| Low-dose computed tomography image restoration using previous normal-dose scan. | |
| | |
MedLine Citation:
|
PMID: 21992386 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
PURPOSE: In current computed tomography (CT) examinations, the associated x-ray radiation dose is of a significant concern to patients and operators. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) or kVp parameter (or delivering less x-ray energy to the body) as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and the noise would propagate into the CT image if no adequate noise control is applied during image reconstruction. Since a normal-dose high diagnostic CT image scanned previously may be available in some clinical applications, such as CT perfusion imaging and CT angiography (CTA), this paper presents an innovative way to utilize the normal-dose scan as a priori information to induce signal restoration of the current low-dose CT image series. METHODS: Unlike conventional local operations on neighboring image voxels, nonlocal means (NLM) algorithm utilizes the redundancy of information across the whole image. This paper adapts the NLM to utilize the redundancy of information in the previous normal-dose scan and further exploits ways to optimize the nonlocal weights for low-dose image restoration in the NLM framework. The resulting algorithm is called the previous normal-dose scan induced nonlocal means (ndiNLM). Because of the optimized nature of nonlocal weights calculation, the ndiNLM algorithm does not depend heavily on image registration between the current low-dose and the previous normal-dose CT scans. Furthermore, the smoothing parameter involved in the ndiNLM algorithm can be adaptively estimated based on the image noise relationship between the current low-dose and the previous normal-dose scanning protocols. RESULTS: Qualitative and quantitative evaluations were carried out on a physical phantom as well as clinical abdominal and brain perfusion CT scans in terms of accuracy and resolution properties. The gain by the use of the previous normal-dose scan via the presented ndiNLM algorithm is noticeable as compared to a similar approach without using the previous normal-dose scan. CONCLUSIONS: For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure. The authors can draw the conclusion that the presented ndiNLM algorithm may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc. |
| | |
Authors:
|
Jianhua Ma; Jing Huang; Qianjin Feng; Hua Zhang; Hongbing Lu; Zhengrong Liang; Wufan Chen |
Related Documents
:
|
19687736 - A prospective diagnostic accuracy study of 18f-fluorodeoxyglucose positron emission tom... 21042866 - Comparison of (11)c-methionine pet and (18)f-fdg pet in patients with primary central n... 7698156 - Relation between myocardial uptake of thallium-201 chloride and fluorine-18 fluorodeoxy... 8006366 - Neuroimaging in infantile autism. 16575556 - Anatomical study of the confluence of the sinuses with contrast-enhanced magnetic reson... 21699616 - Use of cone-beam computed tomography to characterize daily urinary bladder variations d... |
Publication Detail:
|
Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't |
Journal Detail:
|
Title: Medical physics Volume: 38 ISSN: 0094-2405 ISO Abbreviation: Med Phys Publication Date: 2011 Oct |
Date Detail:
|
Created Date: 2011-10-13 Completed Date: 2011-12-13 Revised Date: 2012-03-19 |
Medline Journal Info:
|
Nlm Unique ID: 0425746 Medline TA: Med Phys Country: United States |
Other Details:
|
Languages: eng Pagination: 5713-31 Citation Subset: IM |
Affiliation:
|
Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. jerome@mil.sunysb.edu |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Algorithms Angiography / methods* Brain Neoplasms / pathology Humans Models, Statistical Neoplasms / pathology, radiography Perfusion Phantoms, Imaging Radiographic Image Interpretation, Computer-Assisted / methods* Radiotherapy / methods Reproducibility of Results Tomography, X-Ray Computed / methods* |
| Grant Support | |
ID/Acronym/Agency:
|
CA082402/CA/NCI NIH HHS; CA143111/CA/NCI NIH HHS; R01 CA082402-08A2/CA/NCI NIH HHS; R01 CA082402-09/CA/NCI NIH HHS; R01 CA082402-10/CA/NCI NIH HHS; R01 CA143111-01A1/CA/NCI NIH HHS; R01 CA143111-02/CA/NCI NIH HHS; R01 CA143111-03/CA/NCI NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Image quality of microcalcifications in digital breast tomosynthesis: effects of projection-view dis...
Next Document: Generating lung tumor internal target volumes from 4D-PET maximum intensity projections.