Fast Energy Dependent Scatter Correction for List-Mode PET Data

Improvements in energy resolution of modern positron emission tomography (PET) detectors have created opportunities to implement energy-based scatter correction algorithms. Here, we use the energy information of auxiliary windows to estimate the scatter component. Our method is directly implemented in an iterative reconstruction algorithm, generating a scatter-corrected image without the need for sinograms. The purpose was to implement a fast energy-based scatter correction method on list-mode PET data, when it was not possible to use an attenuation map as a practical approach for the scatter degradation. The proposed method was evaluated using Monte Carlo simulations of various digital phantoms. It accurately estimated the scatter fraction distribution, and improved the image contrast in the simulated studied cases. We conclude that the proposed scatter correction method could effectively correct the scattered events, including multiple scatters and those originated in sources outside the field of view.

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Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽

10.3390/electronics10151836 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1836

Author(s):

Bo-Hye Choi ◽

Donghwi Hwang ◽

Seung-Kwan Kang ◽

Kyeong-Yun Kim ◽

Hongyoon Choi ◽

...

Keyword(s):

Neural Network ◽

Attenuation Correction ◽

Complex Structure ◽

Scatter Correction ◽

Amyloid Β ◽

Correction Method ◽

Percentage Error ◽

Similarity Coefficients ◽

Brain Pet ◽

Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

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LMIRA: list mode iterative reconstruction algorithm for SPECT

2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149) ◽

10.1109/nssmic.2000.950040 ◽

2002 ◽

Author(s):

L. Bouwens ◽

R. Van de Walle ◽

H. Gifford ◽

M. King ◽

I. Lemahieu ◽

...

Keyword(s):

Iterative Reconstruction ◽

Reconstruction Algorithm ◽

List Mode ◽

Iterative Reconstruction Algorithm

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A Filtered Iterative Reconstruction Algorithm for Positron Emission Tomography

Information Processing in Medical Imaging ◽

10.1007/978-1-4615-7263-3_13 ◽

1988 ◽

pp. 217-233 ◽

Cited By ~ 6

Author(s):

Eiichi Tanaka

Keyword(s):

Positron Emission Tomography ◽

Iterative Reconstruction ◽

Reconstruction Algorithm ◽

Emission Tomography ◽

Iterative Reconstruction Algorithm ◽

Positron Emission

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A novel beam hardening correction method requiring no prior knowledge, incorporated in an iterative reconstruction algorithm

NDT & E International ◽

10.1016/j.ndteint.2012.07.002 ◽

2012 ◽

Vol 51 ◽

pp. 68-73 ◽

Cited By ~ 33

Author(s):

L. Brabant ◽

E. Pauwels ◽

M. Dierick ◽

D. Van Loo ◽

M.A. Boone ◽

...

Keyword(s):

Prior Knowledge ◽

Iterative Reconstruction ◽

Reconstruction Algorithm ◽

Correction Method ◽

Beam Hardening ◽

Iterative Reconstruction Algorithm ◽

Beam Hardening Correction

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LMIRA: list-mode iterative reconstruction algorithm for SPECT

IEEE Transactions on Nuclear Science ◽

10.1109/23.958356 ◽

2001 ◽

Vol 48 (4) ◽

pp. 1364-1370 ◽

Cited By ~ 8

Author(s):

L. Bouwens ◽

R. Van de Walle ◽

H. Gifford ◽

M. King ◽

I. Lemahieu ◽

...

Keyword(s):

Iterative Reconstruction ◽

Reconstruction Algorithm ◽

List Mode ◽

Iterative Reconstruction Algorithm

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Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT

EJNMMI Physics ◽

10.1186/s40658-021-00405-3 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Dennis Kupitz ◽

Heiko Wissel ◽

Jan Wuestemann ◽

Stephanie Bluemel ◽

Maciej Pech ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Quantitative Imaging ◽

Scatter Correction ◽

Reconstruction Algorithm ◽

Weighting Factor ◽

Quantitative Spect ◽

Pet Ct ◽

Spect Acquisition ◽

The Mean ◽

A Minor

Abstract Background The introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol. Results The reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by − 13.9 kBq/ml (− 16.3%) and − 7.1 kBq/ml (− 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = − 14.6 kBq/ml (− 17.1%), 240 iterations (24i/10s) = − 8.0 kBq/ml (− 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001). Conclusion Quantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

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