A robust noise reduction technique for time resolved CT

Li, Zhoubo; Yu, Lifeng; Leng, Shuai; Williamson, Eric E.; Kotsenas, Amy L.; DeLone, David R.; Manduca, Armando; McCollough, Cynthia H.

doi:10.1118/1.4938576

Cited by 13 publications

(16 citation statements)

References 48 publications

(61 reference statements)

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“…We used a filter kernel four times wider along x , four times wider along y and two times less wide along z . If we divide our measured performance by a factor of eight (i.e., 4·4·0.5), we end up in an estimated performance of 60 s per slice, which is in a good agreement with the 14 min reported by Li et al for a 20 slices case.…”

Section: Resultssupporting

confidence: 83%

“…Medical Physics, 44 (7), July 2017 performance of 60 s per slice, which is in a good agreement with the 14 min reported by Li et al 18 for a 20 slices case.…”

Section: Massupporting

confidence: 90%

“…We implemented the PATEN filter exactly like in the original publication by Li et al, with the same patch window size in time. As the PATEN introduces a filtering over time, we reduced (similarly to the original authors of this method) the

σ_{p}

when different time steps were filtered together, in order to make the filter more conservative.…”

Section: Resultsmentioning

confidence: 99%

“…The PATEN filter has only one weight (because it does not take into account the Euclidean distance between the voxels):

f^{*} false(r, t false) = \frac{\sum_{bold-italicρ} \sum_{italicτ} f false(r + bold-italicρ, t + italicτ false) w_{normalp} false(r, bold-italicρ, t, italicτ false)}{\sum_{bold-italicρ} \sum_{italicτ} w_{normalp} false(r, bold-italicρ, t, italicτ false)}

where:

w_{normalp} false(r, bold-italicρ, t, italicτ false) = e^{- p false(r, bold-italicρ, t, italicτ false) / {italicσ}_{normalp}}

and:

\begin{matrix} p (bold-italicr, ρ, t, τ) = & \frac{1}{P} {false\sum}_{δ = (1 - P) / 2}^{(P - 1) / 2} (f false(r, t + italicδ false) \\ - f false(r + bold-italicρ, t + italicτ + italicδ false) {())}^{2} . \end{matrix}

Here,

σ_{p}

defines when two partial TACs can be considered to be similar. Following the original implementation of the PATEN filter, we defined

σ_{p}

as a...…”

Section: Methodsmentioning

confidence: 99%

“…Here, r p defines when two partial TACs can be considered to be similar. Following the original implementation of the PATEN filter, 18 we defined r p as a function of the image noise. We then applied two different scaling factors to it, depending if s = 0 or s 6 ¼ 0, in such a way to have the same ratio between the two final values as the one used in the original PATEN publication.…”

Section: E Paten Filtermentioning

confidence: 99%

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Noise reduction and functional maps image quality improvement in dynamic CT perfusion using a new k-means clustering guided bilateral filter (KMGB)

et al. 2017

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In conclusion, the KMGB filter leads to superior results for CT images and functional maps quality improvement, in significantly shorter computational times compared to the other filters. Our results suggest that the KMGB filter might be a more robust solution for halved-dose CTP datasets. For all the filters investigated, some artifacts start to appear on the BF maps if one sixth of the dose is simulated, suggesting that no one of the filters investigated in this study might be optimal for such a drastic dose reduction scenario.

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Section: Resultssupporting

confidence: 83%

“…Medical Physics, 44 (7), July 2017 performance of 60 s per slice, which is in a good agreement with the 14 min reported by Li et al 18 for a 20 slices case.…”

Section: Massupporting

confidence: 90%

σ_{p}

when different time steps were filtered together, in order to make the filter more conservative.…”

Section: Resultsmentioning

confidence: 99%

“…The PATEN filter has only one weight (because it does not take into account the Euclidean distance between the voxels):

f^{*} false(r, t false) = \frac{\sum_{bold-italicρ} \sum_{italicτ} f false(r + bold-italicρ, t + italicτ false) w_{normalp} false(r, bold-italicρ, t, italicτ false)}{\sum_{bold-italicρ} \sum_{italicτ} w_{normalp} false(r, bold-italicρ, t, italicτ false)}

where:

w_{normalp} false(r, bold-italicρ, t, italicτ false) = e^{- p false(r, bold-italicρ, t, italicτ false) / {italicσ}_{normalp}}

and:

\begin{matrix} p (bold-italicr, ρ, t, τ) = & \frac{1}{P} {false\sum}_{δ = (1 - P) / 2}^{(P - 1) / 2} (f false(r, t + italicδ false) \\ - f false(r + bold-italicρ, t + italicτ + italicδ false) {())}^{2} . \end{matrix}

Here,

σ_{p}

defines when two partial TACs can be considered to be similar. Following the original implementation of the PATEN filter, we defined

σ_{p}

as a...…”

Section: Methodsmentioning

confidence: 99%

Section: E Paten Filtermentioning

confidence: 99%

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Noise reduction and functional maps image quality improvement in dynamic CT perfusion using a new k-means clustering guided bilateral filter (KMGB)

et al. 2017

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Volumetric X-ray Imaging

2019

Hendee's Physics of Medical Imaging, Fifth Edition

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Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging

Liu

Huang

2019

J Applied Clin Med Phys

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Purpose Virtual monoenergetic images (VMIs) derived from dual‐energy computed tomography (DECT) have been explored for several clinical applications in recent years. However, VMIs at low and high keVs have high levels of noise. The aim of this study was to reduce image noise in VMIs by using a two‐step noise reduction technique. Methods VMI was first denoised using a modified highly constrained backprojection (HYPR) method. After the first‐step denoising, a general‐threshold filtering method was performed. Two sets of anthropomorphic phantoms were scanned with a clinical dual‐source DECT system. DECT data (80/140Sn kV) were reconstructed as VMI series at 12 different energy levels (range, 40‐150 keV, interval, 10 keV). For comparison, the averaged VMIs obtained from 10 repeated DECT scans were used as the reference standard. The signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR) and root‐mean‐square error (RMSE) were used to evaluate the quality of VMIs. Results Compared to the original HYPR method, the proposed two‐step image denoising method could provide better performance in terms of SNR, CNR, and RMSE. In addition, the proposed method could achieve effective noise reduction while preserving edges and small structures, especially for low‐keV VMIs. Conclusion The proposed two‐step image denoising method is a feasible method for reducing noise in VMIs obtained from a clinical DECT scanner. The proposed method can also reduce edge blurring and the loss of intensity in small lesions.

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A robust noise reduction technique for time resolved CT

Cited by 13 publications

References 48 publications

Noise reduction and functional maps image quality improvement in dynamic CT perfusion using a new k-means clustering guided bilateral filter (KMGB)

Noise reduction and functional maps image quality improvement in dynamic CT perfusion using a new k-means clustering guided bilateral filter (KMGB)

Volumetric X-ray Imaging

Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging

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