Effects of injected dose, BMI and scanner type on NECR and image noise in PET imaging

Chang, Tingting; Chang, Guoqiang; Kohlmyer, S.G.; Clark, John W.; Rohren, Eric M.; Mawlawi, Osama

doi:10.1088/0031-9155/56/16/013

Cited by 44 publications

(55 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the slopes in Table III, which are obtained from five clinical images reconstructed with a fixed number of iterations in the OP-OSEM, are slightly different according to the patient. This may be due to differences in the body mass indices (BMI), injected doses, tracer distributions, and so on, among the patients [34]- [36]. If the BMI of a patient increases, the noise will increase [34].…”

Section: Discussionmentioning

confidence: 99%

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

Kim

Ahn

Nam

et al. 2015

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

Positron emission tomography (PET) images usually suffer from a noticeable amount of statistical noise. In order to reduce this noise, a post-filtering process is usually adopted. However, the performance of this approach is limited because the denoising process is mostly performed on the basis of the Gaussian random noise. It has been reported that in a PET image reconstructed by the expectation-maximization (EM), the noise variance of each voxel depends on its mean value, unlike in the case of Gaussian noise. In addition, we observe that the variance also varies with the spatial sensitivity distribution in a PET system, which reflects both the solid angle determined by a given scanner geometry and the attenuation information of a scanned object. Thus, if a post-filtering process based on the Gaussian random noise is applied to PET images without consideration of the noise characteristics along with the spatial sensitivity distribution, the spatially variant non-Gaussian noise cannot be reduced effectively. In the proposed framework, to effectively reduce the noise in PET images reconstructed by the 3-D ordinary Poisson ordered subset EM (3-D OP-OSEM), we first denormalize an image according to the sensitivity of each voxel so that the voxel mean value can represent its statistical properties reliably. Based on our observation that each noisy denormalized voxel has a linear relationship between the mean and variance, we try to convert this non-Gaussian noise image to a Gaussian noise image. We then apply a block matching 4-D algorithm that is optimized for noise reduction of the Gaussian noise image, and reconvert and renormalize the result to obtain a final denoised image. Using simulated phantom data and clinical patient data, we demonstrate that the proposed framework can effectively suppress the noise over the whole region of a PET image while minimizing degradation of the image resolution.Index Terms-Image denoising, noise characteristics conversion, non-Gaussian noise, positron emission tomography (PET), voxel sensitivity.

show abstract

Section: Discussionmentioning

confidence: 99%

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

Kim

Ahn

Nam

et al. 2015

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

show abstract

“…R represents the random coincidences fraction, S* is the scattered coincidence events, T is the true coincidence events, S' is the corrected frequency of scattered coincidences, and R' is the corrected frequency of random coincidences [2]. S' and R' involve only those projections that pass through the body of the patient and are determined, respectively, from multiple random and scattered frequencies.…”

Section: Osem Image Quality Image Noisementioning

confidence: 99%

“…The true coincidence events can be expressed in terms of total, R)*(1-S*) [2]. This work addresses the connections between NECR and image noise, as well as the pitfalls of relying too much on NECR values as an indication of clinical In PET imaging, NECR describes the true coincidence rate that would give the observed signal to noise ratio (SNR), or the same level of statistical noise, if there were no randoms and no scatter [3,4].…”

Section: Image Noisementioning

confidence: 99%

“…One study found similar SNR for a BGO scanner and a LYSO scanner when 3D--OSEM reconstruction was used, even though the scanners have different NECR [2]. No statistically significant difference in the amount of image noise between the two scanners was found even though the difference in the NECR was statistically significant.…”

Section: Image Noisementioning

confidence: 99%

“…This suggests that SNR may not correlate with the NECR when 3D--OSEM reconstruction is used. When comparing BGO and LYSO scanners, a better count rate performance does not translate to a better image quality when using a 3D-OSEM reconstruction algorithm [2].…”

Section: Image Noisementioning

confidence: 99%

See 2 more Smart Citations

Connections Between Noise Equivalent Count Rate and Image Noise in Pet Medical Imaging

Antić¹,

Haglund²

2016

RAD Conference Proceedings

View full text Add to dashboard Cite

Abstract. Assessing PET image quality is a challenge due to its clinical subjectivity and difficulties in standardisation. Methods to evaluate PET image quality include image noise and noise equivalent count rate (NECR), which are automatic and objective measurements determined from the reconstructed medical PET image or PET raw emission data from phantoms. Although manufacturers emphasize scanner performance by stating peak NECR, the peak NECR is often outside clinically limited by radiation safety factors, as opposed to scanner performance. instead, image noise in the expectation maximization (EM) algorithm inferred that this could also be true for three algorithm. One consequence is that for traditionally based on NECR, should be based on the true produced by increasing the injected activity to match the peak true count rate for 3D

show abstract

Molecular imaging of arterial fibroblast activation protein: association with calcified plaque burden and cardiovascular risk factors

Kosmala

Serfling

Michalski

et al. 2023

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

Purpose We aimed to assess prevalence, distribution, and intensity of in-vivo arterial wall fibroblast activation protein (FAP) uptake, and its association with calcified plaque burden, cardiovascular risk factors (CVRFs), and FAP-avid tumor burden. Methods We analyzed 69 oncologic patients who underwent [68 Ga]Ga-FAPI-04 PET/CT. Arterial wall FAP inhibitor (FAPI) uptake in major vessel segments was evaluated. We then investigated the associations of arterial wall uptake with calcified plaque burden (including number of plaques, plaque thickness, and calcification circumference), CVRFs, FAP-positive total tumor burden, and image noise (coefficient of variation, from normal liver parenchyma). Results High focal arterial FAPI uptake (FAPI +) was recorded in 64/69 (92.8%) scans in 800 sites, of which 377 (47.1%) exhibited concordant vessel wall calcification. The number of FAPI + sites per patient and (FAPI +)-derived target-to-background ratio (TBR) correlated significantly with the number of calcified plaques (FAPI + number: r = 0.45, P < 0.01; TBR: r = − 0.26, P = 0.04), calcified plaque thickness (FAPI + number: r = 0.33, P < 0.01; TBR: r = − 0.29, P = 0.02), and calcification circumference (FAPI + number: r = 0.34, P < 0.01; TBR: r = − 0.26, P = 0.04). In univariate analysis, only body mass index was significantly associated with the number of FAPI + sites (OR 1.06; 95% CI, 1.02 − 1.12, P < 0.01). The numbers of FAPI + sites and FAPI + TBR, however, were not associated with other investigated CVRFs in univariate and multivariate regression analyses. Image noise, however, showed significant correlations with FAPI + TBR (r = 0.30) and the number of FAPI + sites (r = 0.28; P = 0.02, respectively). In addition, there was no significant interaction between FAP-positive tumor burden and arterial wall FAPI uptake (P ≥ 0.13). Conclusion [68 Ga]Ga-FAPI-04 PET identifies arterial wall lesions and is linked to marked calcification and overall calcified plaque burden, but is not consistently associated with cardiovascular risk. Apparent wall uptake may be partially explained by image noise.

show abstract

Effects of injected dose, BMI and scanner type on NECR and image noise in PET imaging

Cited by 44 publications

References 13 publications

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics

Connections Between Noise Equivalent Count Rate and Image Noise in Pet Medical Imaging

Molecular imaging of arterial fibroblast activation protein: association with calcified plaque burden and cardiovascular risk factors

Contact Info

Product

Resources

About