2017
DOI: 10.1002/mp.12259
|View full text |Cite
|
Sign up to set email alerts
|

A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC)

Abstract: Purpose Sequential same-patient CT images may involve deformation-induced and non-deformation-induced voxel intensity changes. An adaptive deformation recovery and intensity correction (ADRIC) technique was developed to improve the CT reconstruction accuracy, and to separate deformation from non-deformation-induced voxel intensity changes between sequential CT images. Materials and Methods ADRIC views the new CT volume as a deformation of a prior high-quality CT volume, but with additional non-deformation-in… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
23
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
7

Relationship

2
5

Authors

Journals

citations
Cited by 17 publications
(23 citation statements)
references
References 59 publications
(130 reference statements)
0
23
0
Order By: Relevance
“…7b) shows the similarity between the reconstructed CBCT images by SMEIR-Bio and SMEIR-Unet, demonstrating that both methods were capable of reconstructing accurate and high-quality CBCT images. We also computed two image quality metrics, the RMSE [39] and the UQI [33], to compare SMEIR-Bio and SMEIR-Unet to the original SMEIR algorithm, based on 10 lung region-of-interests (ROIs) focusing on fine details. For SMEIR-Bio, the average RMSE of the 10 evaluated ROIs was 0.0033 and the average UQI was 0.87.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…7b) shows the similarity between the reconstructed CBCT images by SMEIR-Bio and SMEIR-Unet, demonstrating that both methods were capable of reconstructing accurate and high-quality CBCT images. We also computed two image quality metrics, the RMSE [39] and the UQI [33], to compare SMEIR-Bio and SMEIR-Unet to the original SMEIR algorithm, based on 10 lung region-of-interests (ROIs) focusing on fine details. For SMEIR-Bio, the average RMSE of the 10 evaluated ROIs was 0.0033 and the average UQI was 0.87.…”
Section: Resultsmentioning
confidence: 99%
“…The incorporation of highquality prior information also introduces more accurate Hounsfield units [30], and allows further imaging dose reduction by acquiring fewer projections for reconstruction. However, this technique cannot reconstruct non-deformation-induced intensity changes in new CBCT volumes, since the new CBCT is simplified as a purely deformed volume of the prior image [33].…”
Section: Introductionmentioning
confidence: 99%
“…Several groups have utilized VCTs to evaluate their novel CT image reconstruction algorithms. [305][306][307] Abadi et al 141,308 characterized the noise texture across filtered back projection and iterative reconstruction algorithms. In this study, an XCAT phantom 41,55 was imaged 50 times using a validated CT simulator, setup to mimic the parameters and settings of a specific scanner model (Siemens Definition Flash).…”
Section: Ct Imagingmentioning
confidence: 99%
“…The simulation was performed at 80 kV, and the corresponding spectrum ( Fig. 3) was divided into six energy bins: [20,30] keV, [30,40] keV, [40,50] keV, [50,60]…”
Section: Digital Circle Phantom-mentioning
confidence: 99%
“…One straightforward solution is to reduce the number of projections needed to reconstruct the spectral CT images. However, this reduction will cause severe artifacts in the reconstructed images because the FBP algorithm requires the number of projections to satisfy the Shannon sampling theorem [28][29][30][31][32][33][34].…”
Section: Introductionmentioning
confidence: 99%