2009
DOI: 10.2967/jnumed.108.054726
|View full text |Cite
|
Sign up to set email alerts
|

Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data

Abstract: Attenuation correction (AC) of whole-body PET data in combined PET/MRI tomographs is expected to be a technical challenge. In this study, a potential solution based on a segmented attenuation map is proposed and evaluated in clinical PET/CT cases. Methods: Segmentation of the attenuation map into 4 classes (background, lungs, fat, and soft tissue) was hypothesized to be sufficient for AC purposes. The segmentation was applied to CT-based attenuation maps from 18 F-FDG PET/CT oncologic examinations of 35 patien… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

13
692
3
6

Year Published

2012
2012
2019
2019

Publication Types

Select...
4
4

Relationship

0
8

Authors

Journals

citations
Cited by 684 publications
(714 citation statements)
references
References 33 publications
13
692
3
6
Order By: Relevance
“…Martinez‐Moller et al (14) proposed using a four‐class segmentation scheme with the Dixon technique, in which fat is separated from nonfat soft tissue and assigned a different attenuation coefficient. Although evaluating different segmentation‐based attenuation correction methods was beyond the scope of our study, it should be noted that the Dixon technique can be integrated into our proposed AMR protocol with a modification of the MR sequence, to separate fat and nonfat soft tissue while maintaining similar temporal resolution.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Martinez‐Moller et al (14) proposed using a four‐class segmentation scheme with the Dixon technique, in which fat is separated from nonfat soft tissue and assigned a different attenuation coefficient. Although evaluating different segmentation‐based attenuation correction methods was beyond the scope of our study, it should be noted that the Dixon technique can be integrated into our proposed AMR protocol with a modification of the MR sequence, to separate fat and nonfat soft tissue while maintaining similar temporal resolution.…”
Section: Discussionmentioning
confidence: 99%
“…This mismatch, which reflects the different breathing states captured by the respective imaging modalities, is largely due to the disparity in modalities' imaging speeds (13) . Unlike PET data, which are averaged over several minutes, each CT slice is captured in less than 1 s. Similarly, in whole‐body PET/MR imaging, MR images for attenuation correction, unlike PET data, are usually acquired using a breath‐hold Dixon sequence, which takes about 18 s for each 21 cm bed position (14) . Examples of respiration associated attenuation artifacts in clinical whole‐body PET/MR have been reported by Keller et al (15) The difference in image acquisition time suggests that artifacts caused by spatial mismatch can also occur in cardiac PET/MR imaging.…”
Section: Introductionmentioning
confidence: 99%
“…and to assign a single value to each class. Using discretized attenuation values for μ-maps instead of a continuous attenuation image was evaluated in [28,29] and the accuracy of reconstructed images is dependent on locations. It was claimed that it is feasible to use discretized μ-maps clinically.…”
Section: Sources Of Attenuation/scatter Informationmentioning
confidence: 99%
“…Using anatomical information, these effects can be compensated during image reconstruction. Attenuation correction methods have been investigated using CT [19,[23][24][25][26] or MR [27][28][29]. Scatter correction methods have also been studied in [30][31][32][33][34].…”
Section: Introductionmentioning
confidence: 99%
“…This sequence, which involves a few seconds of acquisition time, allows the separation of water and fat tissue by using the chemical shift of fat relative to that of water. This information facilitates in turn the segmentation of MR images into four to five different classes (lung, fat tissue, nonfat tissue, mixture of fat/nonfat tissue, air) [6]. It is important to highlight that once segmented, fixed 511 keV linear attenuation coefficients (LACs) are assigned to each of the considered tissue types, largely ignoring tissue heterogeneities.…”
mentioning
confidence: 99%