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Title: Technical Note: Iterative megavoltage CT ( MVCT ) reconstruction using block‐matching 3D‐transform ( BM 3D) regularization

Abstract

Purpose Megavoltage CT ( MVCT ) images are noisier than kilovoltage CT ( KVCT ) due to low detector efficiency to high‐energy x rays. Conventional denoising methods compromise edge resolution and low‐contrast object visibility. In this work, we incorporated block‐matching 3D‐transform shrinkage ( BM 3D) transformation into MVCT iterative reconstruction as nonlocal patch‐wise regularization. Methods The iterative reconstruction was achieved by adding to the existing least square data fidelity objective a regularization term, formulated as the L1 norm of the BM 3D transformed image. A Fast Iterative Shrinkage‐Thresholding Algorithm ( FISTA ) was adopted to accelerate CT reconstruction. The proposed method was compared against total variation ( TV ) regularization, BM 3D postprocess method, and filtered back projection ( FBP ). Results In the Catphan phantom study, BM 3D regularization better enhances low‐contrast objects compared with TV regularization and BM 3D postprocess method at the same noise level. The spatial resolution using BM 3D regularization is 2.79 and 2.55 times higher than that using the TV regularization at 50% of the modulation transfer function ( MTF ) magnitude, for the fully sampled reconstruction and down‐sampled reconstruction, respectively. The BM 3D regularization images show better bony details and low‐contrast soft tissues,more » on the head and neck (H&N) and prostate patient images. Conclusions The proposed iterative BM 3D regularization CT reconstruction method takes advantage of both the BM 3D denoising capability and iterative reconstruction data fidelity consistency. This novel approach is superior to TV regularized iterative reconstruction or BM 3D postprocess for improving noisy MVCT image quality.« less

Authors:
 [1];  [2];  [3];  [2];  [1];  [2];  [1]
+ Show Author Affiliations
  1. Department of Radiation Oncology University of California Los Angeles Los Angeles CA USA
  2. Sir Run Run Shaw Hospital Zhejiang University School of Medicine Institute of Translational Medicine Zhejiang University Hangzhou China
  3. Department of Radiation Oncology Duke University Medical Center Durham NC USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1435395
Grant/Contract Number:  
DE‐SC0017057; DE‐SC0017687
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Medical Physics
Additional Journal Information:
Journal Name: Medical Physics Journal Volume: 45 Journal Issue: 6; Journal ID: ISSN 0094-2405
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Lyu, Qihui, Yang, Chunlin, Gao, Hao, Xue, Yi, O'Connor, Daniel, Niu, Tianye, and Sheng, Ke. Technical Note: Iterative megavoltage CT ( MVCT ) reconstruction using block‐matching 3D‐transform ( BM 3D) regularization. United States: N. p., 2018. Web. doi:10.1002/mp.12916.
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Lyu, Qihui, Yang, Chunlin, Gao, Hao, Xue, Yi, O'Connor, Daniel, Niu, Tianye, & Sheng, Ke. Technical Note: Iterative megavoltage CT ( MVCT ) reconstruction using block‐matching 3D‐transform ( BM 3D) regularization. United States. https://doi.org/10.1002/mp.12916
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Lyu, Qihui, Yang, Chunlin, Gao, Hao, Xue, Yi, O'Connor, Daniel, Niu, Tianye, and Sheng, Ke. Mon . "Technical Note: Iterative megavoltage CT ( MVCT ) reconstruction using block‐matching 3D‐transform ( BM 3D) regularization". United States. https://doi.org/10.1002/mp.12916.
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@article{osti_1435395,
title = {Technical Note: Iterative megavoltage CT ( MVCT ) reconstruction using block‐matching 3D‐transform ( BM 3D) regularization},
author = {Lyu, Qihui and Yang, Chunlin and Gao, Hao and Xue, Yi and O'Connor, Daniel and Niu, Tianye and Sheng, Ke},
abstractNote = {Purpose Megavoltage CT ( MVCT ) images are noisier than kilovoltage CT ( KVCT ) due to low detector efficiency to high‐energy x rays. Conventional denoising methods compromise edge resolution and low‐contrast object visibility. In this work, we incorporated block‐matching 3D‐transform shrinkage ( BM 3D) transformation into MVCT iterative reconstruction as nonlocal patch‐wise regularization. Methods The iterative reconstruction was achieved by adding to the existing least square data fidelity objective a regularization term, formulated as the L1 norm of the BM 3D transformed image. A Fast Iterative Shrinkage‐Thresholding Algorithm ( FISTA ) was adopted to accelerate CT reconstruction. The proposed method was compared against total variation ( TV ) regularization, BM 3D postprocess method, and filtered back projection ( FBP ). Results In the Catphan phantom study, BM 3D regularization better enhances low‐contrast objects compared with TV regularization and BM 3D postprocess method at the same noise level. The spatial resolution using BM 3D regularization is 2.79 and 2.55 times higher than that using the TV regularization at 50% of the modulation transfer function ( MTF ) magnitude, for the fully sampled reconstruction and down‐sampled reconstruction, respectively. The BM 3D regularization images show better bony details and low‐contrast soft tissues, on the head and neck (H&N) and prostate patient images. Conclusions The proposed iterative BM 3D regularization CT reconstruction method takes advantage of both the BM 3D denoising capability and iterative reconstruction data fidelity consistency. This novel approach is superior to TV regularized iterative reconstruction or BM 3D postprocess for improving noisy MVCT image quality.},
doi = {10.1002/mp.12916},
journal = {Medical Physics},
number = 6,
volume = 45,
place = {United States},
year = {Mon Apr 30 00:00:00 EDT 2018},
month = {Mon Apr 30 00:00:00 EDT 2018}
}
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https://doi.org/10.1002/mp.12916
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Works referenced in this record:

Low-dose CT reconstruction via edge-preserving total variation regularization
journal, August 2011

Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT
journal, October 2010

Accurate image reconstruction from few-view and limited-angle data in diffraction tomography
journal, January 2008

  • LaRoque, Samuel J.; Sidky, Emil Y.; Pan, Xiaochuan
  • Journal of the Optical Society of America A, Vol. 25, Issue 7
  • DOI: 10.1364/JOSAA.25.001772

Denoised and texture enhanced MVCT to improve soft tissue conspicuity: Denoised and texture enhanced MVCT
journal, October 2014

  • Sheng, Ke; Gou, Shuiping; Wu, Jiaolong
  • Medical Physics, Vol. 41, Issue 10
  • DOI: 10.1118/1.4894714

Low-dose megavoltage cone-beam CT for radiation therapy
journal, February 2005

  • Pouliot, Jean; Bani-Hashemi, Ali
  • International Journal of Radiation Oncology*Biology*Physics, Vol. 61, Issue 2
  • DOI: 10.1016/j.ijrobp.2004.10.011

Megavoltage CT imaging quality improvement on TomoTherapy via tensor framelet: TomoTherapy via tensor framelet
journal, July 2013

  • Gao, Hao; Qi, X. Sharon; Gao, Yu
  • Medical Physics, Vol. 40, Issue 8
  • DOI: 10.1118/1.4816303

Projection data restoration guided non-local means for low-dose computed tomography reconstruction
conference, March 2011

  • Huang, Jing; Ma, Jianhua; Liu, Nan
  • 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011), 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
  • DOI: 10.1109/ISBI.2011.5872609

BM3D Frames and Variational Image Deblurring
journal, April 2012

  • Danielyan, A.; Katkovnik, V.; Egiazarian, K.
  • IEEE Transactions on Image Processing, Vol. 21, Issue 4
  • DOI: 10.1109/TIP.2011.2176954

Fast, low-dose patient localization on TomoTherapy via topogram registration: Topogram localization on TomoTherapy
journal, July 2010

  • Moore, Kevin L.; Palaniswaamy, Geethpriya; White, Benjamin
  • Medical Physics, Vol. 37, Issue 8
  • DOI: 10.1118/1.3453577

An Analysis and Implementation of the BM3D Image Denoising Method
journal, January 2012

Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm
conference, March 2013

  • Kang, Dongwoo; Slomka, Piotr; Nakazato, Ryo
  • SPIE Medical Imaging, SPIE Proceedings
  • DOI: 10.1117/12.2006907

Is tomotherapy the future of IMRT?
journal, April 2004

Decoupled inverse and denoising for image deblurring: Variational BM3D-frame technique
conference, September 2011

  • Katkovnik, Vladimir; Danielyan, Aram; Egiazarian, Karen
  • 2011 18th IEEE International Conference on Image Processing (ICIP 2011)
  • DOI: 10.1109/ICIP.2011.6116455

Towards task-based assessment of CT performance: System and object MTF across different reconstruction algorithms: Towards task-based assessment of CT performance
journal, June 2012

  • Richard, Samuel; Husarik, Daniela B.; Yadava, Girijesh
  • Medical Physics, Vol. 39, Issue 7Part1
  • DOI: 10.1118/1.4725171

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
journal, August 2007

  • Dabov, Kostadin; Foi, Alessandro; Katkovnik, Vladimir
  • IEEE Transactions on Image Processing, Vol. 16, Issue 8
  • DOI: 10.1109/TIP.2007.901238

Dose reduction for kilovotage cone-beam computed tomography in radiation therapy
journal, May 2008

A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
journal, December 2010

  • Chambolle, Antonin; Pock, Thomas
  • Journal of Mathematical Imaging and Vision, Vol. 40, Issue 1
  • DOI: 10.1007/s10851-010-0251-1
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