Your browser doesn't support javascript.
loading
Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation.
Cui, Junlong; Hou, Yong; Jiang, Zekun; Yu, Gang; Ye, Lan; Cao, Qiang; Sun, Qian.
Afiliación
  • Cui J; Department of Cancer Center, The Second Hospital of Shandong University, Jinan, Shandong Province, China.
  • Hou Y; Department of Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong Province, China.
  • Jiang Z; Department of Radiation Oncology, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong Province, China.
  • Yu G; Department of College of Computer Science, Sichuan University, Chengdu, Sichuan Province, China.
  • Ye L; Department of West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
  • Cao Q; Department of Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong Province, China.
  • Sun Q; Department of Cancer Center, The Second Hospital of Shandong University, Jinan, Shandong Province, China.
J Cancer Res Ther ; 20(2): 615-624, 2024 Apr 01.
Article en En | MEDLINE | ID: mdl-38687932
ABSTRACT

AIM:

The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image. MATERIALS AND

METHODS:

It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions.

RESULTS:

This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves.

CONCLUSION:

In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Imagen Asistido por Computador / Fantasmas de Imagen / Tomografía Computarizada de Haz Cónico Límite: Humans Idioma: En Revista: J Cancer Res Ther Asunto de la revista: NEOPLASIAS / TERAPEUTICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: India
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Imagen Asistido por Computador / Fantasmas de Imagen / Tomografía Computarizada de Haz Cónico Límite: Humans Idioma: En Revista: J Cancer Res Ther Asunto de la revista: NEOPLASIAS / TERAPEUTICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: India