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Purpose: We present a probabilistic approach to medical image analysis that requires, and makes use of, explicit prior information provided by a medical expert. Depending on the choice of prior model the method can be used for image enhancement, analysis, and segmentation. Methods: The methodology is based on a probabilistic approach to medical image analysis, that allows integration of 1) arbitrarily complex prior information (for which realizations can be generated), 2) information about a convolution operator of the imaging system, and 3) information about the noise in the reconstructed image into a posterior probability density. The method was demonstrated on positron emission tomography (PET) images obtained from a phantom and a patient with lung cancer. The likelihood model (multivariate log-normal) and the convolution operator were derived from phantom data. Two examples of prior information were used to show the potential of the method. The extended Metropolis-Hastings algorithm, a Markov chain Monte Carlo method, was used to generate realizations of the posterior distribution of the tracer activity concentration. Results: A set of realizations from the posterior was used as the base of a quantitative PET image analysis. The mean and variance of activity concentrations were computed, as well as the probability of high tracer uptake and statistics on the size and activity concentration of high uptake regions. For both phantom and in vivo images, the estimated images of mean activity concentrations appeared to have reduced noise levels, and a sharper outline of high activity regions, as compared to the original PET. The estimated variance of activity concentrations was high at the edges of high activity regions. Conclusions: The methodology provides a probabilistic approach for medical image analysis that explicitly takes into account medical expert knowledge as prior information. The presented first results indicate the potential of the method to improve the detection of small lesions. The methodology allows for a probabilistic measure of the size and activity level of high uptake regions, with possible long-term perspectives for early detection of cancer, aswell as treatment, planning, and follow-up. [ABSTRACT FROM AUTHOR]

Introduction: Pediatric patients have an increased risk of radiation-induced malignancies due to their ongoing development and long remaining life span. Thus, optimization of PET protocols is an important task in pediatric nuclear medicine. Long axial field-of-view (LAFOV) PET/CT has shown a significant increase in sensitivity, which provides an ideal opportunity for reduction of injected tracer activity in the pediatric population. In this study we aim to evaluate the clinical performance of a 2-[18F]FDG-tracer reduction from 3 MBq/kg to 1.5 MBq/kg on the Biograph Vision Quadra LAFOV PET/CT. Materials and methods: The first 50 pediatric patients referred for clinical whole-body PET/CT with 1.5 MBq/kg 2-[18F]FDG, were included. A standard pediatric protocol was applied. Five reconstructions were created with various time, filter and iteration settings. Image noise was computed as coefficient-of-variance (COV = SD/mean standardized-uptake-value) calculated from a spherical 20-50 mm (diameter) liver volume-of-interest. Sets of reconstructions were reviewed by one nuclear medicine physicians, who reported image lesions on a pre-defined list of sites. Paired comparison analysis was performed with significance at PB< 0.05 (Bonferroni corrected). Results: All reconstructions, except one, achieved a COVmean (0.08-0.15) equal to or lower than current clinical acceptable values (COVref ≤ 0.15). Image noise significantly improved with increasing acquisition time, lowering iterations (i) from 6i to 4i (both with five subsets) and when applying a 2 mm Gauss filter (PB< 0.001). Significant difference in lesion detection was seen from 150s to 300s and from 150s to 600s (PB = 0.006-0.007). 99% of all lesions rated as malignant could be found on the 150s reconstruction, while 100% was found on the 300s, when compared to the 600s reconstruction. Conclusion: Injected activity and scan time can be reduced to 1.5 MBq/kg 2-[18F] FDG with 5 min acquisition time on LAFOV PET/CT, while maintaining clinical performance in the pediatric population. These results can help limit radiation exposure to patients and personnel as well as shorten total scan time, which can help increase patient comfort, lessen the need for sedation and provide individually tailored scans. [ABSTRACT FROM AUTHOR]

Introduction: Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations. Methods: We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment. Results: We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model. Conclusion: In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation. [ABSTRACT FROM AUTHOR]