Daniel Gomez-Cardona

Hi‐Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance

PURPOSE The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. METHODS A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. RESULTS At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. CONCLUSIONS The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions.

Noise performance studies of model-based iterative reconstruction (MBIR) as a function of kV, mA and exposure level: Impact on radiation dose reduction and image quality

The significance of understanding the noise properties of clinical CT systems is twofold: First, as the diagnostic performance (particularly for the detection of low contrast lesions) is strongly limited by noise, a thorough study of the dependence of image noise on scanning and reconstruction parameters would enable the desired image quality to be achieved with the least amount of radiation dose; Second, a clear understanding of the noise properties of CT systems would allow the limitations in existing CT systems to be identified and improved. The recent introduction of the model-based iterative reconstruction (MBIR) method has introduced strong nonlinearity to clinical CT systems and violated the classical relationship between CT noise properties and CT system parameters, therefore it is necessary to perform a comprehensive study on the noise properties of MBIR. The purpose of this study was to systematically study the dependence of the noise magnitude and noise texture of MBIR on x-ray tube potential (kV), tube current (mA), and radiation dose level. It has been found that the noise variance σ2 of MBIR has relaxed dependence on kV and mA, which can be described as power-law relationships as σ2 ∝kV−1 and σ2 ∝ mA−0.4, respectively. The shape of the noise power spectrum (NPS) demonstrated a strong dependence on kV and mA, but it remained constant as long as the radiation dose level was the same. These semi-empirical relationships can be potentially used to guide the optimal selection of kV and mA when prescribing CT scans with the maximal dose reduction.

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study.

PURPOSE Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis-particularly in younger patients-might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT. METHODS An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo(TM), GE Healthcare) were used to reconstruct CT images of the airways. For each kV-mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV-mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV-mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. RESULTS The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose levels. For FBP, the relative bias and the angular standard deviation of the measured WT increased steeply with decreasing radiation dose. Except for the smallest airway, MBIR enabled significant reduction in both the relative bias and angular standard deviation of the WT, particularly at low radiation dose levels; the SSQ was reduced by 50%-96% by using MBIR. The optimal reconstruction algorithm was found to be MBIR for the seven airways being assessed, and the combined use of MBIR and optimal kV-mAs selection resulted in a radiation dose reduction of 37%-83% compared with a reference scan protocol with a dose level of 1 mGy. CONCLUSIONS The quantification accuracy of airway WT is strongly influenced by radiation dose and reconstruction algorithm. The MBIR algorithm potentially allows the desired WT quantification accuracy to be achieved with reduced radiation dose, which may enable a wider clinical use of MDCT for the assessment of airway WT, particularly for younger patients who may be more sensitive to exposures with ionizing radiation.

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)

PURPOSE 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. METHODS 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. RESULTS (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. CONCLUSIONS 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.

Low signal correction scheme for low dose CBCT: the good, the bad, and the ugly

Reducing radiation dose in C-arm Cone-beam CT (CBCT) image-guided interventional procedures is of great importance. However, reducing radiation dose may increase noise magnitude and generate noise streaks in the reconstructed image. Several approaches, ranging from simple to highly complex methods, have been proposed in an attempt to reduce noise and mitigate artifacts caused by low detector counts. These approaches include apodizing the ramp kernel used before backprojection, using an adaptive trimmed mean filter based on local flux information, employing penalized-likelihood approaches or edge-preserving filters for sinogram smoothing, incorporating statistical models into the so-called model based iterative reconstruction framework, and more. This work presents a simple yet powerful scheme for low signal correction in low dose CBCT by applying local anisotropic diffusion filtration to the raw detector data prior to the logarithmic transform. It was found that low signal correction efficiently reduced noise magnitude and noise streaks without considerably sacrificing spatial resolution. Yet caution must be taken when selecting the parameters used for low signal correction so that no spurious information is enhanced and noise streaks are effectively reduced.

Modified ideal observer model (MIOM) for high‐contrast and high‐spatial resolution CT imaging tasks

Purpose Although a variety of mathematical observer models have been developed to predict human observer performance for low contrast lesion detection tasks, their predictive power for high contrast and high spatial resolution discrimination imaging tasks, including those in CT bone imaging, could be limited. The purpose of this work was to develop a modified observer model that has improved correlation with human observer performance for these tasks. Methods The proposed observer model, referred to as the modified ideal observer model (MIOM), uses a weight function to penalize components in the task function that have less contribution to the actual human observer performance for high contrast and high spatial resolution discrimination tasks. To validate MIOM, both human observer and observer model studies were performed, each using exactly the same CT imaging task [discrimination of a connected component in a high contrast (1000 HU) high spatial resolution bone fracture model (0.3 mm)] and experimental CT image data. For the human observer studies, three physicist observers rated the connectivity of the fracture model using a five‐point Likert scale; for the observer model studies, a total of five observer models, including both conventional models and the proposed MIOM, were used to calculate the discrimination capability of the CT images in resolving the connected component. Images used in the studies encompassed nine different reconstruction kernels. Correlation between human and observer model performance for these kernels were quantified using the Spearman rank correlation coefficient (ρ). After the validation study, an example application of MIOM was presented, in which the observer model was used to select the optimal reconstruction kernel for a High‐Resolution (Hi‐Res, GE Healthcare) CT scan technique. Results The performance of the proposed MIOM correlated well with that of the human observers with a Spearman rank correlation coefficient ρ of 0.88 (P = 0.003). In comparison, the value of ρ was 0.05 (P = 0.904) for the ideal observer, 0.05 (P = 0.904) for the non‐prewhitening observer, −0.18 (P = 0.634) for the non‐prewhitening observer with eye filter and internal noise, and 0.30 (P = 0.427) for the prewhitening observer with eye filter and internal noise. Using the validated MIOM, the optimal reconstruction kernel for the Hi‐Res mode to perform high spatial resolution and high contrast discrimination imaging tasks was determined to be the HD Ultra kernel at the center of the scan field of view (SFOV), or the Lung kernel at the peripheral region of the SFOV. This result was consistent with visual observations of nasal CT images of an in vivo canine subject. Conclusion Compared with other observer models, the proposed modified ideal observer model provides significantly improved correlation with human observers for high contrast and high spatial resolution CT imaging tasks.

Quantitative accuracy of CT numbers: Theoretical analyses and experimental studies

PURPOSE The CT number accuracy, that is, CT number bias, plays an important role in clinical diagnosis. When strategies to reduce radiation dose are discussed, it is important to make sure that the CT number bias is controlled within an acceptable range. The purpose of this paper was to investigate the dependence of CT number bias on radiation dose level and on image contrast (i.e., the difference in CT number between the ROI and the background) in Computed Tomography (CT). METHODS A lesion-background model was introduced to theoretically study how the CT number bias changes with radiation exposure level and with CT number contrast when a simple linear reconstruction algorithm such as filtered backprojection (FBP) is used. The theoretical results were validated with experimental studies using a benchtop CT system equipped with a photon-counting detector (XC-HYDRA FX50, XCounter AB, Sweden) and a clinical diagnostic MDCT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI, USA) equipped with an energy-integrating detector. The Catphan phantom (Catphan 600, the Phantom Laboratory, Salem, NY, USA) was scanned at different mAs levels and 50 scans were performed for each mAs. The bias of CT number was evaluated for each combination of mAs and ROIs with different contrast levels. An anthropomorphic phantom (ATOM 10-year-old phantom, Model 706, CIRS Inc. Norfolk, VA, USA) with much more heterogeneous object content was used to test the applicability of the theory to the more general image object cases. RESULTS Both theoretical and experimental studies showed that the CT number bias is inversely proportional to the radiation exposure level yet linearly dependent on the CT number contrast between the lesion and the background, that is, Bias ( μ ^ 1 FBP ) = α mAs ( 1 + β Δ H U ) . CONCLUSIONS The quantitative accuracy of CT numbers can be problematic and thus needs some extra attention when radiation dose is reduced. In this work, we showed that the bias of the FBP reconstruction increases as mAs is reduced; both positive and negative bias can be observed depending on the contrast difference between a targeted ROI and its surrounding background tissues.

Noise and spatial resolution characteristics of unregularized statistical iterative reconstruction: an experimental phantom study

One of the main challenges in low dose x-ray computed tomography (CT) is the presence of highly structured noise. Model based iterative reconstruction methods (MBIR) have shown great potential to overcome this problem; however, they have also introduced an additional challenge: highly nonlinear behavior. One example is the noise variance vs. dose power-law, σ2 α (dose)−β, for which quasilinear FBP-based systems have a β value equal to 1, while MBIR methods have values in the range 0.4-0.6.1 This nonlinearity is attributed mainly to the regularization term of the objective function rather than the data fidelity term. Therefore, if statistical iterative reconstruction was performed in the absence of the regularization term, it could be possible to minimize the nonlinear imaging performance of these methods, while still taking advantage of the benefits from the data fidelity term. Once the image is reconstructed, an additional shift-invariant filter could be implemented to reduce the overall noise magnitude. In this work, the potential benefits of performing (I) unregularized statistical iterative reconstruction with additional image domain denoising are explored and compared against (II) regularized statistical iterative reconstruction using a total variation (TV) regularizer. Rigorous repeated phantom studies were performed at 5 exposure levels to assess the imaging performance in terms of noise and spatial resolution. Results regarding the power-law showed that for FBP reconstruction and for paradigm I, β= 1, while for paradigm II β= 0.6. Additionally, noise was independent of contrast in paradigm I, but was contrast dependent in paradigm II.

Impact of radiation dose level on CT number accuracy in photon counting CT

It is well known that when radiation dose is reduced in x-ray computed tomography, image noise increases and noise induced streaks may appear. However, to quantify the accuracy of the image reconstruction, another source of error, known as the statistical bias, also needs to be considered. In the projection domain, signal bias originates in the quantum nature of the x-ray photon fluctuations as well as the nonlinear nature of the logarithmic transform used to generate line integral data. The bias in the projection domain is then propagated into the image domain through the image reconstruction process to generate CT number biases. The purpose of this work is to experimentally study the dependence of CT number bias on the radiation dose.

Pushing the boundaries of diagnostic CT systems for high spatial resolution imaging tasks

In a previous work [Cruz-Bastida et al Med. Phys. 43, 2399 (2016)], the spatial resolution performance of a new High-Resolution (Hi-Res) multi-detector row CT (MDCT) scan mode and the associated High Definition (HD) reconstruction kernels was systematically characterized. The purpose of the present work was to study the noise properties of the Hi-Res scan mode and the joint impact of spatial resolution and noise characteristics on high contrast and high spatial resolution imaging tasks. Using a physical phantom and a diagnostic MDCT system, equipped with both Hi-Res and conventional scan modes, noise power spectrum (NPS) measurements were performed at 8 off-centered positions (0 to 14 cm with an increment of 2 cm) for 8 non-HD kernels and 7 HD kernels. An in vivo rabbit experiment was then performed to demonstrate the potential clinical value of the Hi-Res scan mode. Without the HD kernels, the Hi-Res scan mode preserved the shape of the NPS and slightly increased noise magnitude across all object positions. The combined use of the Hi-Res scan mode and HD kernels led to a greater noise increase and pushed the NPS towards higher frequencies, particularly for those edge-preserving or edge-enhancing HD kernels. Results of the in vivo rabbit study demonstrate important trade-offs between spatial resolution and noise characteristics. Overall, for a given high contrast and high spatial resolution imaging task (bronchi imaging), the benefit of spatial resolution improvement introduced by the Hi-Res scan mode outweighs the potential noise amplification, leading to better overall imaging performance for both centered and off-centered positions.