Common carotid segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method

Background: Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with 18F-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\ua0SUVtotal). 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\ua0and Interobserver variability with manual segmentation was examined in 25 randomly selected scans. Results: Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 \ub1 2.06, −0.01 \ub1 0.05, 0.09 \ub1 0.48, and 1.18 \ub1 1.99 in the left and 1.89 \ub1 1.5, −0.07 \ub1 0.12, 0.05 \ub1 0.47, and 1.61 \ub1 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

Катетерный тромболизис в лечении острого венозного илиофеморального тромбоза в Дании

ТРОМБОЛИТИЧЕСКАЯ ТЕРАПИЯКАТЕТЕРИЗАЦИЯТРОМБОЗ ВЕНОЗНЫЙ /ЛЕК ТЕРКОНЕЧНОСТЬ НИЖНЯ

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

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 18F-fluorodeoxyglucose (FDG) and 18F-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 \ub1 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 \ub1 0.34 vs. 0.69 \ub1 0.15). All NaF uptake values were higher in cases, including higher SUVmax in the intervertebral discs (11.63 \ub1 3.29 vs. 9.45 \ub1 1.32) and facet joints (14.98 \ub1 6.55 vs. 10.60 \ub1 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

Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival

Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-na\uefve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2\ub765, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by S\uf8rensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0\ub778 and 0\ub779, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P\ua0=\ua00\ub702), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival

Germansk dyrestil (Salins stil I–III): et historisk perspektiv

A c. 1000-word abstract in English is already published at pp. 301-2 in the same volume of Hikuin. This is included as a pdf on the relevant CD