Knowledge-based segmentation of attenuation-relevant regions of the head in T1-weighted MR images for attenuation correction in MR/PET systems

Wagenknecht, Gudrun; Kops, Elena Rota; Tellmann, Lutz; Herzog, Hans

doi:10.1109/nssmic.2009.5401751

Cited by 23 publications

(17 citation statements)

References 6 publications

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“…The most obvious approach is to attempt to translate from MRI to attenuation via tissue segmentation (3)(4)(5)(6)(7)(8). The greatest difficulty in tissue classification of MRI for AC is that for conventional MRI sequences bone and air appear similar, but bone has the highest attenuation coefficient of any tissue class, whereas air induces negligible photon attenuation.…”

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confidence: 99%

Attenuation Correction Methods Suitable for Brain Imaging with a PET/MRI Scanner: A Comparison of Tissue Atlas and Template Attenuation Map Approaches

et al. 2011

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Modeled attenuation correction (AC) will be necessary for combined PET/MRI scanners not equipped with transmission scanning hardware. We compared 2 modeled AC approaches that use nonrigid registration with rotating 68 Ge rod-based measured AC for 10 subjects scanned with 18 F-FDG. Methods: Two MRI and attenuation map pairs were evaluated: tissue atlas-based and measured templates. The tissue atlas approach used a composite of the BrainWeb and Zubal digital phantoms, whereas the measured templates were produced by averaging spatially normalized measured MR image and coregistered attenuation maps. The composite digital phantom was manually edited to include 2 additional tissue classes (paranasal sinuses, and ethmoidal air cells or nasal cavity). In addition, 3 attenuation values for bone were compared. The MRI and attenuation map pairs were used to generate subject-specific attenuation maps via nonrigid registration of the MRI to the MR image of the subject. SPM2 and a B-spline free-form deformation algorithm were used for the nonrigid registration. To determine the accuracy of the modeled AC approaches, radioactivity concentration was assessed on a voxelwise and regional basis. Results: The template approach produced better spatial consistency than the phantom-based atlas, with an average percentage error in radioactivity concentration across the regions, compared with measured AC, of 21.2% 6 1.2% and 21.5% 6 1.9% for B-spline and SPM2 registration, respectively. In comparison, the tissue atlas method with B-spline registration produced average percentage errors of 0.0% 6 3.0%, 0.9% 6 2.9%, and 2.9% 6 2.8% for bone attenuation values of 0.143 cm 21 , 0.152 cm 21 , and 0.172 cm 21 , respectively. The largest errors for the template AC method were found in parts of the frontal cortex (23%) and the cerebellar vermis (25%). Intersubject variability was higher with SPM2 than with B-spline. Compared with measured AC, template AC with B-spline and SPM2 achieved a correlation coefficient (R 2 ) of 0.99 and 0.98, respectively, for regional radioactivity concentration. The corresponding R 2 for the tissue atlas approach with B-spline registration was 0.98, irrespective of the bone attenuation coefficient. Conclusion: Nonrigid registration of joint MRI and attenuation map templates can produce accurate AC for brain PET scans, particularly with measured templates and B-spline registration. Consequently, these methods are suitable for AC of brain scans acquired on combined PET/ MRI systems. At tenuation correction (AC) is a vital step in the determination of quantitatively accurate PET images (1). Furthermore, the most commonly used scatter-correction technique (2) also relies on accurate attenuation information. With the advent of scanners that combine PET with MRI (3) and incorporate neither rotating radioactive sources nor CT, a new solution must be found for determining photon attenuation.It is more challenging to estimate attenuation from MRI than CT because the contrast mechanism is unrelated to photon attenuation. The ...

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Attenuation Correction Methods Suitable for Brain Imaging with a PET/MRI Scanner: A Comparison of Tissue Atlas and Template Attenuation Map Approaches

et al. 2011

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“…For registrationbased methods, the attenuation map is obtained by register the individual MR image to a representative CT [3] or another MR that has corresponding attenuation map [4]. For segmentation-based methods, the major task is to label MR images into different classes, e.g., air, soft-tissues, bone, such that specific attenuation coefficients can be applied on different regions [5]. It is worth noting that all these methods exploit the anatomical information provided by structural MR images, which is not available in our study.…”

Section: No Ct => Less Radiation => Frequently Monitor Dementia Patiementioning

confidence: 99%

Brain PET Attenuation Correction without CT: An Investigation

Dewan¹,

Zhan²,

Hermosillo³

et al. 2013

2013 International Workshop on Pattern Recognition in Neuroimaging

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1 In the last decade, Brain PET Imaging has taken big strides in becoming an effective diagnostic tool for dementia and epilepsy disorders, particularly Alzheimer's. CT is often used to provide information for PET attenuation correction. However, for dementia patients, which often require multiple follow-ups, the elimination of CT is desirable to reduce the radiation dose. In this paper, we present a robust algorithm for PET attenuation correction without CT. The algorithm involves building a database of non-attenuation corrected (NAC) PET and CT pairs (model scans). Given a new patient's NAC PET, a learning-based algorithm is used to detect key landmarks, which are then used to select the most similar model scans. Deformable registration is then employed to warp the model CTs to the subject space, followed by a fusion step to obtain the virtual CT for attenuation correction. Besides comparing the normalized AC values with ground truth, we also use a diagnostic tool to evaluate the solution. In addition, a diagnostic evaluation is conducted by a trained nuclear medicine physician, all with promising results.

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“…12 In segmentation-based methods, each voxel is classified as a particular tissue type and subsequently assigned an appropriate attenuation coefficient based on typical values for that tissue. [13][14][15][16][17][18] Other recent methods have followed a similar approach to segmentation-based methods, but with the signal intensities from the MR images being used to assign continuous valued attenuation coefficients to each voxel, 8,19,20 rather than classifying each voxel and then assigning discrete values. Several methods also incorporate additional information such as the location of the voxel or the MR image intensities in neighboring voxels when assigning attenuation coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods also incorporate additional information such as the location of the voxel or the MR image intensities in neighboring voxels when assigning attenuation coefficients. 8,16,17,20 A combination of registration-based and segmentation-based methods can also be used. 8 Accurately assigning attenuation coefficients to bone using MR images is challenging because cortical bone exhibits low proton densities and very fast transverse relaxation rates (T * 2 ), causing it to appear with low intensities with standard MRI pulse sequences, which do not begin sampling until the signal has decayed substantially.…”

Section: Introductionmentioning

confidence: 99%

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

et al. 2013

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Knowledge-based segmentation of attenuation-relevant regions of the head in T1-weighted MR images for attenuation correction in MR/PET systems

Cited by 23 publications

References 6 publications

Attenuation Correction Methods Suitable for Brain Imaging with a PET/MRI Scanner: A Comparison of Tissue Atlas and Template Attenuation Map Approaches

Attenuation Correction Methods Suitable for Brain Imaging with a PET/MRI Scanner: A Comparison of Tissue Atlas and Template Attenuation Map Approaches

Brain PET Attenuation Correction without CT: An Investigation

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

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