PurposeThe average (fav) or peak (fpeak) noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it.ApproachA model of CT NPS was created based on its fpeak and a half-Gaussian fit (σ) to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the fpeak/σ-space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the fpeak/σ-space. NPS differences were quantified by the noise texture contrast (Ctexture), the integral of the absolute NPS difference.ResultsThe two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for fpeak alone are 0.2 lp/cm for body and 0.4 lp/cm for lung NPSs. For σ, these values are 0.15 and 2 lp/cm, respectively. Thresholds change if the other parameter also changes. Different NPSs with the same fpeak or fav can be discriminated. Nonradiologist observers did not need more Ctexture than radiologists.Conclusionsfpeak or fav is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.
KEYWORDS: Modulation transfer functions, CT reconstruction, Computed tomography, Image quality, Computer simulations, Data acquisition, Deep learning, Sensors, Medical image reconstruction
As Deep Learning Reconstruction (DLR) begins to dominate computed tomography (CT) reconstruction, performance evaluation via conventional phantoms with uniform backgrounds and specific sizes may benefit from augmentation with simulated controlled test objects inserted into anatomical backgrounds. The purpose of this study is to validate a simulation tool with physics-based image quality metrics both in phantom and in patient data. An analytic forward projection tool, based on detector and source geometry with beam spectra was designed to match the specifications of Canon Medical’s Aquilion ONE Prism. The CatphanTM 500 and two water phantoms, 24cm and 32cm in diameter, were scanned with Aquilion ONE Prism at various mA levels and reconstructed with FBP. Corresponding simulated images were generated. The CT number, noise power spectrum (NPS) and modulation transfer function (MTF) were evaluated and compared between the simulated images and actual images. Simulated projection data of a CatphanTM sensitometry cylinder was also combined with a patient sinogram and reconstructed with a variety of kernels. The MTF of three different contrast rods were measured and compared with the MTF measured from CatphanTM. The CT numbers were equivalent between the simulated data and the image acquired from the actual CT system. The MTF measured from the simulated data of both phantom and patient image matched with the MTF from CatphanTM. The noise properties of the simulated data also aligned with the NPS of the 24cm and 32cm water phantom image. The simulation tool was able to generate images with image quality equivalent to the images scanned and reconstructed from the actual CT system. With this validation study, the simulation will be utilized to further evaluate the performance of deep learning reconstructions (DLRs).
Physicists generally use the Noise Power Spectra (NPS) and Standard Deviation (SD) to characterize noise properties associated with non-linear reconstruction algorithms. However, these metrics capture only first and second order statistics. The purpose of this work is to characterize the impact of the higher order statistics, commonly associated with non-linear reconstruction, on noise texture. Images of a 32cm water phantom were acquired on the Aquilion ONE Genesis Computed Tomography (CT) system and reconstructed with deep learning reconstruction (DLR), model-based iterative reconstruction (MBIR), hybrid iterative reconstruction (AIDR), and filtered backprojection (FBP). Regions of interest (ROIs) of 100x100pixels were extracted from the center of the images. Pure Gaussian noise counterpart image datasets with the same mean, SD, and NPS as each acquired data condition were also generated by convolving random white noise with the root-NPS of the acquired data. Nine naïve observers were tasked with distinguishing the acquired noise image from its pure Gaussian counterpart via a two-alternative forced choice experiment. Results showed the FBP images appeared indistinguishable from their pure Gaussian counterparts (Percent Correct=54%), while MBIR images were readily distinguishable from Gaussian ones (Percent Correct=98-100%). DLR and AIDR images were more difficult to distinguish from their pure Gaussian counterparts (Percent Correct=58-88%), than MBIR, which indicates that it is more similar in perceived texture to Gaussian noise. This work demonstrates the appearance of CT noise texture may be dependent on higher orders statistics not captured by the NPS; noise textures with identical NPS and SD can be distinguished based on non-Gaussian properties.
The Noise Power Spectra (NPS) only characterizes first and second order statistics associated with noise in Computed
Tomography (CT) reconstructions. The purpose of this work is to characterize the impact of the higher order statistics on
perception of noise texture for a variety of reconstruction algorithms. Images of a 32 cm water phantom were acquired on
the Aquilion ONE Genesis CT system and reconstructed with AiCE deep learning reconstruction (DLR), model-based
iterative reconstruction (MBIR), hybrid iterative reconstruction (AIDR), and filtered backprojection (FBP). Regions of
interest (ROIs) of 100x100pixels were extracted from the center of the images and 4th order statistics of each ROI were
assessed via excess kurtosis measurement. Pure Gaussian noise counterpart image datasets with the same mean, standard
deviation (SD), and NPS as each acquired data condition were also generated by convolving random white noise with the
root-NPS of the acquired data. Nine naïve observers were tasked with distinguishing the acquired noise image from the
pure Gaussian counterpart via a two-alternative forced choice experiment. Excess kurtosis in the image ROIs was 0.01
for FBP, 0.74 to 0.85 for FIRST, 0.03 to 0.08 for AIDR, and -0.13 to 0.21 for AiCE. Results showed the FBP images appeared
indistinguishable from their pure Gaussian counterparts with a Percent Correct (PC)=54%, while MBIR images were
readily distinguishable from their pure Gaussian counterparts, PC=98 to 100%. DLR and AIDR images are more difficult
to distinguish from their pure Gaussian counterparts, with the PC ranging from 58% to 88%. The discriminability index
derived from the PCs correlated strongly with excess kurtosis.
Noise texture in CT images, commonly characterized by using the noise power spectrum (NPS), is mainly dictated by the shape of the reconstruction kernel. The peak frequency of the NPS (fpeak) is often used as a one-parameter metric for characterizing noise texture. However, if the downslope of the NPS beyond the fpeak influences noise texture visibly, then fpeak is insufficient as a single descriptor. Therefore, we investigated the human-detectable differences in NPSs having different fpeak and/or downslope parameters. NPSs were estimated using various reconstruction kernels on a commercial CT scanner. To quantify NPS downslope, half of a Gaussian function was fit through the NPS portion that lies beyond fpeak. The σ of this Gaussian was used as the downslope descriptor of the NPS. A two alternative forced choice observer study was performed to determine the just noticeable- differences (JND) in fpeak only, σ only, and both simultaneously. Visibility thresholds for these changes were determined and an elliptical limiting detectability boundary was determined. The JND threshold ellipse is centered on the reference values and has a major and minor radius of 0.47 lp/cm and 0.12 lp/cm, respectively. The major radius makes an angle of 143° with the x-axis. A change in only fpeak of 0.2 lp/cm is below the detection threshold. This number changes if the apodization part of the NPS changes simultaneously. In conclusion, both the peak frequency and the apodization section of the NPS influence the detectability of changes in image noise texture.
Chronic kidney disease (CKD) is associated with gradual bone loss that occurs from the failure of the kidneys to regulate bone mineralization. Degradation of bone structure can be quantified with the usage of Micro-CT. The current methods of quantitative imaging typically use a single region of interest (ROI) that segments the whole trabecular region and obtain bone parameters, which usually are not homogenous across such a large ROI. Here we introduce a novel method of quantifying bone parameters that can be used to determine overall bone health. This method analyzes sequential regions on the trabecular bone with multiple small ROIs and evaluates the gradients of bone parameters across these ROIs. Two C57Bl/6J mice femur groups were prepared: a control and CKD groups. All femurs were scanned with a Micro-CT system using tube voltage of 60 kV and current of 0.667 mA. Femur volumes were reconstructed with the Feldkamp-Davis-Kress algorithm and were imported into MicroView to perform bone analysis. Six different sequential ROIs were selected at different distances from the growth plate (0.5mm increments). The gradients of bone parameters along the ROI distance for the control and CKD group were compared. Significant differences were found between two groups in the gradients of bone volume density (P = 0.0002), connective density (P = 0.0003), trabecular spacing (P = 0.001), and trabecular number (P = 0.01). As a result, our method identified a sharp change in several parameters representing a novel and biologically significant strategy.
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