Proof‐of‐concept prototype time‐of‐flight <scp>PET</scp> system based on high‐quantum‐efficiency multianode <scp>PMT</scp>s

Son, Jun Hyuk; Kim, Kyeong Yun; Yoon, Hyun Sun; Won, Jun Yeon; Ko, Gyung Hyuck; Lee, Min Sun; Lee, Jae Sung

doi:10.1002/mp.12440

Cited by 21 publications

(12 citation statements)

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“…In addition, the TOF PET systems yield fewer artifacts due to the misalignment of emission and transmission data in AC than non-TOF PET systems (Fig. 14) [168,169].…”

Section: Simultaneous Activity and Attenuation Reconstructionmentioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Attenuation correction (AC) is essential for the generation of artifact-free and quantitaAtively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuationcorrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high energy photon attenuation. Deep learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT-and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.

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“…In addition, the TOF PET systems yield fewer artifacts due to the misalignment of emission and transmission data in AC than non-TOF PET systems (Fig. 14) [168,169].…”

Section: Simultaneous Activity and Attenuation Reconstructionmentioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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“…MLAA-generated attenuation maps allow the use of image-domain priors to improve the algorithm accuracy and convergence (19,20). However, mainly because of the insufficient timing resolution of clinical PET systems, the MLAA suffers from slow convergence, high noise levels in attenuation maps, and crosstalk between activity and attenuation distribution (21).…”

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confidence: 99%

Generation of PET Attenuation Map for Whole-Body Time-of-Flight ¹⁸F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps

et al. 2019

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We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18 F-FDG PET/CT scan data of 100 cancer patients (38 men and 62 women; age, 57.3 ± 14.1 y) were retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution (l-MLAA) and μ-map (μ-MLAA). We used 1.3 million patches derived from 60 patients' data for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method (μ-CNN), μ-MLAA, and 4-segment method (μ-segment) were compared with the μ-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the μ-maps, and the SUVs of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between μ-CNN and μ-CT was 0.77, which was significantly higher than that between μ-MLAA and μ-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences in activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511-keV photons than the 4-segment method currently used in whole-body PET/MRI studies. http://jnm.snmjournals.org/content/60/8/1183This article and updated information are available at: http://jnm.snmjournals.org/site/subscriptions/online.xhtml Information about subscriptions to JNM can be found at: http://jnm.snmjournals.org/site/misc/permission.xhtml

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“…Simultaneous reconstruction of activity and attenuation using only emission data is a promising method for the PET attenuation correction augmented by the recent advancement of time-offlight technology (14)(15)(16)(17). Because no anatomic images are necessary for the attenuation correction if the simultaneous reconstruction works properly, it is a potentially significant approach to overcoming the above-mentioned limitations of PET attenuation correction in PET/CT and PET/MRI (18)(19)(20).…”

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Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning

et al. 2018

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Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suffers from several problems, including crosstalk artifacts, slow convergence speed, and noisy attenuation maps (μ-maps). In this work, we developed deep convolutional neural networks (CNNs) to overcome these MLAA limitations, and we verified their feasibility using a clinical brain PET dataset. We applied the proposed method to one of the most challenging PET cases for simultaneous image reconstruction (F-fluorinated--3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl)nortropane [F-FP-CIT] PET scans with highly specific binding to striatum of the brain). Three different CNN architectures (convolutional autoencoder [CAE], Unet, and Hybrid of CAE) were designed and trained to learn a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution and μ-map (μ-MLAA). The PET/CT data of 40 patients with suspected Parkinson disease were used for 5-fold cross-validation. For the training of CNNs, 800,000 transverse PET and CT slices augmented from 32 patient datasets were used. The similarity to μ-CT of the CNN-generated μ-maps (μ-CAE, μ-Unet, and μ-Hybrid) and μ-MLAA was compared using Dice similarity coefficients. In addition, we compared the activity concentration of specific (striatum) and nonspecific (cerebellum and occipital cortex) binding regions and the binding ratios in the striatum in the PET activity images reconstructed using those μ-maps. The CNNs generated less noisy and more uniform μ-maps than the original μ-MLAA. Moreover, the air cavities and bones were better resolved in the proposed CNN outputs. In addition, the proposed deep learning approach was useful for mitigating the crosstalk problem in the MLAA reconstruction. The Hybrid network of CAE and Unet yielded the most similar μ-maps to μ-CT (Dice similarity coefficient in the whole head = 0.79 in the bone and 0.72 in air cavities), resulting in only about a 5% error in activity and binding ratio quantification. The proposed deep learning approach is promising for accurate attenuation correction of activity distribution in time-of-flight PET systems.

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Proof‐of‐concept prototype time‐of‐flight PET system based on high‐quantum‐efficiency multianode PMTs

Cited by 21 publications

References 50 publications

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Generation of PET Attenuation Map for Whole-Body Time-of-Flight ¹⁸F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning

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Proof‐of‐concept prototype time‐of‐flight PET system based on high‐quantum‐efficiency multianode PMTs

Cited by 21 publications

References 50 publications

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning

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Generation of PET Attenuation Map for Whole-Body Time-of-Flight ¹⁸F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps