Table 5 Comparison of the four attenuation correction techniques outlined in this review
From: A review of PET attenuation correction methods for PET-MR
| Â | MR-based AC | Emission-based AC | Atlas-based AC | Deep learning-based AC |
|---|---|---|---|---|
Accuracy | Low but somewhat improved when properly incorporating bone tissue [36, 73]. Low accuracy in lung and pelvis [120, 241, 308] | Good accuracy in the brain and tissue lesions. Moderate accuracy in air cavities, bone and lung [36, 142] | Good accuracy in the brain for most methods [36]. Moderate in whole-body (limited number of studies) [6] | |
Artefacts and biases | Truncation artefacts [26]. Motion artefacts in lung and heart [22] Metallic artefacts when implants are present [47]. Workaround techniques to partially alleviate the artefacts [49, 97] | Additive constant [132]. Positive bias on low count data [133]. Crosstalk in non-TOF data [131] | Moderately sensitive to metallic and truncation artefacts [271]. High biases for non-standard anatomies [10, 192]. Separate adult and paediatric databases required [33] | Insensitive to metallic and truncation artefacts [271]. Separate adult and paediatric network training may be required [212]. Insensitive to tracer (depending on the technique) [228, 255] |
Processing time |  ~ 1 h [253] | |||
Provided by the manufacturers | Yes | No. Only for addressing truncation artefacts on the Siemens mMR [158] | One method that requires T1 images is available on the GE SIGNA [170] | No |
User input | Minimal. Only the acquisition of the sequences required | Moderate/High. Increases when coregistration is also required | Moderate/High. Data acquisition, potential data processing and visual inspection | Moderate. After training only the trained weights need to be applied on the new image |
Susceptibility to misregistration | Yes [14] | Not for the original MLAA. Yes for methods requiring anatomical priors or initial μ values [137] | Yes. Most methods require two or more registration steps [43, 60] | Yes, if an anatomical MR image is used as input [247] |
PET dependency | Independent | Not suitable for non-TOF systems and low count datasets [130] | Independent | Independent if anatomical images are used. Could be tracer-dependent if NAC images are used as input [255] |
Applicability to whole-body | Only Dixon-based sequences [67] | Yes [142] | Separate atlases for each region. Not widely used in whole body [186] | |
Requirement for additional data | No | Tissue priors required to tackle the additive constant issue | MR images if they are used as input | MR images if they are used as input |
Additional requirements | None | Coregistration and/or segmentation tools | Large database of paired CT and MR images Coregistration and/or segmentation tools | Large image database for model training on a powerful workstation or a suitable pre-trained model if available |