and modulation transfer function (MTF) were measured locally at two spatial locations in the field-of-view (FOV): a posterior region contaminated by noise streaks and an anterior region away from noise streaks. An isotropy score metric was introduced to characterize the directional dependence of the NPS and MTF (viz., ϵNPS and ϵMTF , respectively), with a range from 0 for highly anisotropic to 1 for perfectly isotropic. The noise magnitude and coarseness were also measured. (a) Both the ATM and AD LSC methods were successful in reducing noise streaks, but their noise and spatial resolution properties were found to be highly anisotropic and shift-variant. (b) NPS isotropy scores in the posterior region were generally improved from ϵNPS = 0.09 for the images without LSC to the range ϵNPS = (0.11, 0.67) for ATM and ϵNPS = (0.06, 0.67) for AD, depending on the filter parameters used. (c) The noise magnitude was reduced across the parameter space of either LSC filter whenever a change along the axis of the controlling parameter led to stronger raw data filtration. Changes in noise magnitude were inversely related to changes in spatial resolution along the direction perpendicular to the streaks. No correlation was found, however, between the contour maps of noise magnitude and the NPS isotropy. (d) Both filters influenced the noise coarseness anisotropically, with coarser noise occurring along directions perpendicular to the noise streaks. The anisotropic noise coarseness was intrinsically and directly related to resolution losses in a given direction: coarseness plots mimic the topography of the 2D MTF, i.e., the coarser the noise, the lower the resolution. Both AD and ATM LSC schemes enable low-dose CBCT imaging. However, it was found that noise magnitude and overall spatial resolution vary considerably across the parameter space for each filter, and more importantly these image quality features are highly anisotropic and shift-variant.

Evaluation of a commercial Model Based Iterative reconstruction algorithm in computed tomography

IntroductionIterative reconstruction algorithms have been introduced in clinical practice to obtain dose reduction without compromising the diagnostic performance. PurposeTo investigate the commercial Model Based IMR algorithm by means of patient dose and image quality, with standard Fourier and alternative metrics. Materials and methodsA Catphan phantom, a commercial density phantom and a cylindrical water filled phantom were scanned both varying CTDIvol and reconstruction thickness. Images were then reconstructed with Filtered Back Projection and both statistical (iDose) and Model Based (IMR) Iterative reconstruction algorithms.Spatial resolution was evaluated with Modulation Transfer Function and Target Transfer Function. Noise reduction was investigated with Standard Deviation. Furthermore, its behaviour was analysed with 3D and 2D Noise Power Spectrum. Blur and Low Contrast Detectability were investigated.Patient dose indexes were collected and analysed. ResultsAll results, related to image quality, have been compared to FBP standard reconstructions.Model Based IMR significantly improves Modulation Transfer Function with an increase between 12% and 64%. Target Transfer Function curves confirm this trend for high density objects, while Blur presents a sharpness reduction for low density details.Model Based IMR underlines a noise reduction between 44% and 66% and a variation in noise power spectrum behaviour. Low Contrast Detectability curves underline an averaged improvement of 35–45%; these results are compatible with an achievable reduction of 50% of CTDIvol.A dose reduction between 25% and 35% is confirmed by median values of CTDIvol. ConclusionIMR produces an improvement in image quality and dose reduction.

Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs.

To investigate the use of local noise power spectrum (NPS) to characterize image noise and wavelet analysis to isolate defective pixels and inter-subpanel flat-fielding artifacts for quantitative quality assurance (QA) of electronic portal imaging devices (EPIDs). A total of 93 image sets including custom-made bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Global quantitative metrics such as modulation transform function (MTF), NPS, and detective quantum efficiency (DQE) were computed for each image set. Local NPS was also calculated for individual subpanels by sampling region of interests within each subpanel of the EPID. The 1D NPS, obtained by radially averaging the 2D NPS, was fitted to a power-law function. The r-square value of the linear regression analysis was used as a singular metric to characterize the noise properties of individual subpanels of the EPID. The sensitivity of the local NPS was first compared with the global quantitative metrics using historical image sets. It was then compared with two commonly used commercial QA systems with images collected after applying two different EPID calibration methods (single-level gain and multilevel gain). To detect isolated defective pixels and inter-subpanel flat-fielding artifacts, Haar wavelet transform was applied on the images. Global quantitative metrics including MTF, NPS, and DQE showed little change over the period of data collection. On the contrary, a strong correlation between the local NPS (r-square values) and the variation of the EPID noise condition was observed. The local NPS analysis indicated image quality improvement with the r-square values increased from 0.80 ± 0.03 (before calibration) to 0.85 ± 0.03 (after single-level gain calibration) and to 0.96 ± 0.03 (after multilevel gain calibration), while the commercial QA systems failed to distinguish the image quality improvement between the two calibration methods. With wavelet analysis, defective pixels and inter-subpanel flat-fielding artifacts were clearly identified as spikes after thresholding the inversely transformed images. The proposed local NPS (r-square values) showed superior sensitivity to the noise level variations of individual subpanels compared with global quantitative metrics such as MTF, NPS, and DQE. Wavelet analysis was effective in detecting isolated defective pixels and inter-subpanel flat-fielding artifacts. The proposed methods are promising for the early detection of imaging artifacts of EPIDs.

Impact of bowtie filter and object position on the two-dimensional noise power spectrum of a clinical MDCT system.

Noise characteristics of clinical multidetector CT (MDCT) systems can be quantified by the noise power spectrum (NPS). Although the NPS of CT has been extensively studied in the past few decades, the joint impact of the bowtie filter and object position on the NPS has not been systematically investigated. This work studies the interplay of these two factors on the two dimensional (2D) local NPS of a clinical CT system that uses the filtered backprojection algorithm for image reconstruction. A generalized NPS model was developed to account for the impact of the bowtie filter and image object location in the scan field-of-view (SFOV). For a given bowtie filter, image object, and its location in the SFOV, the shape and rotational symmetries of the 2D local NPS were directly computed from the NPS model without going through the image reconstruction process. The obtained NPS was then compared with the measured NPSs from the reconstructed noise-only CT images in both numerical phantom simulation studies and experimental phantom studies using a clinical MDCT scanner. The shape and the associated symmetry of the 2D NPS were classified by borrowing the well-known atomic spectral symbols s, p, and d, which correspond to circular, dumbbell, and cloverleaf symmetries, respectively, of the wave function of electrons in an atom. Finally, simulated bar patterns were embedded into experimentally acquired noise backgrounds to demonstrate the impact of different NPS symmetries on the visual perception of the object. (1) For a central region in a centered cylindrical object, an s-wave symmetry was always present in the NPS, no matter whether the bowtie filter was present or not. In contrast, for a peripheral region in a centered object, the symmetry of its NPS was highly dependent on the bowtie filter, and both p-wave symmetry and d-wave symmetry were observed in the NPS. (2) For a centered region-ofinterest (ROI) in an off-centered object, the symmetry of its NPS was found to be different from that of a peripheral ROI in the centered object, even when the physical positions of the two ROIs relative to the isocenter were the same. (3) The potential clinical impact of the highly anisotropic NPS, caused by the interplay of the bowtie filter and position of the image object, was highlighted in images of specific bar patterns oriented at different angles. The visual perception of the bar patterns was found to be strongly dependent on their orientation. The NPS of CT depends strongly on the bowtie filter and object position. Even if the location of the ROI with respect to the isocenter is fixed, there can be different symmetries in the NPS, which depend on the object position and the size of the bowtie filter. For an isolated off-centered object, the NPS of its CT images cannot be represented by the NPS measured from a centered object.

TU‐H‐207A‐05: Automated Early Identification of An Excessive Air‐In‐Oil X‐Ray Tube Artifact That Mimics Acute Cerebral Infarct

Purpose:There is an infrequent but serious CT artifact that occurs when there is too much air in the cooling oil of an x‐ray tube. This artifact manifests as patchy hypodensities and mimics acute cerebral infarct. Routine quality control testing is unlikely to detect this artifact before it is observed in patient images. The purpose of this project was to develop an automated, quantitative method that increased the likelihood of identifying and preventing such artifacts.Methods:Using QC phantom images with a known air‐in‐oil artifact, a 1D radial representation of the 2D noise power spectrum(NPS) was calculated and compared against that for artifact‐free images. The QC program software used at our institution to analyze daily phantom images was modified to include measuring the average frequency of NPS within the water section of daily phantom scans. The threshold values developed for each CT system were incorporated into our daily QC program and email notification system.Results:The NPS for the known air‐in‐oil artifact images included a large low frequency peak compared with artifact‐free images; the average NPS frequency for these images were 0.197 and 0.319 (1/mm), respectively. The average NPS frequency values (mean+/− standard deviation) for the GE CT750, GE VCT, GE Lightspeed Xtra, and Siemens SOMATOM Definition Flash scanners were 0.322+/− 0.0058, 0.324+/−0.0024, 0.320+/−0.0020, and 0.303+/−0.0039 (1/mm), respectively. Threshold values were chosen to be the average plus or minus twice the standard deviation. Automated QC successfully identified an air‐in‐oil artifact in the Lightspeed Xtra before any detrimental clinical effect occurred; the average NPS frequency value that triggered service was 0.307, which is six standard deviations smaller than average.Conclusion:Clinically serious problems associated with the air‐in‐oil artifact can be detected earlier and mitigated/avoided by incorporating the average frequency of NPS measurements of daily phantom images into an automated QC program.

Toward standardized quantitative image quality (IQ) assessment in computed tomography (CT): A comprehensive framework for automated and comparative IQ analysis based on ICRU Report 87

PurposeBased on the guidelines from “Report 87: Radiation Dose and Image-quality Assessment in Computed Tomography” of the International Commission on Radiation Units and Measurements (ICRU), a software framework for automated quantitative image quality analysis was developed and its usability for a variety of scientific questions demonstrated. MethodsThe extendable framework currently implements the calculation of the recommended Fourier image quality (IQ) metrics modulation transfer function (MTF) and noise-power spectrum (NPS), and additional IQ quantities such as noise magnitude, CT number accuracy, uniformity across the field-of-view, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated lesions for a commercially available cone-beam phantom. Sample image data were acquired with different scan and reconstruction settings on CT systems from different manufacturers. ResultsSpatial resolution is analyzed in terms of edge-spread function, line-spread-function, and MTF. 3D NPS is calculated according to ICRU Report 87, and condensed to 2D and radially averaged 1D representations. Noise magnitude, CT numbers, and uniformity of these quantities are assessed on large samples of ROIs. Low-contrast resolution (CNR, SNR) is quantitatively evaluated as a function of lesion contrast and diameter. Simultaneous automated processing of several image datasets allows for straightforward comparative assessment. ConclusionsThe presented framework enables systematic, reproducible, automated and time-efficient quantitative IQ analysis. Consistent application of the ICRU guidelines facilitates standardization of quantitative assessment not only for routine quality assurance, but for a number of research questions, e.g. the comparison of different scanner models or acquisition protocols, and the evaluation of new technology or reconstruction methods.

SU‐C‐304‐05: Use of Local Noise Power Spectrum and Wavelets in Comprehensive EPID Quality Assurance

Purpose:As EPIDs are increasingly used for IMRT QA and real‐time treatment verification, comprehensive quality assurance (QA) of EPIDs becomes critical. Current QA with phantoms such as the Las Vegas and PIPSpro™ can fail in the early detection of EPID artifacts. Beyond image quality assessment, we propose a quantitative methodology using local noise power spectrum (NPS) to characterize image noise and wavelet transform to identify bad pixels and inter‐subpanel flat‐fielding artifacts.Methods:A total of 93 image sets including bar‐pattern images and open exposure images were collected from four iViewGT a‐Si EPID systems over three years. Quantitative metrics such as modulation transform function (MTF), NPS and detective quantum efficiency (DQE) were computed for each image set. Local 2D NPS was calculated for each subpanel. A 1D NPS was obtained by radial averaging the 2D NPS and fitted to a power‐law function. R‐square and slope of the linear regression analysis were used for panel performance assessment. Haar wavelet transformation was employed to identify pixel defects and non‐uniform gain correction across subpanels.Results:Overall image quality was assessed with DQE based on empirically derived area under curve (AUC) thresholds. Using linear regression analysis of 1D NPS, panels with acceptable flat fielding were indicated by r‐square between 0.8 and 1, and slopes of −0.4 to −0.7. However, for panels requiring flat fielding recalibration, r‐square values less than 0.8 and slopes from +0.2 to −0.4 were observed. The wavelet transform successfully identified pixel defects and inter‐subpanel flat fielding artifacts. Standard QA with the Las Vegas and PIPSpro phantoms failed to detect these artifacts.Conclusion:The proposed QA methodology is promising for the early detection of imaging and dosimetric artifacts of EPIDs. Local NPS can accurately characterize the noise level within each subpanel, while the wavelet transforms can detect bad pixels and inter‐subpanel flat fielding artifacts.

Noise Power Spectrum in PROPELLER MR Imaging.

The noise power spectrum (NPS), an index for noise evaluation, represents the frequency characteristics of image noise. We measured the NPS in PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) magnetic resonance (MR) imaging, a nonuniform data sampling technique, as an initial study for practical MR image evaluation using the NPS. The 2-dimensional (2D) NPS reflected the k-space sampling density and showed agreement with the shape of the k-space trajectory as expected theoretically. Additionally, the 2D NPS allowed visualization of a part of the image reconstruction process, such as filtering and motion correction.

SU-C-9A-02: Structured Noise Index as An Automated Quality Control for Nuclear Medicine: A Two Year Experience

Purpose: Flood-field uniformity evaluation is an essential element in the assessment of nuclear medicine (NM) gamma cameras. It serves as the central element of the quality control (QC) program, acquired and analyzed on a daily basis prior to clinical imaging. Uniformity images are traditionally analyzed using pixel value-based metrics which often fail to capture subtle structure and patterns caused by changes in gamma camera performance requiring additional visual inspection which is subjective and time demanding. The goal of this project was to develop and implement a robust QC metrology for NM that is effective in identifying non-uniformity issues, reporting issues in a timely manner for efficient correction prior to clinical involvement, all incorporated into an automated effortless workflow, and to characterize the program over a two year period. Methods: A new quantitative uniformity analysis metric was developed based on 2D noise power spectrum metrology and confirmed based on expert observer visual analysis. The metric, termed Structured Noise Index (SNI) was then integrated into an automated program to analyze, archive, and report on daily NM QC uniformity images. The effectiveness of the program was evaluated over a period of 2 years. Results: The SNI metric successfully identified visually apparent non-uniformities overlooked by the pixel valuebased analysis methods. Implementation of the program has resulted in nonuniformity identification in about 12% of daily flood images. In addition, due to the vigilance of staff response, the percentage of days exceeding trigger value shows a decline over time. Conclusion: The SNI provides a robust quantification of the NM performance of gamma camera uniformity. It operates seamlessly across a fleet of multiple camera models. The automated process provides effective workflow within the NM spectra between physicist, technologist, and clinical engineer. The reliability of this process has made it the preferred platform for NM uniformity analysis.

An algorithm for noise correction of dual-energy computed tomography material density images.

Dual-energy computed tomography (DECT) images can undergo a two-material decomposition process which results in two images containing material density information. Material density images obtained by that process result in images with increased pixel noise. Noise reduction in those images is desirable in order to improve image quality. A noise reduction algorithm for material density images was developed and tested. A three-level wavelet approach combined with the application of an anisotropic diffusion filter was used. During each level, the resulting noise maps are further processed, until the original resolution is reached and the final noise maps obtained. Our method works in image space and, therefore, can be applied to any type of material density images obtained from any DECT vendor. A quantitative evaluation of the noise-reduced images using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and 2D noise power spectrum was done to quantify the improvements. The noise reduction algorithm was applied to a set of images resulting in images with higher SNR and CNR than the raw density images obtained by the decomposition process. The average improvement in terms of SNR gain was about 49 % while CNR gain was about 52 %. The difference between the raw and filtered regions of interest mean values was far from reaching statistical significance (minimum [Formula: see text], average [Formula: see text]). We have demonstrated through a series of quantitative analyses that our novel noise reduction algorithm improves the image quality of DECT material density images.

Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance

To reduce radiation dose in CT imaging, the statistical model based iterative reconstruction (MBIR) method has been introduced for clinical use. Based on the principle of MBIR and its nonlinear nature, the noise performance of MBIR is expected to be different from that of the well-understood filtered backprojection (FBP) reconstruction method. The purpose of this work is to experimentally assess the unique noise characteristics of MBIR using a state-of-the-art clinical CT system. Three physical phantoms, including a water cylinder and two pediatric head phantoms, were scanned in axial scanning mode using a 64-slice CT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI) at seven different mAs levels (5, 12.5, 25, 50, 100, 200, 300). At each mAs level, each phantom was repeatedly scanned 50 times to generate an image ensemble for noise analysis. Both the FBP method with a standard kernel and the MBIR method (Veo(®), GE Healthcare, Waukesha, WI) were used for CT image reconstruction. Three-dimensional (3D) noise power spectrum (NPS), two-dimensional (2D) NPS, and zero-dimensional NPS (noise variance) were assessed both globally and locally. Noise magnitude, noise spatial correlation, noise spatial uniformity and their dose dependence were examined for the two reconstruction methods. (1) At each dose level and at each frequency, the magnitude of the NPS of MBIR was smaller than that of FBP. (2) While the shape of the NPS of FBP was dose-independent, the shape of the NPS of MBIR was strongly dose-dependent; lower dose lead to a "redder" NPS with a lower mean frequency value. (3) The noise standard deviation (σ) of MBIR and dose were found to be related through a power law of σ ∝ (dose)(-β) with the component β ≈ 0.25, which violated the classical σ ∝ (dose)(-0.5) power law in FBP. (4) With MBIR, noise reduction was most prominent for thin image slices. (5) MBIR lead to better noise spatial uniformity when compared with FBP. (6) A composite image generated from two MBIR images acquired at two different dose levels (D1 and D2) demonstrated lower noise than that of an image acquired at a dose level of D1+D2. The noise characteristics of the MBIR method are significantly different from those of the FBP method. The well known tradeoff relationship between CT image noise and radiation dose has been modified by MBIR to establish a more gradual dependence of noise on dose. Additionally, some other CT noise properties that had been well understood based on the linear system theory have also been altered by MBIR. Clinical CT scan protocols that had been optimized based on the classical CT noise properties need to be carefully re-evaluated for systems equipped with MBIR in order to maximize the method's potential clinical benefits in dose reduction and/or in CT image quality improvement.

A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise‐power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom

To develop an easily-implemented technique with free publicly-available analysis software to measure the modulation transfer function (MTF) and noise-power spectrum (NPS) of a clinical computed tomography (CT) system from images acquired using a widely-available and standardized American College of Radiology (ACR) CT accreditation phantom. Images of the ACR phantom were acquired on a Siemens SOMATOM Definition Flash system using a standard adult head protocol: 120 kVp, 300 mAs, and reconstructed voxel size of 0.49 mm × 0.49 mm × 4.67 mm. The radial (axial) MTF was measured using an edge method where the boundary of the third module of the ACR phantom, originally designed to measure uniformity and noise, was used as a circular edge. The 3D NPS was measured using images from this same module and using a previously-described methodology that quantifies noise magnitude and 3D noise correlation. The axial MTF was radially symmetrical and had a value of 0.1 at 0.62 mm(-1). The 3D NPS shape was consistent with the filter-ramp function of filtered-backprojection reconstruction algorithms and previously reported values. The radial NPS peak value was ∼115 HU(2)mm(3) at ∼0.25 mm(-1) and dropped to 0 HU(2)mm(3) by 0.8 mm(-1). The authors have developed an easily-implementable technique to measure the axial MTF and 3D NPS of clinical CT systems using an ACR phantom. The widespread availability of the phantom along with the free software the authors have provided will enable many different institutions to immediately measure MTF and NPS values for comparison of protocols and systems.