Adaptive patch-based POCS approach for super resolution reconstruction of 4D-CT lung data

被引：10

作者：

Wang, Tingting ^{[1
]}

Cao, Lei ^{[1
]}

Yang, Wei ^{[1
]}

Feng, Qianjin ^{[1
]}

Chen, Wufan ^{[1
]}

Zhang, Yu ^{[1
]}

机构：

[1] Southern Med Univ, Sch Biomed Engn, Guangzhou 510515, Guangdong, Peoples R China

来源：

PHYSICS IN MEDICINE AND BIOLOGY | 2015年 / 60卷 / 15期

基金：

中国国家自然科学基金;

关键词：

patch-based; adaptive selection; super resolution; IMAGE-RECONSTRUCTION; SUPERRESOLUTION IMAGE; SPARSE REPRESENTATION; REGISTRATION; MOTION; VIDEO; MRI; TIME;

D O I：

10.1088/0031-9155/60/15/5939

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Image enhancement of lung four-dimensional computed tomography (4D-CT) data is highly important because image resolution remains a crucial point in lung cancer radiotherapy. In this paper, we proposed a method for lung 4D-CT super resolution (SR) by using an adaptive-patch-based projection onto convex sets (POCS) approach, which is in contrast with the global POCS SR algorithm, to recover fine details with lesser artifacts in images. The main contribution of this patch-based approach is that the interfering local structure from other phases can be rejected by employing a similar patch adaptive selection strategy. The effectiveness of our approach is demonstrated through experiments on simulated images and real lung 4D-CT datasets. A comparison with previously published SR reconstruction methods highlights the favorable characteristics of the proposed method.

引用

页码：5939 / 5954

页数：16

共 50 条

[41] Breathing motion robustness of 4D-CT and ITV based treatment plans in lung cancer IMPT
Pastor-Serrano, O.
Lathouwers, D.
Habraken, S.
Hoogeman, M.
Perko, Z.
RADIOTHERAPY AND ONCOLOGY, 2020, 152 : S158 - S159
[42] Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations
Flampouri, Stella
Jiang, Steve B.
Sharp, Greg C.
Wolfgang, John
Patel, Abhijit A.
Choi, Noah C.
PHYSICS IN MEDICINE AND BIOLOGY, 2006, 51 (11): : 2763 - 2779
[43] Attenuation and Motion Correction of 4D-PET Images Using Simultaneous Motion Estimation and Image Reconstruction (SMEIR) of 4D-CT Data
Kalantari, F.
Zhong, Y.
Wang, J.
MEDICAL PHYSICS, 2017, 44 (06) : 3250 - 3250
[44] Tumor Segmentation for Lung 4D-CT Data Using Graph Cuts with Inter-Phase Shape Prior
Gao, YuanYuan
Shen, Zhengwen
Zhang, Yu
Chen, Wufan
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2016, 6 (03) : 634 - 639
[45] Investigation on super-resolution reconstruction of lung CT images for COVID-19 based on sequential images
Zhang, Fengjun
Gong, Le
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2025, 102
[46] Four-Dimensional Computerized Tomography (4D-CT) Reconstruction Based on the Similarity Measure of Spatial Adjacent Images
ZHANG Shu-xu1
ChineseJournalofBiomedicalEngineering, 2008, (03) : 106 - 113
[47] Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction
Wu, Guorong
Wang, Qian
Lian, Jun
Shen, Dinggang
MEDICAL PHYSICS, 2013, 40 (03)
[48] Evaluation and Comparison of Force Terms for the Estimation of Lung Motion by Non-linear Registration of 4D-CT Image Data
Schmidt-Richberg, A.
Ehrhardt, J.
Werner, R.
Handels, H.
WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, VOL 25, PT 4: IMAGE PROCESSING, BIOSIGNAL PROCESSING, MODELLING AND SIMULATION, BIOMECHANICS, 2010, 25 : 2128 - 2131
[49] An unsupervised multi-scale framework with attention-based network (MANet) for lung 4D-CT registration
Yang, Juan
Yang, Jinhui
Zhao, Fen
Zhang, Wenjun
PHYSICS IN MEDICINE AND BIOLOGY, 2021, 66 (13):
[50] Comparison of the radiotherapy plan with the image data approach of a conventional and 4D-CT for movement target volume:: A phantom study
Block, A.
Noertersheuser, T.
Waletzko, O.
Rohn, R.
Mathias, K.
STRAHLENTHERAPIE UND ONKOLOGIE, 2008, 184 : 41 - 41

← 1 2 3 4 5 →