Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods

Chen, Baiyu; Christianson, Olav; Wilson, Joshua M.; Samei, Ehsan

doi:10.1118/1.4881519

Cited by 103 publications

(107 citation statements)

References 35 publications

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“…This choice was determined by manual tuning to obtain appropriate spectral smoothness, and is the same as that reported in a similar study (21) . In practice, the magnitude of the NPS at the spatial frequency origin

false({normalf}_{x} = {normalf}_{y} = 0 false)

is difficult to estimate accurately for finite data, as has been reported in previous literature (23) . Since the radial, 1D NPS varies linearly at low spatial frequencies, (4) the zero‐frequency value of the 1D NPS in this study was estimated via linear extrapolation.…”

Section: Methodsmentioning

confidence: 99%

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Practical considerations for noise power spectra estimation for clinical CT scanners

Dolly

Chen

Anastasio

et al. 2016

J Applied Clin Med Phys

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Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ∼0.15 mm−1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low‐frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task‐based image quality assessment.PACS number(s): 87.57.C‐, 87.57.Q‐

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false({normalf}_{x} = {normalf}_{y} = 0 false)

Section: Methodsmentioning

confidence: 99%

“…Previous studies have utilized various square ROI sizes, with side lengths of 128 pixels, 4 , 21 , 22 64 pixels, (23) and 32 pixels (19) . Similarly, multiple background removal methods have been proposed and implemented.…”

Section: Introductionmentioning

confidence: 99%

Practical considerations for noise power spectra estimation for clinical CT scanners

Dolly

Chen

Anastasio

et al. 2016

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…Inspired by this, our study focused on how size affects image quality with different imaging parameters using a second generation dual source CT scanner (SOMATOM Definition Flash, Siemens Healthcare). Some studies have reported the image quality from this new CT [5,11,13] . However, these studies focused on one particular technique, such as varying tube voltage, varying mAs, or comparing reconstruction methods.…”

Section: Introductionmentioning

confidence: 99%

“…Lower tube voltage can provide better contrast because iodine preferentially absorbs much more photons at low tube voltage, but at lower tube voltage, images can be undesirably noisy [8][9][10][11] . Iterative reconstructions have been proven to reduce radiation dose notably, but can also alter the noise texture and obscure potential lesions of interest against a "waxy appearance" [5,7,11,12] . To optimize protocols, these tradeoffs should be carefully and conjunctively considered.…”

Section: Introductionmentioning

confidence: 99%

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Size dependence of inherent image quality of a 2nd generation dual source CT scanner

Zhang

Solomon

Samei

2015

IJDI

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The fast developing CT technologies have complicated the protocol optimization process. Since there are increasing numbers of free parameters that can be adjusted, it is imperative to know exactly how these parameters affect the image quality. This paper examines the effect of varying dose level, tube voltage, reconstruction methods and the AEC function on the CT image quality across different sizes. A size varying phantom, consisting of five cylindrical tiers, was used. To assess the image quality, task transfer function (TTF), noise power spectra (NPS), contrast, and detectability index was computed and compared across sizes. It was found that from small to large size, detectability increased more as the dose increased. When the CTDIvol increased from 0.7 to 22.6 mGy, the detectability for the smallest size section increased by four fold but increased by eight fold for the largest section. Low tube voltage exhibited superior detectability and contrast for all sections, especially for the small size sections, where detectability doubled for the smallest section when tube voltage decreased from 120 kV to 80 kV. TTF curves showed considerable dependence on size, but more pronounced dependence on reconstruction techniques. In conclusion, small size phantoms were affected very differently from large size phantoms by dose levels, reconstruction methods, and tube voltage selection. Low tube voltage and iterative reconstruction technique can deliver superior image quality for small patients. Due to beam hardening and substantially increased noise at low tube voltage for large patients, high tube voltage is still recommended for large patients to retain image quality.

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A pitfall of using the circular‐edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images

Narita

Ohkubo

2020

J Applied Clin Med Phys

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The circular‐edge technique using a low‐contrast cylindrical object is commonly used to measure the modulation transfer functions (MTFs) in computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms. This method generally entails averaging multiple images of the cylinder to reduce the image noise. We suspected that the cylinder edge shape depicted in the IR images might exhibit slight deformation with respect to the true shape because of the intrinsic nonlinearity of IR algorithms. Image averaging can reduce the image noise, but does not effectively improve the deformation of the edge shape; thereby causing errors in the MTF measurements. We address this issue and propose a method to correct the MTF. We scanned a phantom including cylindrical objects with a CT scanner (Ingenuity Elite, Philips Healthcare). We obtained cylinder images with iterative model reconstruction (IMR) algorithms. The images suggested that the depicted edge shape deforms and fluctuates depending on slice positions. Because of this deformation, image averaging can potentially cause additional blurring. We define the deformation function D that describes the additional blurring, and obtain D by analyzing multiple images. The MTF measured by the circular‐edge method (referred to as MTF') can be thought of as the multiplication of the true MTF by the Fourier transformation (FT) of D. We thus obtain the corrected MTF (MTFcorrected) by dividing MTF' by the FT of D. We validate our correction method by comparing the calculated images based on the convolution theorem using MTF' and MTFcorrected with the actual images obtained with the scanner. The calculated image using MTFcorrected is more similar to the actual image compared with the image calculated using MTF', particularly in edge regions. We describe a pitfall in MTF measurement using the circular‐edge technique with image averaging, and suggest a method to correct it.

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Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods

Cited by 103 publications

References 35 publications

Practical considerations for noise power spectra estimation for clinical CT scanners

Practical considerations for noise power spectra estimation for clinical CT scanners

Size dependence of inherent image quality of a 2nd generation dual source CT scanner

A pitfall of using the circular‐edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images

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