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A multichannel block-matching denoising algorithm for spectral photon-counting CT images.

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Title: A multichannel block-matching denoising algorithm for spectral photon-counting CT images.
Authors: Harrison, Adam P.1, Xu, Ziyue1 ziyue.xu@gmail.com, Pourmorteza, Amir2, Bluemke, David A.2, Mollura, Daniel J.1
Source: Medical Physics. Jun2017, Vol. 44 Issue 6, p2447-2452. 6p.
Subjects: Photon counting, Photometry, Computed tomography, Radiotherapy, Electrotherapeutics
Abstract: Purpose We present a denoising algorithm designed for a whole-body prototype photon-counting computed tomography ( PCCT) scanner with up to 4 energy thresholds and associated energy-binned images. Methods Spectral PCCT images can exhibit low signal to noise ratios ( SNRs) due to the limited photon counts in each simultaneously-acquired energy bin. To help address this, our denoising method exploits the correlation and exact alignment between energy bins, adapting the highly-effective block-matching 3D ( BM3D) denoising algorithm for PCCT. The original single-channel BM3D algorithm operates patch-by-patch. For each small patch in the image, a patch grouping action collects similar patches from the rest of the image, which are then collaboratively filtered together. The resulting performance hinges on accurate patch grouping. Our improved multi-channel version, called BM3D_ PCCT, incorporates two improvements. First, BM3D_ PCCT uses a more accurate shared patch grouping based on the image reconstructed from photons detected in all 4 energy bins. Second, BM3D_ PCCT performs a cross-channel decorrelation, adding a further dimension to the collaborative filtering process. These two improvements produce a more effective algorithm for PCCT denoising. Results Preliminary results compare BM3D_ PCCT against BM3D_Naive, which denoises each energy bin independently. Experiments use a three-contrast PCCT image of a canine abdomen. Within five regions of interest, selected from paraspinal muscle, liver, and visceral fat, BM3D_ PCCT reduces the noise standard deviation by 65.0%, compared to 40.4% for BM3D_Naive. Attenuation values of the contrast agents in calibration vials also cluster much tighter to their respective lines of best fit. Mean angular differences (in degrees) for the original, BM3D_Naive, and BM3D_ PCCT images, respectively, were 15.61, 7.34, and 4.45 (iodine); 12.17, 7.17, and 4.39 (galodinium); and 12.86, 6.33, and 3.96 (bismuth). Conclusion We outline a multi-channel denoising algorithm tailored for spectral PCCT images, demonstrating improved performance over an independent, yet state-of-the-art, single-channel approach. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A multichannel block-matching denoising algorithm for spectral photon-counting CT images.
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  Data: <searchLink fieldCode="AR" term="%22Harrison%2C+Adam+P%2E%22">Harrison, Adam P.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xu%2C+Ziyue%22">Xu, Ziyue</searchLink><relatesTo>1</relatesTo><i> ziyue.xu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Pourmorteza%2C+Amir%22">Pourmorteza, Amir</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Bluemke%2C+David+A%2E%22">Bluemke, David A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Mollura%2C+Daniel+J%2E%22">Mollura, Daniel J.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jun2017, Vol. 44 Issue 6, p2447-2452. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Photon+counting%22">Photon counting</searchLink><br /><searchLink fieldCode="DE" term="%22Photometry%22">Photometry</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Radiotherapy%22">Radiotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Electrotherapeutics%22">Electrotherapeutics</searchLink>
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  Label: Abstract
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  Data: Purpose We present a denoising algorithm designed for a whole-body prototype photon-counting computed tomography ( PCCT) scanner with up to 4 energy thresholds and associated energy-binned images. Methods Spectral PCCT images can exhibit low signal to noise ratios ( SNRs) due to the limited photon counts in each simultaneously-acquired energy bin. To help address this, our denoising method exploits the correlation and exact alignment between energy bins, adapting the highly-effective block-matching 3D ( BM3D) denoising algorithm for PCCT. The original single-channel BM3D algorithm operates patch-by-patch. For each small patch in the image, a patch grouping action collects similar patches from the rest of the image, which are then collaboratively filtered together. The resulting performance hinges on accurate patch grouping. Our improved multi-channel version, called BM3D_ PCCT, incorporates two improvements. First, BM3D_ PCCT uses a more accurate shared patch grouping based on the image reconstructed from photons detected in all 4 energy bins. Second, BM3D_ PCCT performs a cross-channel decorrelation, adding a further dimension to the collaborative filtering process. These two improvements produce a more effective algorithm for PCCT denoising. Results Preliminary results compare BM3D_ PCCT against BM3D_Naive, which denoises each energy bin independently. Experiments use a three-contrast PCCT image of a canine abdomen. Within five regions of interest, selected from paraspinal muscle, liver, and visceral fat, BM3D_ PCCT reduces the noise standard deviation by 65.0%, compared to 40.4% for BM3D_Naive. Attenuation values of the contrast agents in calibration vials also cluster much tighter to their respective lines of best fit. Mean angular differences (in degrees) for the original, BM3D_Naive, and BM3D_ PCCT images, respectively, were 15.61, 7.34, and 4.45 (iodine); 12.17, 7.17, and 4.39 (galodinium); and 12.86, 6.33, and 3.96 (bismuth). Conclusion We outline a multi-channel denoising algorithm tailored for spectral PCCT images, demonstrating improved performance over an independent, yet state-of-the-art, single-channel approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2017
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