4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling

Zichun Zhong, Xuejun Gu, Weihua Mao, Jing Wang

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

Original languageEnglish (US)
Pages (from-to)996-1020
Number of pages25
JournalPhysics in Medicine and Biology
Volume61
Issue number3
DOIs
StatePublished - Jan 13 2016

Fingerprint

Computer-Assisted Image Processing
Cone-Beam Computed Tomography
Lung Neoplasms
Respiration
Neoplasms

Keywords

  • 4D-CBCT
  • GPU
  • multi-organ meshing model
  • simultaneous motion estimation and image reconstruction (SMEIR)
  • sliding motion

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling. / Zhong, Zichun; Gu, Xuejun; Mao, Weihua; Wang, Jing.

In: Physics in Medicine and Biology, Vol. 61, No. 3, 13.01.2016, p. 996-1020.

Research output: Contribution to journalArticle

@article{5df738d7bf2d4b0b8252b6058f435553,
title = "4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling",
abstract = "A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.",
keywords = "4D-CBCT, GPU, multi-organ meshing model, simultaneous motion estimation and image reconstruction (SMEIR), sliding motion",
author = "Zichun Zhong and Xuejun Gu and Weihua Mao and Jing Wang",
year = "2016",
month = "1",
day = "13",
doi = "10.1088/0031-9155/61/3/996",
language = "English (US)",
volume = "61",
pages = "996--1020",
journal = "Physics in Medicine and Biology",
issn = "0031-9155",
publisher = "IOP Publishing Ltd.",
number = "3",

}

TY - JOUR

T1 - 4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling

AU - Zhong, Zichun

AU - Gu, Xuejun

AU - Mao, Weihua

AU - Wang, Jing

PY - 2016/1/13

Y1 - 2016/1/13

N2 - A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

AB - A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

KW - 4D-CBCT

KW - GPU

KW - multi-organ meshing model

KW - simultaneous motion estimation and image reconstruction (SMEIR)

KW - sliding motion

UR - http://www.scopus.com/inward/record.url?scp=84955498238&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84955498238&partnerID=8YFLogxK

U2 - 10.1088/0031-9155/61/3/996

DO - 10.1088/0031-9155/61/3/996

M3 - Article

C2 - 26758496

AN - SCOPUS:84955498238

VL - 61

SP - 996

EP - 1020

JO - Physics in Medicine and Biology

JF - Physics in Medicine and Biology

SN - 0031-9155

IS - 3

ER -