Ultrasound (US) imaging is one of the most accessible, non-invasive, and real-time diagnostic techniques in clinical medicine. However, conventional B-mode US suffers from intrinsic limitations such as speckle noise, operator dependence, and variability in image interpretation, which reduce diagnostic reproducibility and hinder skill acquisition. Because accurate image acquisition and interpretation rely heavily on the operator’s experience, mastering ultrasound requires extensive hands-on training under diverse anatomical and pathological conditions. Yet, traditional educational settings rarely provide consistent exposure to such variability, making simulation-based environments essential for developing and standardizing operator expertise. This scoping review synthesizes advances from 2014 to 2024 in B-mode ultrasound simulation, identifying 80 studies through structured searches in PubMed, Scopus, Web of Science, and IEEE. Simulation methods were organized into interpolative, wave-based, ray-based, and convolution-based models, as well as emerging Artificial Intelligence (AI)-driven approaches. The review emphasizes recent simulation engines and toolboxes reported in this period and highlights the growing role of learning-based pipelines (e.g., Generative Adversarial Networks (GANs) and diffusion) for realism, scalability, and data augmentation. The results show steady progress toward high realism and computational efficiency, including Graphics Processing Unit (GPU)-accelerated transport models, physics-informed convolution, and AI-enhanced translation and synthesis. Remaining challenges include the modeling of nonlinear and dynamic effects at scale, standardizing evaluation across tasks, and integrating physics with learning to balance fidelity and speed. These findings outline current capabilities and future directions for training, validation, and diagnostic support in ultrasound imaging.

Positron range (PR) limits spatial resolution and quantitative accuracy in PET imaging, particularly for high-energy positron-emitting radionuclides such as [Formula: see text]Ga. This study proposes a deep learning-based approach using 3D residual encoder-decoder convolutional neural networks (3D RED-CNNs), incorporating tissue-dependent anatomical information through a μ-map-dependent loss function. We propose a model trained from realistic simulations and, using the information in the initial PET and CT images, obtain positron range corrected images. We validated our model in both simulations and real acquisitions. Three different 3D RED-CNN architectures-Single-Channel, Two-Channel, and DualEncoder-were trained using simulated PET datasets and evaluated on both synthetic and real PET acquisitions from [Formula: see text]Ga-FH and [Formula: see text]Ga-PSMA-617 mouse studies. The performance of each model was compared to a standard Richardson-Lucy deconvolution (RL-PRC) approach using metrics such as mean absolute error (MAE), structural similarity index (SSIM), contrast recovery (CR), and contrast-to-noise ratio (CNR). In simulations, CNN-based methods achieved up to 19% improvement in SSIM and 13% reduction in MAE compared to RL-PRC. The Two-Channel model showed the highest CR and CNR values, recovering lung activity with 97% agreement to the ground truth, compared to 77% with RL-PRC. Noise remained stable for CNN models (∼5.9%), whereas RL-PRC increased noise by 5.8%. In preclinical acquisitions, the Two-Channel model achieved the highest CNR in different tissues, while maintaining the lowest noise level (9.6%). Although no ground truth was available for real data, analysis confirmed superior tumor delineation and reduced spillover artifacts with the Two-Channel model. These findings demonstrate the potential of CNN-based PRC for improving quantitative PET imaging, particularly for [Formula: see text]Ga. CNN-based methods, particularly the Two-Channel model, outperformed conventional deconvolution in both simulated and real data. Future work will focus on enhancing model generalization through domain adaptation and hybrid training strategies, as well as extending the method to other high-energy PET radionuclides.

Introduction: The positron range (PR) effect is a significant factor limiting spatial resolution in positron emission tomography (PET), particularly for high-resolution systems and non-standard isotopes. Objective: This study introduces a novel analytical model to accurately and rapidly describe positron range distributions (PRd) for various PET radioisotopes. Approach: The proposed model explicitly incorporates the Coulomb repulsion effect, the multi-branch nature of certain β⁺ emitters, and the scaling of PR with electronic density. To minimise bias, we used a histogram-free statistical method to derive the cumulative PRd from Monte Carlo (MC) simulated annihilation datasets, avoiding arbitrary histogram binning. A comparative analysis of PR estimates was conducted across three major MC radiation transport algorithm packages: PENELOPE (via PenEasy/PeneloPET), GEANT4 (via GATE), and EGS5 (via PHITS), revealing notable discrepancies between codes, versions, and input configurations, especially at short distances from the source. Main results: The new analytical model demonstrated an excellent reproduction of the simulated data for isotopes including C-11, N-13, O-15, F-18, Cu-64, Ga-68, Rb-82, and I-124, achieving in general coefficients of determination (R²) greater than 0.995 and mean absolute percentage errors (MAPE) < 20%. Compared to previous methods, our model provides a more accurate description of PRd at short and long distances and offers improved R² values. Significance: This work provides a robust framework for generating accurate annihilation point spread function (aPSF) kernels, facilitating improved PR correction in quantitative Nuclear Medical imaging and supporting research with diverse radioisotopes.

Positron range (PR) degrades spatial resolution in positron emission tomography (PET), with its effect being radionuclide- and medium-dependent. Current accurate PR simulators are computationally intensive and cannot be used in clinical time frames. This work presents the Hybrid Monte Carlo (HMC) PR simulator, a GPU-accelerated condensed-history code in CUDA Fortran for 3D annihilation imaging in heterogeneous voxelised geometries derived from CT images and $\mu$-maps. Pre-computed look-up interaction tables (LUTs) covering 24 reference-tissue materials and support for arbitrary ${\beta }^{+}$ emitters up to 3.55 MeV end-point energies (⁶⁸Ga, ⁸²Rb, ¹²⁴I, etc.) allow both GPU and CPU implementations. Maximum speeds reach $4.77·{10}^5$ (Intel i9-14900KF) and $3.32·{10}^8$ (NVIDIA RTX 5080) histories/s, surpassing prior CPU-only PenEasy 2024 by up to 4 orders of magnitude. A fixed step of 0.5 mm was chosen for LUT generation with a uniform $\pm 10\%$ variation each HMC step that allowed reducing aliasing effects. These values are consistent with our previous cross-code benchmark for the isotopes studied. Close agreement with PenEasy 2024 is demonstrated: point-source FWHM and FWTM residuals using ¹⁸F, ⁶⁸Ga, ¹²⁴I and ⁸²Rb are below 0.3 mm at ${10}^7$ histories, and voxelwise $R^2=0.995$, $\mathrm{SSIM}=0.995$ and PSNR $\approx 36.4$ dB are achieved simulating ⁸²Rb uptake from a human-scale [¹⁸F]FDG myocardium acquisition, while decreasing the simulation time from almost 9 h to roughly 1 s using HMC GPU. The resulting annihilation maps enable PET resolution modelling and patient-specific PR correction within clinically practical time frames.