MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Mecheter, Imene; Alic, Lejla; Abbod, Maysam F.; Amira, Abbes; Ji, Jim

doi:10.1007/s10278-020-00361-x

Cited by 21 publications

(12 citation statements)

References 113 publications

(190 reference statements)

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“…For hybrid PET/MR systems, attenuation corrections (AC) need to be derived using either a dedicated MR sequence (MR-AC) or use of CT templates depending on the system's available methods. In most of the systems, only MR-AC or CT templates are commercially available now, but improved and more advanced methods using special image reconstruction algorithms and artificial intelligence [114][115][116][117][118] are expected to be offered in future software updates. For MR-AC imaging, the body coil or a dedicated brain/head coil may be required.…”

Section: Attenuation Correctionmentioning

confidence: 99%

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

Guedj

Varrone

Boellaard

et al. 2021

Eur J Nucl Med Mol Imaging

134

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The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [18F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [18F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [18F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.

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Section: Attenuation Correctionmentioning

confidence: 99%

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

Guedj

Varrone

Boellaard

et al. 2021

Eur J Nucl Med Mol Imaging

134

View full text Add to dashboard Cite

show abstract

“…In the new PET/MRI hybrid scanners, this step is not immediate, and MRI‐to‐sCT translation has been proposed to solve the MR attenuation correction (MRAC) issue. Besides, stand‐alone PET scanners can benefit from the derivation of sCT from uncorrected PET 32–34 …”

Section: Introductionmentioning

confidence: 99%

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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“…Besides, standalone PET scanner can benefit from the derivation of sCT from uncorrected PET. 31,32,33 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

Spadea,

Maspero,

Zaffino

et al. 2021

Preprint

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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 key characteristics of the DL methods 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 also 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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MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Cited by 21 publications

References 113 publications

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

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

Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

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