| Title: |
Clinical and Deep-Learned Evaluation of MR-Guided Self-Supervised PET Reconstruction |
| Authors: |
Hopson, Jessica B.; Ellis, Sam; Flaus, Anthime; McGinnity, Colm J.; Neji, Radhouene; Reader, Andrew J.; Hammers, Alexander |
| Source: |
Hopson, J B, Ellis, S, Flaus, A, McGinnity, C J, Neji, R, Reader, A J & Hammers, A 2025, 'Clinical and Deep-Learned Evaluation of MR-Guided Self-Supervised PET Reconstruction', IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 9, no. 3, pp. 337-346. https://doi.org/10.1109/TRPMS.2024.3496779 |
| Publication Year: |
2025 |
| Collection: |
King's College, London: Research Portal |
| Subject Terms: |
Image quality assessment; image reconstruction; PET—magnetic resonance (PET-MR) imaging; positron emission tomography (PET) imaging; self-supervision |
| Description: |
Reduced dose Positron Emission Tomography (PET) lowers the radiation dose to patients and reduces costs. Lower count data, however, degrades reconstructed image quality. Advanced reconstruction methods help mitigate image quality losses, but it is important to assess the resulting images from a clinical perspective. Two experienced clinicians assessed four PET reconstruction algorithms for [18F]FDG brain data, compared to a clinical standard reference (Maximum-Likelihood ExpectationMaximization (MLEM)), based on seven clinical image quality metrics: global quality rating, pattern recognition, diagnostic confidence (all on a scale of 0-4), sharpness, caudate-putamen separation, noise, and contrast (on a scale between 0-2). The reconstruction methods assessed were a guided and unguided version of self-supervised maximum a posteriori EM (MAPEM) (where the guidance case used the patient’s MR image to control the smoothness penalty). For 3 of the 11 patient datasets reconstructed, post-smoothed versions of the MAPEM reconstruction were also considered, where the smoothing was with the point-spread-function used in the resolution modelling. Statistically significant improvements were observed in sharpness, caudate-putamen separation, and contrast for self-supervised MR-guided MAPEM compared to MLEM. For example, MLEM scored between 1-1.1 out of 2 for sharpness, caudate-putamen separation and contrast, whereas self-supervised MR-guided MAPEM scored between 1.5-1.75. In addition to the clinical evaluation, pre-trained Convolutional Neural Networks (CNNs) were used to assess the image quality of a further 62 images. The CNNs demonstrated similar trends to the clinician, showing their potential as automated standalone observers. Both the clinical and CNN assessments suggest when using only 5% of the standard injected dose, self-supervised MR-guided MAPEM reconstruction matches the 100% MLEM case for overall performance. This makes the images far more clinically useful than standard MLEM. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| DOI: |
10.1109/TRPMS.2024.3496779 |
| Availability: |
https://kclpure.kcl.ac.uk/portal/en/publications/9895c887-c965-4e5f-a7c1-639ba2519a3b; https://doi.org/10.1109/TRPMS.2024.3496779; https://kclpure.kcl.ac.uk/ws/files/310897885/JH_IEEE_TRPMS_2023-100_FINALPDF.pdf; https://www.scopus.com/pages/publications/105000953319 |
| Rights: |
info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.C16651DC |
| Database: |
BASE |