An improved MR sequence for attenuation correction in PET/MR hybrid imaging

Sagiyama, Koji; Watanabe, Yuji; Kamei, Ryotaro; Shinyama, Daiki; Baba, Shingo; Honda, Hiroshi

doi:10.1016/j.mri.2015.10.037

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Although the implementation details differ between the various studies, an improved tissue class identification is reported when compared to either the Dixon, UTE or the ZTE alone in the brain [ 81 , 85 ] the thorax [ 83 ] or the pelvis [ 82 ]. Alternatively, a few studies suggest an improvement of tissue classification by extracting information regarding the tissue properties from conventional anatomical sequences such as T 1 images [ 39 , 86 ], T 1 and T 2 maps [ 87 ], combinations of multiple turbo field-echo sequences [ 88 ] or a 31 P-MRI image to utilise the signal from the phosphorus atoms present in the bone crystals [ 89 ]. A rigorous assessment on clinical PET data still needs to be performed for those methods, while the main limitation is the long acquisition time needed for all required sequences to be acquired.…”

Section: Mr-based Attenuation Correctionmentioning

confidence: 99%

A review of PET attenuation correction methods for PET-MR

Krokos,

MacKewn,

Dunn

et al. 2023

EJNMMI Phys

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Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

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Section: Mr-based Attenuation Correctionmentioning

confidence: 99%

A review of PET attenuation correction methods for PET-MR

Krokos,

MacKewn,

Dunn

et al. 2023

EJNMMI Phys

View full text Add to dashboard Cite

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Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results

et al. 2017

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• PET/MR allows voxel-based comparison of SUVs and ADCs in soft-tissue tumours. • A comprehensive assessment of internal heterogeneity was performed with scatter plots. • SUVmax or ADCminimum could not differentiate high-grade sarcoma from low/intermediate-grade tumours. • Only the correlation coefficient between SUV and ADC differentiated the two groups. • The correlation coefficient showed the best diagnostic performance by ROC analysis.

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An improved MR sequence for attenuation correction in PET/MR hybrid imaging

Cited by 2 publications

References 20 publications

A review of PET attenuation correction methods for PET-MR

A review of PET attenuation correction methods for PET-MR

Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results

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