Articles
Publication Date: 2025
Magnetic Resonance Materials in Physics, Biology and Medicine (13528661)38(2)pp. 299-315
Objective: This study presents a novel deep learning-based framework for precise brain MR region segmentation, aiming to identify the location and the shape details of different anatomical structures within the brain. Materials and methods: The approach uses a two-stage 3D segmentation technique on a dataset of adult subjects, including cognitively normal participants and individuals with cognitive decline. Stage 1 employs a 3D U-Net to segment 13 brain regions, achieving a mean DSC of 0.904 ± 0.060 and a mean HD95 of 1.52 ± 1.53 mm (a mean DSC of 0.885 ± 0.065 and a mean HD95 of 1.57 ± 1.35 mm for smaller parts). For challenging regions like hippocampus, thalamus, cerebrospinal fluid, amygdala, basal ganglia, and corpus callosum, Stage 2 with SegResNet refines segmentation, improving mean DSC to 0.921 ± 0.048 and HD95 to 1.17 ± 0.69 mm. Results: Statistical analysis reveals significant improvements (p-value < 0.001) for these regions, with DSC increases ranging from 1.3 to 3.2% and HD95 reductions of 0.06–0.33 mm. Comparisons with recent studies highlight the superior performance of the performed method. Discussion: The inclusion of a second stage for refining the segmentation of smaller regions demonstrates substantial improvements, establishing the framework’s potential for precise and reliable brain region segmentation across diverse cognitive groups. © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2025.
Publication Date: 2025
Scientific Reports (20452322)15(1)
In PET systems, the SNR relies on the coincidence time resolution (CTR) of 511 keV photon pairs. This research investigates the impact of reflectors, surface treatments, materials, and scintillation crystal length on the CTR of a brainPET detector using dual-layer offset scintillators (DLOs). This study is based on a brainPET, under development at the University of Manitoba, to propose a new design to achieve an improved CTR. Four different pairs of LYSO crystals with distinct optical compositions, surface treatments, and reflective materials were simulated (using GATEv9.3). Each model comprises two LYSO crystal with dimensions of 3 × 3 × 10 mm3. Considering the initial experimental data from the brainPET lab, simulation results showed that the crystal with a roughened surface and ESR reflector demonstrated 13.6% energy resolution and an average 17.8% improvement in CTR compared to other models. In addition, a more comprehensive model, including a dual-layer offset detector was designed. The bottom and top layers have 25 × 19 and 24 × 18 crystals with thickness of 12 and 8 mm, respectively in the DLO model. The simulation investigation showed that the DLO configuration could enhance the time resolution by 17.5% and the energy resolution by 5.4% which are considerably comparable to the state-of-the-art brainPET systems. © The Author(s) 2025.
Publication Date: 2025
Iranian Conference on Electrical Engineering, ICEE (26429527)pp. 393-397
Pulmonary embolism (PE) is a life-threatening condition, often leading to late diagnoses. Diagnostic tools like Computed Tomography Pulmonary Angiography (CTPA) rely on radiologist skills, resulting in variable sensitivity and specificity. This study aims to leverage deep learning, specifically a convolutional neural network with U-net architecture enhanced by Squeeze-and-Attention and Long Short-Term Memory (LSTM) blocks, to improve the segmentation of emboli in CTPA images. Utilizing two datasets, CAD-PE (91 cases, 89 with PE) and FUMPE (35 cases, 33 with PE), the research assesses how increasing the number of network layers (57, 67, and 103) affects segmentation performance. The results demonstrated that the slice-wise sensitivity improved from 76.73 pm 21.94 with a 57 layer architecture to 80.36 pm 21.42 with a 67-layer architecture, indicating better pulmonary embolism detection (with a significant difference due to paired T-test P-value of less than 0.05). In addition, the patient-wise AUC slightly increases from 81.68 pm 10.94 (57 layers) to 85.09 pm 10.69 (67 layers) with a Kruskal-Wallis P-value of 0. 0 1 8 9, which indicates a significant difference between the networks' performance. However, no significant improvement was observed with the 103-layer model, highlighting the potential for overfitting. Results from this study demonstrate the potential of deep learning algorithms in enhancing the accurate diagnosis of pulmonary embolism. Finally, the neural network's performance in segmenting pulmonary embolisms from CT images demonstrates promising directions with particular specificity and overall AUC strengths. © 2025 IEEE.
