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Deep-learning-based methods of attenuation correction for SPECT and PET

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Journal of Nuclear Cardiology Aims and scope

Abstract

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.

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Figure 1

Adapted with permission from Ref 47

Figure 2

Adapted with permission from Ref. 47

Figure 3

Adapted with permission from Ref. 52

Figure 4

Adapted with permission from Ref. 50

Figure 5

Adapted with permission from Ref. 16

Figure 6

Adapted with permission from Ref. 16

Figure 7

Adapted with permission from Ref. 45

Figure 8

Adapted with permission from Ref. 46. Reproduced by permission of IOP Publishing. All rights reserved

Figure 9

Adapted with permission from Ref. 88.

Figure 10

Reproduced by permission of IOP Publishing. All rights reserved)

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Abbreviations

TOF:

Time of flight

MPI:

Myocardial perfusion imaging

UTE:

Ultra-short echo time

ZTE:

Zero echo time

AC:

Attenuation correction

ASC:

Attenuation- and scatter-correction

NAC:

Non-attenuation-correction

μ-Map:

Attenuation map

MLAA:

Maximum-likelihood reconstruction of activity and attenuation

FOV:

Field of view

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Disclosures

Xiongchao Chen and Chi Liu are named inventors on non-provisional patent applications that Yale University has filed on SPECT attenuation correction. No other potential conflicts of interest relevant to this article exist.

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  1. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

    Xiongchao Chen MS & Chi Liu PhD

  2. Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT, 06520, USA

    Chi Liu PhD

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This work is supported by NIH Grants R01HL154345.

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Chen, X., Liu, C. Deep-learning-based methods of attenuation correction for SPECT and PET. J. Nucl. Cardiol. 30, 1859–1878 (2023). https://doi.org/10.1007/s12350-022-03007-3

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