Showing 1–26 of 26 results for author: Safari, M

Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction

Authors: Mojtaba Safari, Shansong Wang, Zach Eidex, Matthew Goette, Tonghe Wang, Zhen Tian, Xiaofeng Yang

Abstract: Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are rarely available in clinical settings. We propose SSRL-MAR, a motion artifact-aware unpaired represen… ▽ More Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are rarely available in clinical settings. We propose SSRL-MAR, a motion artifact-aware unpaired representation learning framework for motion artifact reduction that requires neither paired training data nor explicit motion labels. SSRL-MAR employed a three-stage training strategy: (1) contrastive learning on 3D patches to extract motion representations by contrasting clean and synthetically corrupted images, (2) a motion artifact-aware synthesis network to generate motion artifacts from clean scans, and (3) a motion artifact-aware generator to restore clean volumes using the learned degrader for self-supervised supervision. On in-silico dataset, SSRL-MAR achieved PSNR 23.81dB, SSIM 91.55%, and NMSE 0.79%. On in-vivo MR-ART dataset, the pretrained model reduced motion distortion, and unsupervised domain adaptation further improved anatomical fidelity. Against a source-only supervised model trained on the same simulated pairs, SSRL-MAR improved PSNR by up to 2.0 dB on MR-ART after unsupervised domain adaptation, and remained within 0.25-0.47 dB of an oracle supervised model that requires real paired data unavailable in practice. At the milder motion level, volumetric error in structures such as the corpus callosum and ventricular system decreased by more than 50%, confirming improved neuroanatomical consistency. These results indicate that SSRL-MAR provides a robust and scalable image-domain solution for 3D brain MRI motion correction, enabling reliable structural quantification in large-scale neuroimaging studies without requiring prospectively acquired pairs or acquisition-specific calibration. △ Less

Submitted 10 August, 2026; originally announced August 2026.

MRI super-resolution in ten sampling steps using a diffusion bridge model

Authors: Mojtaba Safari, Hang Yu, Zach Eidex, Mingzhe Hu, Ryan J. Sanford, Alexandru Florea, Shansong Wang, Chih-Wei Chang, Erik H Middlebrooks, Aditya Juloori, Stanley L. Liauw, Ralph Weichselbaum, Xiaofeng Yang

Abstract: Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial resolution and scan time. Diffusion-based super-resolution (SR) reconstructs high-resolution (HR) images from low-resolution (LR) inputs, but typically needs many sampling steps and initializes from a Gaussian prior ill-su… ▽ More Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial resolution and scan time. Diffusion-based super-resolution (SR) reconstructs high-resolution (HR) images from low-resolution (LR) inputs, but typically needs many sampling steps and initializes from a Gaussian prior ill-suited to image restoration. We developed an efficient diffusion framework that reconstructs HR MRI directly from LR data. Approach. We propose super-resolution diffusion bridge model (SR-DBM), a super-resolution diffusion bridge model that casts SR as a stochastic transport between the LR and HR image distributions. Through a Doob's h-transform of a mean-reverting stochastic differential equation, SR-DBM pins the process to the paired HR and LR images at its endpoints, initializing reconstruction from the measured anatomy rather than from Gaussian noise. The HR image is recovered by a deterministic reverse trajectory in which a network predicts the clean image at each of only ten sampling steps. We evaluated SR-DBM on ultra-high-field 7T brain T1 MP2RAGE maps and pelvic T2-weighted prostate images against nine comparison methods using PSNR, SSIM, GMSD, and LPIPS. Main results. SR-DBM attained the highest PSNR and SSIM and the lowest GMSD on both datasets (brain: 27.66+-1.52 dB, 0.96+-0.02, 7.96+-1.86$; prostate: 27.87+-2.29 dB, 0.80+-0.05, 8.38+- 1.44), with statistically significant gains over every comparison method (two-sided Wilcoxon signed-rank test with Holm correction, p<0.05). The strongest baseline, SR-EMamba, ranked second. Qualitatively, SR-DBM produced the smallest residual errors and best preserved fine structures and lesions. △ Less

Submitted 9 August, 2026; originally announced August 2026.

One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy

Authors: Shaoyan Pan, Kirk Jon Luca, Yuan Gao, Shansong Wang, Mingzhe Hu, Ryan Sanford, Mojtaba Safari, Justin Roper, Zhen Tian, Tonghe Wang, Xiaofeng Yang

Abstract: In this work, we introduce the One-for-All Adaptive Radiotherapy Planning Agent, a unified foundation-model-based system that performs complete, treatment-specific online adaptive planning directly from daily cone-beam CT in under two minutes. The agent first autonomously predicts all essential planning components, including synthetic CT generation, multimodal alignment, and tumor/organ segmentati… ▽ More In this work, we introduce the One-for-All Adaptive Radiotherapy Planning Agent, a unified foundation-model-based system that performs complete, treatment-specific online adaptive planning directly from daily cone-beam CT in under two minutes. The agent first autonomously predicts all essential planning components, including synthetic CT generation, multimodal alignment, and tumor/organ segmentation. It then intelligently leverages these outputs to execute the final clinical plan design, providing a comprehensive, automated solution for daily treatment. We also demonstrate that the agent enables clinicians to define planning with intent and intervene at critical decision points, ensuring a "human-in-the-loop" framework that generates acceptable plans before final approval. Evaluated on multiple datasets spanning head-and-neck, lung, abdominal, and prostate cancers with both photon and proton therapy, the proposed framework achieves clinically acceptable accuracy and plan quality comparable to clinically generated treatment plans, with target dose errors (D98) generally within 2.0 Gy of the reference plan. The strong performance of the One-for-All agent highlights the promise of a unified foundation-model approach and opens opportunities for fast, scalable, and fully automated online adaptive radiotherapy across diverse clinical scenarios. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Disentangling spanwise asymmetries in unsteady wing wakes: global mode sensitivity and spatio-temporal harmonic resolvent analyses

Authors: Maryam Safari, Chi-An Yeh

Abstract: We investigate the emergence of long-time spanwise asymmetries in an unsteady wake downstream of a finite-span wing by disentangling flow asymmetries into symmetric and anti-symmetric components using global mode (structural) sensitivity and spatio-temporal harmonic resolvent analysis. The global mode sensitivity analysis shows that asymmetric modes emerge when symmetric and anti-symmetric eigenmo… ▽ More We investigate the emergence of long-time spanwise asymmetries in an unsteady wake downstream of a finite-span wing by disentangling flow asymmetries into symmetric and anti-symmetric components using global mode (structural) sensitivity and spatio-temporal harmonic resolvent analysis. The global mode sensitivity analysis shows that asymmetric modes emerge when symmetric and anti-symmetric eigenmodes appear as pairs and exhibit high levels of modal non-normality. The modal non-normality renders the eigenmodes susceptible to asymmetric disturbances, which results in phase interference between the paired symmetric and anti-symmetric modes and unfolds them into highly asymmetric modes. Such interferences further motivate the development of a spatio-temporal harmonic resolvent analysis to examine the cross-frequency phase coupling between modes of different phase velocities. We observe that the flow asymmetries are primarily driven by elliptic vortex instability and its interaction with the wake shear layers. Moreover, we show that, even with a large-amplitude departure in the base flow from the symmetric state, the asymmetric modes obtained from the asymmetric wake can be accurately reconstructed by the symmetric and anti-symmetric modes from the symmetric base flow. This important finding suggests that flow asymmetries can be understood as a superposition of symmetric and anti-symmetric structures that lie under the symmetric base flow, and their phase interference serves as a potential mechanism for the emergence of long-time flow asymmetries. We believe that the present study provides a promising path towards understanding and controlling the emergence of asymmetric flow structures over finite-span wings. △ Less

Submitted 30 May, 2026; originally announced June 2026.

Comments: 32 pages, 18 figures

Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning

Authors: Mojtaba Safari, Shansong Wang, Vanessa L Wildman, Mingzhe Hu, Zach Eidex, Chih-Wei Chang, Erik H Middlebrooks, Richard L. J Qiu, Pretesh Patel, Ashesh B. Jani, Hui Mao, Zhen Tian, Xiaofeng Yang

Abstract: Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep learning methods face fidelity-efficiency trade-offs. Purpose: To develop a computationally efficient and accurate deep learning framework for MRI SR that preserves anatomical detail for clinical integration. Materials a… ▽ More Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep learning methods face fidelity-efficiency trade-offs. Purpose: To develop a computationally efficient and accurate deep learning framework for MRI SR that preserves anatomical detail for clinical integration. Materials and Methods: We propose a novel SR framework combining multi-head selective state-space models (MHSSM) with a lightweight channel MLP. The model uses 2D patch extraction with hybrid scanning to capture long-range dependencies. Each MambaFormer block integrates MHSSM, depthwise convolutions, and gated channel mixing. Evaluation used 7T brain T1 MP2RAGE maps (n=142) and 1.5T prostate T2w MRI (n=334). Comparisons included Bicubic interpolation, GANs (CycleGAN, Pix2pix, SPSR), transformers (SwinIR), Mamba (MambaIR), and diffusion models (I2SB, Res-SRDiff). Results: Our model achieved superior performance with exceptional efficiency. For 7T brain data: SSIM=0.951+-0.021, PSNR=26.90+-1.41 dB, LPIPS=0.076+-0.022, GMSD=0.083+-0.017, significantly outperforming all baselines (p<0.001). For prostate data: SSIM=0.770+-0.049, PSNR=27.15+-2.19 dB, LPIPS=0.190+-0.095, GMSD=0.087+-0.013. The framework used only 0.9M parameters and 57 GFLOPs, reducing parameters by 99.8% and computation by 97.5% versus Res-SRDiff, while outperforming SwinIR and MambaIR in accuracy and efficiency. Conclusion: The proposed framework provides an efficient, accurate MRI SR solution, delivering enhanced anatomical detail across datasets. Its low computational demand and state-of-the-art performance show strong potential for clinical translation. △ Less

Submitted 7 March, 2026; v1 submitted 22 December, 2025; originally announced December 2025.

Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model

Authors: Qiang Li, Mojtaba Safari, Shansong Wang, Huiqiao Xie, Jie Ding, Tonghe Wang, Xiaofeng Yang

Abstract: Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framew… ▽ More Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framework that integrates a residual shifting mechanism to align low-dose and full-dose distributions and performs only four reverse diffusion steps using a Swin-based U-Net backbone. A composite loss combining pixel reconstruction, perceptual similarity (LPIPS), and total variation (TV) regularization effectively suppresses spatially varying noise while preserving anatomical structures. RE-EDPM was evaluated on a public LDCT benchmark across dose levels and anatomical sites. On 10 percent dose chest and 25 percent dose abdominal scans, it achieved SSIM = 0.879 (0.068), PSNR = 31.60 (2.52) dB, VIFp = 0.366 (0.121) for chest, and SSIM = 0.971 (0.000), PSNR = 36.69 (2.54) dB, VIFp = 0.510 (0.007) for abdomen. Visual and statistical analyses, including ablation and Wilcoxon signed-rank tests (p < 0.05), confirm significant contributions from residual shifting and regularization terms. RE-EDPM processes two 512x512 slices in about 0.25 s on modern GPUs, supporting near real-time clinical use. The proposed framework achieves an optimal balance between noise suppression and anatomical fidelity, offering an efficient solution for LDCT restoration and broader medical image enhancement tasks. △ Less

Submitted 27 October, 2025; originally announced October 2025.

Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis

Authors: Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He, Mingzhe Hu, Shansong Wang, Jun Zhou, Xiaofeng Yang

Abstract: Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks, from segmentation to report generation. Unlike traditional task-specific AI models, FMs leverage large corpora of labeled and unlabeled multimodal datasets to learn generalize… ▽ More Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks, from segmentation to report generation. Unlike traditional task-specific AI models, FMs leverage large corpora of labeled and unlabeled multimodal datasets to learn generalized representations that can be adapted to various downstream clinical applications with minimal fine-tuning. However, despite the rapid proliferation of FM research in medical imaging, the field remains fragmented, lacking a unified synthesis that systematically maps the evolution of architectures, training paradigms, and clinical applications across modalities. To address this gap, this review article provides a comprehensive and structured analysis of FMs in medical image analysis. We systematically categorize studies into vision-only and vision-language FMs based on their architectural foundations, training strategies, and downstream clinical tasks. Additionally, a quantitative meta-analysis of the studies was conducted to characterize temporal trends in dataset utilization and application domains. We also critically discuss persistent challenges, including domain adaptation, efficient fine-tuning, computational constraints, and interpretability along with emerging solutions such as federated learning, knowledge distillation, and advanced prompting. Finally, we identify key future research directions aimed at enhancing the robustness, explainability, and clinical integration of FMs, thereby accelerating their translation into real-world medical practice. △ Less

Submitted 19 October, 2025; originally announced October 2025.

Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction

Authors: Mojtaba Safari, Zach Eidex, Richard L. J. Qiu, Matthew Goette, Tonghe Wang, Xiaofeng Yang

Abstract: Background: To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion artifacts, assessing current developments, effectiveness, challenges, and future research directions. Methods: A comprehensive systematic review and meta-analysis were conducted, focusing on deep learning (DL) approache… ▽ More Background: To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion artifacts, assessing current developments, effectiveness, challenges, and future research directions. Methods: A comprehensive systematic review and meta-analysis were conducted, focusing on deep learning (DL) approaches, particularly generative models, for the detection and correction of MRI motion artifacts. Quantitative data were extracted regarding utilized datasets, DL architectures, and performance metrics. Results: DL, particularly generative models, show promise for reducing motion artifacts and improving image quality; however, limited generalizability, reliance on paired training data, and risk of visual distortions remain key challenges that motivate standardized datasets and reporting. Conclusions: AI-driven methods, particularly DL generative models, show significant potential for improving MRI image quality by effectively addressing motion artifacts. However, critical challenges must be addressed, including the need for comprehensive public datasets, standardized reporting protocols for artifact levels, and more advanced, adaptable DL techniques to reduce reliance on extensive paired datasets. Addressing these aspects could substantially enhance MRI diagnostic accuracy, reduce healthcare costs, and improve patient care outcomes. △ Less

Submitted 5 September, 2025; originally announced September 2025.

Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI

Authors: Mojtaba Safari, Shansong Wang, Qiang Li, Zach Eidex, Richard L. J. Qiu, Chih-Wei Chang, Hui Mao, Xiaofeng Yang

Abstract: Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact correct… ▽ More Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact correction.Approach.Res-MoCoDiff exploits a novel residual error shifting mechanism during the forward diffusion process to incorporate information from motion-corrupted images. This mechanism allows the model to simulate the evolution of noise with a probability distribution closely matching that of the corrupted data, enabling a reverse diffusion process that requires only four steps. The model employs a U-net backbone, with attention layers replaced by Swin Transformer blocks, to enhance robustness across resolutions. Furthermore, the training process integrates a combined l1+l2 loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both an in-silico dataset generated using a realistic motion simulation framework and an in-vivo MR-ART dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and a diffusion model with a vision transformer backbone, using quantitative metrics such as PSNR, SSIM, and NMSE.Main results. The proposed method demonstrated superior performance in removing motion artifacts across minor, moderate, and heavy distortion levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41.91+-2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 seconds per batch of two image slices, compared with 101.74 seconds for conventional approaches. △ Less

Submitted 4 September, 2025; v1 submitted 6 May, 2025; originally announced May 2025.

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting

Authors: Mojtaba Safari, Shansong Wang, Zach Eidex, Qiang Li, Erik H. Middlebrooks, David S. Yu, Xiaofeng Yang

Abstract: Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reconstruction. Approach:We propose a novel diffusion-based SR framework called Res-SRDiff, which integrates residual error shifting into the forward diffusion process. This enables efficient HR image reconstruction by align… ▽ More Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reconstruction. Approach:We propose a novel diffusion-based SR framework called Res-SRDiff, which integrates residual error shifting into the forward diffusion process. This enables efficient HR image reconstruction by aligning the degraded HR and LR distributions.We evaluated Res-SRDiff on ultra-high-field brain T1 MP2RAGE maps and T2-weighted prostate images, comparing it with Bicubic, Pix2pix, CycleGAN, and a conventional denoising diffusion probabilistic model with vision transformer backbone (TM-DDPM), using quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), gradient magnitude similarity deviation (GMSD), and learned perceptual image patch similarity (LPIPS). Main results: Res-SRDiff significantly outperformed all comparative methods in terms of PSNR, SSIM, and GMSD across both datasets, with statistically significant improvements (p-values<<0.05). The model achieved high-fidelity image restoration with only four sampling steps, drastically reducing computational time to under one second per slice, which is substantially faster than conventional TM-DDPM with around 20 seconds per slice. Qualitative analyses further demonstrated that Res-SRDiff effectively preserved fine anatomical details and lesion morphology in both brain and pelvic MRI images. Significance: Our findings show that Res-SRDiff is an efficient and accurate MRI SR method, markedly improving computational efficiency and image quality. Integrating residual error shifting into the diffusion process allows for rapid and robust HR image reconstruction, enhancing clinical MRI workflows and advancing medical imaging research. The source at:https://github.com/mosaf/Res-SRDiff △ Less

Submitted 26 April, 2025; v1 submitted 3 March, 2025; originally announced March 2025.

A Physics-Informed Deep Learning Model for MRI Brain Motion Correction

Authors: Mojtaba Safari, Shansong Wang, Zach Eidex, Richard Qiu, Chih-Wei Chang, David S. Yu, Xiaofeng Yang

Abstract: Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motion correction network that integrates spatial and k-space information to remove motion artifacts without explicit motion parameter estimation, enhancing image fidelity and diagnostic reliability. Materials and Methods: PI… ▽ More Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motion correction network that integrates spatial and k-space information to remove motion artifacts without explicit motion parameter estimation, enhancing image fidelity and diagnostic reliability. Materials and Methods: PI-MoCoNet consists of a motion detection network (U-net with spatial averaging) to identify corrupted k-space lines and a motion correction network (U-net with Swin Transformer blocks) to reconstruct motion-free images. The correction is guided by three loss functions: reconstruction (L1), perceptual (LPIPS), and data consistency (Ldc). Motion artifacts were simulated via rigid phase encoding perturbations and evaluated on IXI and MR-ART datasets against Pix2Pix, CycleGAN, and U-net using PSNR, SSIM, and NMSE. Results: PI-MoCoNet significantly improved image quality. On IXI, for minor artifacts, PSNR increased from 34.15 dB to 45.95 dB, SSIM from 0.87 to 1.00, and NMSE reduced from 0.55% to 0.04%. For moderate artifacts, PSNR improved from 30.23 dB to 42.16 dB, SSIM from 0.80 to 0.99, and NMSE from 1.32% to 0.09%. For heavy artifacts, PSNR rose from 27.99 dB to 36.01 dB, SSIM from 0.75 to 0.97, and NMSE decreased from 2.21% to 0.36%. On MR-ART, PI-MoCoNet achieved PSNR gains of ~10 dB and SSIM improvements of up to 0.20, with NMSE reductions of ~6%. Ablation studies confirmed the importance of data consistency and perceptual losses, yielding a 1 dB PSNR gain and 0.17% NMSE reduction. Conclusions: PI-MoCoNet effectively mitigates motion artifacts in brain MRI, outperforming existing methods. Its ability to integrate spatial and k-space information makes it a promising tool for clinical use in motion-prone settings. Code: https://github.com/mosaf/PI-MoCoNet.git. △ Less

Submitted 13 February, 2025; originally announced February 2025.

Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration

Authors: Mojtaba Safari, Zach Eidex, Chih-Wei Chang, Richard L. J. Qiu, Xiaofeng Yang

Abstract: Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to s… ▽ More Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI. △ Less

Submitted 1 February, 2025; v1 submitted 23 January, 2025; originally announced January 2025.

Comments: arXiv admin note: substantial text overlap with arXiv:2405.00241

Duty-cycle actuation for drag reduction of deep dynamic stall: Insights from linear stability analysis

Authors: Lucas Feitosa de Souza, William Roberto Wolf, Maryam Safari, Chi-An Yeh

Abstract: A flow control framework based on linear stability analysis is proposed focusing on reducing the aerodynamic drag due to dynamic stall through a finite-window temporal actuation. The methodology is applied on a periodically plunging SD7003 airfoil.Finite-time Lyapunov exponent (FTLE) fields reveal a saddle point near the airfoil leading edge, where a shear layer forms and feeds a dynamic stall vor… ▽ More A flow control framework based on linear stability analysis is proposed focusing on reducing the aerodynamic drag due to dynamic stall through a finite-window temporal actuation. The methodology is applied on a periodically plunging SD7003 airfoil.Finite-time Lyapunov exponent (FTLE) fields reveal a saddle point near the airfoil leading edge, where a shear layer forms and feeds a dynamic stall vortex (DSV). A local stability analysis conducted at this saddle point identifies a Kelvin-Helmholtz instability, and the most unstable eigenvalue frequencies remain constant when the variation in the effective angle of attack is minimal. The findings from the FTLE fields and the stability analysis are used to inform the position and finite duty cycle of a periodic blowing and suction actuation applied in a wall-resolved large eddy simulation (LES). The present framework reduces the actuation duty cycle by 77.5% during the airfoil plunging motion, while maintaining the same performance as a continuous actuation throughout the entire cycle. The LES results demonstrate that disturbances from the stability-analysis-informed actuation modify the leading-edge dynamics, preventing the formation of the coherent DSV and significantly reducing the drag. △ Less

Submitted 14 January, 2025; originally announced January 2025.

Comments: 23 pages, 10 figures

Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review

Authors: Mojtaba Safari, Zach Eidex, Chih-Wei Chang, Richard L. J. Qiu, Xiaofeng Yang

Abstract: Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve t… ▽ More Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve the overall efficiency of MRI. One such technique is compressed sensing (CS), which reduces data acquisition by leveraging image sparsity in transformed spaces. In recent years, deep learning (DL) has been integrated with CS-MRI, leading to a new framework that has seen remarkable growth. DL-based CS-MRI approaches are proving to be highly effective in accelerating MR imaging without compromising image quality. This review comprehensively examines DL-based CS-MRI techniques, focusing on their role in increasing MR imaging speed. We provide a detailed analysis of each category of DL-based CS-MRI including end-to-end, unroll optimization, self-supervised, and federated learning. Our systematic review highlights significant contributions and underscores the exciting potential of DL in CS-MRI. Additionally, our systematic review efficiently summarizes key results and trends in DL-based CS-MRI including quantitative metrics, the dataset used, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based CS-MRI in the advancement of medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based CS-MRI publications and publicly available datasets - https://github.com/mosaf/Awesome-DL-based-CS-MRI. △ Less

Submitted 30 April, 2024; originally announced May 2024.

High-resolution 3T to 7T MRI Synthesis with a Hybrid CNN-Transformer Model

Authors: Zach Eidex, Jing Wang, Mojtaba Safari, Eric Elder, Jacob Wynne, Tonghe Wang, Hui-Kuo Shu, Hui Mao, Xiaofeng Yang

Abstract: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. To address these issues, we propose a hybrid CNN-transfo… ▽ More 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multi-modal 3T MRI. The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in the Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided: 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). The results are as follows: PSNR: 27.0+-0.9 dB, SSIM: 0.945+-0.010, and MSE: 2.0+-0.4E-3. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, especially when 7T MRI scanners are unavailable. △ Less

Submitted 25 November, 2023; originally announced November 2023.

EU COST Action on future generation optical wireless communication technologies, 2nd White paper

Authors: Z. Ghassemlooy, M. A. Khalighi, S. Zvanovec, A. Shrestha, B. Ortega, M. Petkovic, X. Pang, C. Sirtori, D. Orsucci, A. Shrestha, F. Moll, G. Cossu, V. Spirito, M. P. Ninos, E. Ciaramella, J. Bas, M. Amay, S. Huang, M. Safari, T. Gutema, W. Popoola, Vicente Matus, Jose Rabadan, Rafael Perez-Jimenez, E. Panayirci , et al. (3 additional authors not shown)

Abstract: NEWFOCUS is an EU COST Action targeted at exploring radical solutions that could influence the design of future wireless networks. The project aims to address some of the challenges associated with optical wireless communication (OWC) and to establish it as a complementary technology to the radio frequency (RF)-based wireless systems in order to meet the demanding requirements of the fifth generat… ▽ More NEWFOCUS is an EU COST Action targeted at exploring radical solutions that could influence the design of future wireless networks. The project aims to address some of the challenges associated with optical wireless communication (OWC) and to establish it as a complementary technology to the radio frequency (RF)-based wireless systems in order to meet the demanding requirements of the fifth generation (5G) and the future sixth generation (6G) backhaul and access networks. Only 6G will be able to widely serve the exponential growth in connected devices (i.e., more than 500 billion) in 2030, real-time holographic communication, future virtual reality, etc. Space is emerging as the new frontier in 5 and 6G and beyond communication networks, where it offers high-speed wireless coverage to remote areas both in lands and sees. This activity is supported by the recent development of low-altitude Earth orbit satellite mega-constellations. The focus of this 2nd White Paper is on the use of OWC as an enabling technology for medium- and long-range links for deployment in (i) smart-cities and intelligent transportation systems; (ii) first- and last-mile access and backhaul/fronthaul wireless networks; (iii) hybrid free-space optics/RF adaptive wireless connections; (iv) space-to-ground, inter-satellite, ground-to-air, and air-to-air communications; and (v) underwater communications. △ Less

Submitted 14 June, 2023; originally announced November 2023.

Growing borophene on metal substrates: a theoretical study of the role of oxygen on Al(111)

Authors: Mandana Safari, Erik Vesselli, Stefano de Gironcoli, Stefano Baroni

Abstract: Charge transfer from a metal substrate stabilizes honeycomb borophene, whose electron deficit would otherwise spoil the hexagonal order of a $π$-bonded 2D atomic network. However, the coupling between the substrate and the boron overlayer may result in the formation of strong chemical bonds that would compromise the electronic properties of the overlayer. In this paper we present a theoretical stu… ▽ More Charge transfer from a metal substrate stabilizes honeycomb borophene, whose electron deficit would otherwise spoil the hexagonal order of a $π$-bonded 2D atomic network. However, the coupling between the substrate and the boron overlayer may result in the formation of strong chemical bonds that would compromise the electronic properties of the overlayer. In this paper we present a theoretical study, based on state-of-the-art density-functional and genetic-optimization techniques, of the electronic and structural properties of borophene grown on Al(111), with emphasis on the impact of oxygen on the strength of the coupling between substrate and overlayer. While our results confirm the formation of Al-B bonds, they also predict that oxygen doping reduces charge transfer between aluminum and borophene, thus allowing modulation of their strength and paving the way to engineering the electronic properties of 2D-supported borophene sheets for industrial applications. Our study is completed by a thorough study of the thermodynamic stability of the oxygenated borophene-Al(111) interface. △ Less

Submitted 31 October, 2022; v1 submitted 16 June, 2022; originally announced June 2022.

Multi-functional Metasurface: Visibly and RF Transparent, NIR Control and Low Thermal Emissivity

Authors: Mahdi Safari, Nazir P. Kherani, George V. Eleftheriades

Abstract: Rapid advances in metamaterial technology are enabling the engineering of wave-matter interactions heretofore not realized and functionalities with potentially far-reaching implications for major challenges in the fields of energy conservation and radio frequency (RF) communication. We propose a visibly and RF transparent composite metasurface utilizing dielectric-metal spectrally selective coatin… ▽ More Rapid advances in metamaterial technology are enabling the engineering of wave-matter interactions heretofore not realized and functionalities with potentially far-reaching implications for major challenges in the fields of energy conservation and radio frequency (RF) communication. We propose a visibly and RF transparent composite metasurface utilizing dielectric-metal spectrally selective coatings with high NIR control and low thermal emissivity, thus achieving a multi-functional metasurface capable of enhancing 5G communication efficiency and exhibiting energy conservation features. The proposed meta-glass yields 92% peak RF transmission at 30 GHz which corresponds to 20% and 90% enhancement when compared to plain glass and low-emissive glass substrates. This meta-glass possesses 86% peak optical transparency at $λ=550 nm$, $>$60% near-IR reflection, and $>$ 80% mid-IR reflection which corresponds to $\approx$ 0.2 thermal emissivity. The proposed metasurface design is highly flexible and can be tuned to operate over different frequency ranges owing to its frequency scalability. This study provides a better alternative using earth-abundant materials compared to low-emissive glass based on indium tin oxide (ITO) while boosting the efficiency of 5G communication amenable to window systems demanding simultaneous functionalities for emergent smart/energy-efficient buildings/cities and autonomous transportation applications. △ Less

Submitted 15 January, 2021; originally announced January 2021.

Comments: 38 Pages, 16 figures

Illusion Mechanisms with Cylindrical Metasurfaces: A General Synthesis Approach

Authors: Mahdi Safari, Hamidreza Kazemi, Ali Abdolali, Mohammad Albooyeh, Filippo Capolino

Abstract: We explore the use of cylindrical metasurfaces in providing several illusion mechanisms including scattering cancellation and creating fictitious line sources. We present the general synthesis approach that leads to such phenomena by modeling the metasurface with effective polarizability tensors and by applying boundary conditions to connect the tangential components of the desired fields to the r… ▽ More We explore the use of cylindrical metasurfaces in providing several illusion mechanisms including scattering cancellation and creating fictitious line sources. We present the general synthesis approach that leads to such phenomena by modeling the metasurface with effective polarizability tensors and by applying boundary conditions to connect the tangential components of the desired fields to the required surface polarization current densities that generate such fields. We then use these required surface polarizations to obtain the effective polarizabilities for the synthesis of the metasurface. We demonstrate the use of this general method for the synthesis of metasurfaces that lead to scattering cancellation and illusion effects, and discuss practical scenarios by using loaded dipole antennas to realize the discretized polarization current densities. This study is the first fundamental step that may lead to interesting electromagnetic applications, like stealth technology, antenna synthesis, wireless power transfer, sensors, cylindrical absorbers, etc. △ Less

Submitted 20 May, 2019; originally announced May 2019.

Comments: 12 pages, 9 figures

Journal ref: Phys. Rev. B 100, 165418 (2019)

Asymmetric Metal-dielectric Meta-cylinders and their Potential Applications from Engineering Scattering Patterns to Spatial Optical Signal Processing

Authors: Ali Momeni, Mahdi Safari, Ali Abdolali, Nazir P. Kherani, Romain Fleury

Abstract: We propose a novel type of bi-anisotropic hybrid metal-dielectric structure comprising dielectric and metallic cylindrical wedges wherein the composite meta-cylinder enables advanced control of electric, magnetic and magnetoelectric resonances. We establish a theoretical framework in which the electromagnetic response of this meta-atom is described through the electric and magneticmultipole moment… ▽ More We propose a novel type of bi-anisotropic hybrid metal-dielectric structure comprising dielectric and metallic cylindrical wedges wherein the composite meta-cylinder enables advanced control of electric, magnetic and magnetoelectric resonances. We establish a theoretical framework in which the electromagnetic response of this meta-atom is described through the electric and magneticmultipole moments. The complete dynamic polarizability tensor, expressed in a compact form, is derived as a function of the Mie scattering coefficients. Flexibility in the design makes the proposed meta-cylinder a viable candidate for various applications in the microscopic (single meta-atom) and macroscopic (metasurface) levels. We show that the highly versatile bi-anisotropic meta-atom is amenable to being designed for the desired electromagnetic response, such as electric dipole-free and zero/near-zero (backward and forward) scattering at the microscopic level. In addition, we show that the azimuthal asymmetry gives rise to normal polarizability components which are vital elements in synthesizing asymmetric Optical Transfer Function (OTF) at the macroscopic level.We conduct a precise inspection, from the microscopic to the macroscopic level, of the metasurface synthesis for emphasizing on the role of normal polarizability components for spatial optical signal processing. It is shown that this simple two-dimensional asymmetric meta-atom can perform first-order differentiation and edge detection at normal illumination.The results reported herein contribute toward improving the physical understanding of wave interaction with artificial materials composed of asymmetric elongated metal-dielectric inclusions and open the potential of its application in spatial signal and image processing. △ Less

Submitted 6 August, 2020; v1 submitted 8 April, 2019; originally announced April 2019.

Comments: 16 pages, 10 figures

Journal ref: Phys. Rev. Applied 15, 034010 (2021)

Design of a Broadband and Polarization Insensitive THz Absorber Based on Two Layers of Periodic Arrays of Graphene Disks

Authors: Omid Mohsen Daraei, Mohammad Eskandari, Kiyanoush Goudarzi, Mir Mahdi Safari, Mohammad Bemani

Abstract: In this paper, we analytically design a simple configuration of a broadband THz and polarization-insensitive absorber. The mentioned absorber consists of two layers of graphene disks, and the transmission line model is considered for the whole of the proposed absorber's structure to design it accurately. Therefore, the input admittance of the designed absorber is obtained by the transmission line… ▽ More In this paper, we analytically design a simple configuration of a broadband THz and polarization-insensitive absorber. The mentioned absorber consists of two layers of graphene disks, and the transmission line model is considered for the whole of the proposed absorber's structure to design it accurately. Therefore, the input admittance of the designed absorber is obtained by the transmission line model. Also, the real part of the input admittance is approximately tuned to be matched to the free space admittance. In contrast, the imaginary part of it is closely adjusted to zero around the central frequency of the THz absorber. Using only just two layers of Periodic Arrays of Graphene Disks (PAGDs) with one kind of dielectric as the material of substrates, it causes that the absorption of the structure can be achieved higher than 90% by the Finite Element Method (FEM). Normalized bandwidth has reached up to 75.4% in 5 THz as the central frequency of the device. As the next step, we use the CST studio software to validate our designed absorber, and it will show that the numerical results will have the best matching with the analytical method. △ Less

Submitted 18 December, 2020; v1 submitted 14 February, 2019; originally announced February 2019.

A novel analytical method for analysis of electromagnetic scattering from inhomogeneous spherical structures using duality principles

Authors: Mohammad Kiani, Ali Abdolali, Mahdi Safari

Abstract: In this article, a novel analytical approach is presented for the analysis of electromagnetic (EM) scattering from radially inhomogeneous spherical structures (RISSs) based on the duality principle. According to the spherical symmetry, similar angular dependencies in all the regions are considered using spherical harmonics. To extract the radial dependency, the system of differential equations of… ▽ More In this article, a novel analytical approach is presented for the analysis of electromagnetic (EM) scattering from radially inhomogeneous spherical structures (RISSs) based on the duality principle. According to the spherical symmetry, similar angular dependencies in all the regions are considered using spherical harmonics. To extract the radial dependency, the system of differential equations of wave propagation toward the inhomogeneity direction is equated with the dual planar ones. A general duality between electromagnetic fields and parameters and scattering parameters of the two structures is introduced. The validity of the proposed approach is verified through a comprehensive example. The presented approach substitutes a complicated problem in spherical coordinate to an easy, well posed, and a previously solved problem in planar geometry. This approach is valid for all continuously varying inhomogeneity profiles. One of the major advantages of the proposed method is the capability of studying two general and applicable types of RISSs. As an interesting application, a new class of lens antenna based on the physical concept of the gradient refractive index material is introduced. The approach is used to analyze the EM scattering from the structure and validate strong performance of the lens. △ Less

Submitted 29 November, 2017; originally announced December 2017.

Comments: 10 pages, 5 figures

Journal ref: Phys. Rev. E 97, 032137 (2018)

Shadow-free multimers as extreme-performance meta-atoms

Authors: M. Safari, M. Albooyeh, C. R. Simovski, S. A. Tretyakov

Abstract: We generalize the concept of parity-time symmetric structures with the goal to create meta-atoms exhibiting extraordinary abilities to overcome the presumed limitations in the scattering of overall lossless particles, such as non-zero forward scattering and the equality of scattering and extinction powers for all lossless particles. Although the forward scattering amplitude and the extinction cros… ▽ More We generalize the concept of parity-time symmetric structures with the goal to create meta-atoms exhibiting extraordinary abilities to overcome the presumed limitations in the scattering of overall lossless particles, such as non-zero forward scattering and the equality of scattering and extinction powers for all lossless particles. Although the forward scattering amplitude and the extinction cross section of our proposed meta-atoms vanish, they scatter incident energy into other directions, with controllable directionality. These meta-atoms possess extreme electromagnetic properties not achievable for passive scatterers. As an example, we study meta-atoms consisting of two or three small dipole scatters. We consider possible microwave realizations in the form of short dipole antennas loaded by lumped elements. The proposed meta-atom empowers extraordinary response of a shadow-free scatterer and theoretically enables most unusual material properties when used as a building block of an artificial medium. △ Less

Submitted 16 November, 2017; v1 submitted 24 October, 2017; originally announced October 2017.

Comments: 14 pages, 9 Figures

Journal ref: Phys. Rev. B 97, 085412 (2018)

Signaling on the Continuous Spectrum of Nonlinear Optical fiber

Authors: Iman Tavakkolnia, Majid Safari

Abstract: This paper studies different signaling techniques on the continuous spectrum (CS) of nonlinear optical fiber defined by nonlinear Fourier transform. Three different signaling techniques are proposed and analyzed based on the statistics of the noise added to CS after propagation along the nonlinear optical fiber. The proposed methods are compared in terms of error performance, distance reach, and c… ▽ More This paper studies different signaling techniques on the continuous spectrum (CS) of nonlinear optical fiber defined by nonlinear Fourier transform. Three different signaling techniques are proposed and analyzed based on the statistics of the noise added to CS after propagation along the nonlinear optical fiber. The proposed methods are compared in terms of error performance, distance reach, and complexity. Furthermore, the effect of chromatic dispersion on the data rate and noise in nonlinear spectral domain is investigated. It is demonstrated that, for a given sequence of CS symbols, an optimal bandwidth (or symbol rate) can be determined so that the temporal duration of the propagated signal at the end of the fiber is minimized. In effect, the required guard interval between the subsequently transmitted data packets in time is minimized and the effective data rate is significantly enhanced. Moreover, by selecting the proper signaling method and design criteria a reach distance of 7100 km is reported by only singling on the CS at a rate of 9.6 Gbps. △ Less

Submitted 21 September, 2017; v1 submitted 18 April, 2017; originally announced April 2017.

Journal ref: Optics Express, vol. 25, issue 16, pages 18685-18702, July 2017

Discrete Fourier Transform Method for Discrimination of Digital Scintillation Pulses in Mixed Neutron-Gamma Fields

Authors: M. J. Safari, F. Abbasi Davani, H. Afarideh, S. Jamili, E. Bayat

Abstract: A Discrete Fourier Transform Method (DFTM) for discrimination between the signal of neutrons and gamma rays in organic scintillation detectors is presented. The method is based on the transformation of signals into the frequency domain using the sine and cosine Fourier transforms in combination with the discrete Fourier transform. The method is largely benefited from considerable differences that… ▽ More A Discrete Fourier Transform Method (DFTM) for discrimination between the signal of neutrons and gamma rays in organic scintillation detectors is presented. The method is based on the transformation of signals into the frequency domain using the sine and cosine Fourier transforms in combination with the discrete Fourier transform. The method is largely benefited from considerable differences that usually is available between the zero-frequency components of sine and cosine and the norm of the amplitude of the DFT for neutrons and gamma-ray signals. Moreover, working in frequency domain naturally results in considerable suppression of the unwanted effects of various noise sources that is expected to be effective in time domain methods. The proposed method could also be assumed as a generalized nonlinear weighting method that could result in a new class of pulse shape discrimination methods, beyond definition of the DFT. A comparison to the traditional Charge Integration Method (CIM), as well as the Frequency Gradient Analysis Method (FGAM) and the Wavelet Packet Transform Method (WPTM) has been presented to demonstrate the applicability and efficiency of the method for real-world applications. The method, in general, shows better discrimination Figure of Merits (FoMs) at both the low-light outputs and in average over the studied energy domain. A noise analysis has been performed for all of the abovementioned methods. It reveals that the frequency domain methods (FGAM and DFTM) are less sensitive to the noise effects. △ Less

Submitted 1 November, 2016; originally announced November 2016.

Comments: 9 pages, 9 figures, 2 tables

Journal ref: IEEE Trans. Nucl. Sci. Vol. 77, No. 1, pp. 325-332, 2016

Differentiation method for localization of Compton edge in organic scintillation detectors

Authors: M. J. Safari, F. Abbasi Davani, H. Afarideh

Abstract: This paper, presents a simple method for accurate calibration of organic scintillation detectors. The method is based on the fact that differentiating the response function leads to accurate estimation of the Compton edge. The differentiation method in addition to the location of the Compton edge, gives insights into the parameters of the folded Gaussian function which is useful for determination… ▽ More This paper, presents a simple method for accurate calibration of organic scintillation detectors. The method is based on the fact that differentiating the response function leads to accurate estimation of the Compton edge. The differentiation method in addition to the location of the Compton edge, gives insights into the parameters of the folded Gaussian function which is useful for determination of the energy resolution. Moreover, it is observed that the uncorrelated noise in the measurement of the response function does not impose significant uncertainties in the evaluations. By simulation of the bounded electrons and considering the Doppler effects, we are able to calculate a first estimation for the intrinsic Doppler resolution of a plastic scintillator, benefiting from the capability of the differentiation method. △ Less

Submitted 25 November, 2016; v1 submitted 28 October, 2016; originally announced October 2016.

Comments: 13 pages, 8 figures

Journal ref: Radiation Physics and Engineering 2020; 1(4), pp. 9-16