Justin Solomon

Diagnostic Performance of an Advanced Modeled Iterative Reconstruction Algorithm for Low-Contrast Detectability with a Third-Generation Dual-Source Multidetector CT Scanner: Potential for Radiation Dose Reduction in a Multireader Study

PURPOSE To assess the effect of radiation dose reduction on low-contrast detectability by using an advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) algorithm in a contrast-detail phantom with a third-generation dual-source multidetector computed tomography (CT) scanner. MATERIALS AND METHODS A proprietary phantom with a range of low-contrast cylindrical objects, representing five contrast levels (range, 5-20 HU) and three sizes (range, 2-6 mm) was fabricated with a three-dimensional printer and imaged with a third-generation dual-source CT scanner at various radiation dose index levels (range, 0.74-5.8 mGy). Image data sets were reconstructed by using different section thicknesses (range, 0.6-5.0 mm) and reconstruction algorithms (filtered back projection [FBP] and ADMIRE with a strength range of three to five). Eleven independent readers blinded to technique and reconstruction method assessed all data sets in two reading sessions by measuring detection accuracy with a two-alternative forced choice approach (first session) and by scoring the total number of visible object groups (second session). Dose reduction potentials based on both reading sessions were estimated. Results between FBP and ADMIRE were compared by using both paired t tests and analysis of variance tests at the 95% significance level. RESULTS During the first session, detection accuracy increased with increasing contrast, size, and dose index (diagnostic accuracy range, 50%-87%; interobserver variability, ±7%). When compared with FBP, ADMIRE improved detection accuracy by 5.2% on average across the investigated variables (P < .001). During the second session, a significantly increased number of visible objects was noted with increasing radiation dose index, section thickness, and ADMIRE strength over FBP (up to 80% more visible objects, P < .001). Radiation dose reduction potential ranged from 56% to 60% and from 4% to 80% during the two sessions, respectively. CONCLUSION Low-contrast detectability performance increased with increasing object size, object contrast, dose index, section thickness, and ADMIRE strength. Compared with FBP, ADMIRE allows a substantial radiation dose reduction while preserving low-contrast detectability. Online supplemental material is available for this article.

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

PURPOSE Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). METHODS Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. RESULTS In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was reduced by an average of 60% in SAFIRE images compared to FBP. However, for edge pixels, noise magnitude ranged from 20% higher to 40% lower in SAFIRE images compared to FBP. SAFIRE images of the lung phantom exhibited distinct regions with varying noise texture (i.e., noise autocorrelation/power spectra). CONCLUSIONS Quantum noise properties observed in uniform phantoms may not be representative of those in actual patients for nonlinear reconstruction algorithms. Anatomical texture should be considered when evaluating the performance of CT systems that use such nonlinear algorithms.

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

PURPOSE The purpose of this work was to assess the inherent image quality characteristics of a new multidetector computed tomography system in terms of noise, resolution, and detectability index as a function of image acquisition and reconstruction for a range of clinically relevant settings. METHODS A multisized image quality phantom (37, 30, 23, 18.5, and 12 cm physical diameter) was imaged on a SOMATOM Force scanner (Siemens Medical Solutions) under variable dose, kVp, and tube current modulation settings. Images were reconstructed with filtered back projection (FBP) and with advanced modeled iterative reconstruction (ADMIRE) with iterative strengths of 3, 4, and 5. Image quality was assessed in terms of the noise power spectrum (NPS), task transfer function (TTF), and detectability index for a range of detection tasks (contrasts of approximately 45, 90, 300, -900, and 1000 HU, and 2-20 mm diameter) based on a non-prewhitening matched filter model observer with eye filter. RESULTS Image noise magnitude decreased with decreasing phantom size, increasing dose, and increasing ADMIRE strength, offering up to 64% noise reduction relative to FBP. Noise texture in terms of the NPS was similar between FBP and ADMIRE (<5% shift in peak frequency). The resolution, based on the TTF, improved with increased ADMIRE strength by an average of 15% in the TTF 50% frequency for ADMIRE-5. The detectability index increased with increasing dose and ADMIRE strength by an average of 55%, 90%, and 163% for ADMIRE 3, 4, and 5, respectively. Assessing the impact of mA modulation for a fixed average dose over the length of the phantom, detectability was up to 49% lower in smaller phantom sections and up to 26% higher in larger phantom sections for the modulated scan compared to a fixed tube current scan. Overall, the detectability exhibited less variability with phantom size for modulated scans compared to fixed tube current scans. CONCLUSIONS Image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose. The use of tube current modulation resulted in more consistent image quality with changing phantom size.

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi–Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm

PURPOSE To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT). MATERIALS AND METHODS Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features. RESULTS Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998). CONCLUSION Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

Relating Noise to Image Quality Indicators in CT Examinations With Tube Current Modulation

OBJECTIVE Modern CT systems use surrogates of noise-noise index (NI) and quality reference effective tube current-time product (Q)-to infer the quality of images acquired using tube current modulation. This study aimed to determine the relationship between actual noise and these surrogates for two CT scanners from two different manufacturers. MATERIALS AND METHODS Two phantoms (adult and 1-year-old child) were imaged on two CT scanners (64 and 128 MDCT) using a clinical range of NI (6-22) and Q (30-300 mA). Each scan was performed twice, and noise was measured in the mediastinum, lung, and abdomen using an image subtraction technique. The effect on noise from changing other imaging parameters, such as beam collimation, pitch, peak kilovoltage, slice thickness, FOV, reconstruction kernel or algorithm, and patient age category (adult or pediatric), was investigated. RESULTS On the 64-MDCT scanner, noise increased linearly along with NI, with the slope affected by changing the anatomy of interest, peak kilovoltage, reconstruction algorithm, and convolution kernel. The noise-NI relationship was independent of phantom size, slice thickness, pitch, FOV, and beam width. On the 128-MDCT scanner, noise decreased nonlinearly along with increasing Q, slice thickness, and peak tube voltage. The noise-Q relationship also depended on anatomy of interest, phantom size, age selection, and reconstruction algorithm but was independent of pitch, FOV, and detector configuration. CONCLUSION We established how noise changes with changing image quality indicators across a clinically relevant range of imaging parameters. This work can aid in optimizing protocols by targeting specific noise levels for different types of CT examinations.

A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging

Realistic three-dimensional (3D) mathematical models of subtle lesions are essential for many computed tomography (CT) studies focused on performance evaluation and optimization. In this paper, we develop a generic mathematical framework that describes the 3D size, shape, contrast, and contrast-profile characteristics of a lesion, as well as a method to create lesion models based on CT data of real lesions. Further, we implemented a technique to insert the lesion models into CT images in order to create hybrid CT datasets. This framework was used to create a library of realistic lesion models and corresponding hybrid CT images. The goodness of fit of the models was assessed using the coefficient of determination (R(2)) and the visual appearance of the hybrid images was assessed with an observer study using images of both real and simulated lesions and receiver operator characteristic (ROC) analysis. The average R(2) of the lesion models was 0.80, implying that the models provide a good fit to real lesion data. The area under the ROC curve was 0.55, implying that the observers could not readily distinguish between real and simulated lesions. Therefore, we conclude that the lesion-modeling framework presented in this paper can be used to create realistic lesion models and hybrid CT images. These models could be instrumental in performance evaluation and optimization of novel CT systems.

Are uniform phantoms sufficient to characterize the performance of iterative reconstruction in CT

The evaluation of dose reduction potential from iterative reconstruction (IR) algorithms is an area of ongoing research in CT. The non-linearity of IR algorithms poses challenges to using traditional image quality metrics. Past attempts to evaluate iterative algorithms have relied on measurements taken from uniform background phantoms. In this study, noise is evaluated in CT images with no texture (water), fine texture (sponge + water), and gross texture (acrylic spheres + water. Images were reconstructed with a commercially available IR algorithm (SAFIRE 5) and filtered back projection (FBP). Noise was characterized in terms of its magnitude (pixel standard deviation) and stationarity across reconstruction algorithms and background types using an image subtraction technique. The IR algorithm reduced noise magnitude across all dose levels by 66 ±1%, 47 ±3%, and 29 ±4% in uniform, finely textured, and grossly textured backgrounds respectively. Noise was reasonably stationary in uniform FBP and IR images. For IR images with gross texture, pixel noise was 29 ±4% lower in acrylic sphere regions compared to water regions in the same slice. For FBP images, there were negligible differences between acrylic sphere and water regions in terms of pixel noise. This object-dependent noise is a feature of SAFIRE reconstruction that has not been previously reported.

Design of anthropomorphic textured phantoms for CT performance evaluation

Commercially available computed tomography (CT) technologies such as iterative reconstruction (IR) have the potential to enable reduced patient doses while maintaining diagnostic image quality. However, systematically determining safe dose reduction levels for IR algorithms is a challenging task due to their nonlinear nature. Most attempts to evaluate IR algorithms rely on measurements made in uniform phantoms. Such measurements may overstate the dose reduction potential of IR because they don’t account for the complex relationship between anatomical variability and image quality. The purpose of this study was to design anatomically informed textured phantoms for CT performance evaluation. Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom includes intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and imaged on a modern multi-slice clinical CT scanner to assess the noise performance of a commercial IR algorithm in the context of uniform and textured backgrounds. Fifty repeated acquisitions were acquired for each background type and noise was assessed by measuring pixel standard deviation, across the ensemble of repeated acquisitions. For pixels in uniform areas, the IR algorithm reduced noise magnitude (STD) by 60% (compared to FBP). However, for edge pixels, the noise magnitude in the IR images ranged from 20% higher to 40% lower compared to FBP. In all FBP images and in IR images of the uniform phantom, noise appeared to be globally non-stationary (i.e., spatially dependent) but locally stationary (within a reasonably small region of interest). In the IR images of the textured phantoms, the noise was globally and locally non-stationary.

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

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

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodules location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation ([Formula: see text], [Formula: see text] and [Formula: see text] of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the ([Formula: see text]) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.