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- Piri, R., et al.
(författare)
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Common carotid segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method
- 2023
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 43:2, s. 71-77
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Tidskriftsartikel (refereegranskat)abstract
- Background Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with F-18-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience. We compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids. Methods Segmentation in NaF-PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients were compared for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, and SUVtotal). SUVmean was the average of SUVmeans within the VOI, SUVmax the highest SUV in all voxels in the VOI, and SUVtotal the SUVmean multiplied by the Vol of the VOI. Intra and Interobserver variability with manual segmentation was examined in 25 randomly selected scans. Results Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 +/- 2.06, -0.01 +/- 0.05, 0.09 +/- 0.48, and 1.18 +/- 1.99 in the left and 1.89 +/- 1.5, -0.07 +/- 0.12, 0.05 +/- 0.47, and 1.61 +/- 1.47, respectively, in the right common carotid artery. Manual segmentation lasted typically 20 min versus 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14% and 27% in left and right common carotids. Conclusions CNN-based segmentation was much faster and provided SUVmean values virtually identical to manually obtained ones, suggesting CNN-based analysis as a promising substitute of slow and cumbersome manual processing.
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- Piri, R., et al.
(författare)
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"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison
- 2022
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Ingår i: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1071-3581 .- 1532-6551. ; 29:5, s. 2531-2539
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Tidskriftsartikel (refereegranskat)abstract
- Background Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden. Methods A trained convolutional neural network (CNN) was used for cardiac segmentation in F-18-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans. Results Mean (+/- SD) values with manual vs. CNN-based segmentation were Vol 617.65 +/- 154.99 mL vs 625.26 +/- 153.55 mL (P = .21), SUVmean 0.69 +/- 0.15 vs 0.69 +/- 0.15 (P = .26), SUVmax 2.68 +/- 0.86 vs 2.77 +/- 1.05 (P = .34), and SUVtotal 425.51 +/- 138.93 vs 427.91 +/- 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators. Conclusion The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.
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- Piri, R., et al.
(författare)
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PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence-based segmentation
- 2022
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 42:4, s. 225-232
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Tidskriftsartikel (refereegranskat)abstract
- Background Current imaging modalities are often incapable of identifying nociceptive sources of low back pain (LBP). We aimed to characterize these by means of positron emission tomography/computed tomography (PET/CT) of the lumbar spine region applying tracers F-18-fluorodeoxyglucose (FDG) and F-18-sodium fluoride (NaF) targeting inflammation and active microcalcification, respectively. Methods Using artificial intelligence (AI)-based quantification, we compared PET findings in two sex- and age-matched groups, a case group of seven males and five females, mean age 45 +/- 14 years, with ongoing LBP and a similar control group of 12 pain-free individuals. PET/CT scans were segmented into three distinct volumes of interest (VOIs): lumbar vertebral bodies, facet joints and intervertebral discs. Maximum, mean and total standardized uptake values (SUVmax, SUVmean and SUVtotal) for FDG and NaF uptake in the 3 VOIs were measured and compared between groups. Holm-Bonferroni correction was applied to adjust for multiple testing. Results FDG uptake was slightly higher in most locations of the LBP group including higher SUVmean in the intervertebral discs (0.96 +/- 0.34 vs. 0.69 +/- 0.15). All NaF uptake values were higher in cases, including higher SUVmax in the intervertebral discs (11.63 +/- 3.29 vs. 9.45 +/- 1.32) and facet joints (14.98 +/- 6.55 vs. 10.60 +/- 2.97). Conclusion Observed intergroup differences suggest acute inflammation and microcalcification as possible nociceptive causes of LBP. AI-based quantification of relevant lumbar VOIs in PET/CT scans of LBP patients and controls appears to be feasible. These promising, early findings warrant further investigation and confirmation.
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