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

Lee, Jae Sung

doi:10.1109/trpms.2020.3009269

Cited by 76 publications

(43 citation statements)

References 217 publications

(363 reference statements)

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“…Convolutional neural networks (CNNs), which are the most successful type of models for image processing 44,45 , have been proposed for sCT generation since 2016 46 , with a rapidly increasing number of published papers on the topic. However, DL-based sCT generation has not been reviewed in details, except for applications in PET 47 . With this survey, we aim at summarising the latest developments in DL-based sCT generation, highlighting the contributions based on the applications and providing detailed statistics discussing trends in terms of imaging protocols, DL architectures, and performance achieved.…”

Section: Accepted Articlementioning

confidence: 99%

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Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

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Section: Accepted Articlementioning

confidence: 99%

“…Deep learning-based sCT generation in RT and PET August 18, 2021 page 27 these works were not included, but they can be found in a recent review by Lee 47 .…”

Section: Accepted Articlementioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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“…Finally, a 3-D GAN with discriminative and cycle-consistency loss was proposed by Gong et al to derive continuous attenuation correction maps from Dixon MRI images; the technique generated better pseudo-CT images than the segmentation and atlas methods and its performance was comparable to a CNN-based one [ 107 ]. For a detailed overview of DL approaches for attenuation correction in PET, please refer to the following review article [ 108 ].…”

Section: Overview Of Deep Learning Applications In Medical Imagingmentioning

confidence: 99%

Application and Construction of Deep Learning Networks in Medical Imaging

Torres-Velázquez

Chen

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this paper reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.

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“…The simultaneous operation of PET and magnetic resonance imaging (PET/MRI) scanners enables better spatiotemporal correlation between the functional and anatomical data from both imaging modalities [11,12]. In addition, PET/MRI yields less radiation exposure and features a superior soft-tissue contrast than PET/computed tomography (CT), resulting in better diagnostic performance in various applications despite the fact that attenuation correction in PET/MRI is more complex than the PET/CT [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development and Initial Results of a Brain PET Insert for Simultaneous 7-Tesla PET/MRI Using an FPGA-Only Signal Digitization Method

Won

Park

Lee

et al. 2021

IEEE Trans. Med. Imaging

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In this study, we developed a positron emission tomography (PET) insert for simultaneous brain imaging within 7-Tesla (7T) magnetic resonance (MR) imaging scanners. The PET insert has 18 sectors, and each sector is assembled with two-layer depth-of-interaction (DOI)-capable high-resolution block detectors. The PET scanner features a 16.7-cm-long axial field-of-view (FOV) to provide entire human brain images without bed movement. The PET scanner early digitizes a large number of block detector signals at a front-end data acquisition (DAQ) board using a novel field-programmable gate array (FPGA)-only signal digitization method. All the digitized PET data from the front-end DAQ boards are transferred using gigabit transceivers via non-magnetic high-definition multimedia interface (HDMI) cables. A back-end DAQ system provides a common clock and synchronization signal for FPGAs over the HDMI cables. An active cooling system using copper heat pipes is applied for thermal regulation. All the 2.17-mm-pitch crystals with two-layer DOI information were clearly identified in the block detectors, exhibiting a system-level energy resolution of 12.6%. The PET scanner yielded clear hot-rod and Hoffman brain phantom images and demonstrated 3D PET imaging capability without bed movement. We also performed a pilot simultaneous PET/MR imaging study of a brain phantom. The PET scanner achieved a spatial resolution of 2.5 mm at the center FOV (NU 4) and a sensitivity of 18.9 kcps/MBq (NU 2) and 6.19% (NU 4) in accordance with the National Electrical Manufacturers Association (NEMA) standards.

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

Cited by 76 publications

References 217 publications

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Application and Construction of Deep Learning Networks in Medical Imaging

Development and Initial Results of a Brain PET Insert for Simultaneous 7-Tesla PET/MRI Using an FPGA-Only Signal Digitization Method

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