Stephan Anderson
· Associate Professor (BUSM, ME)VerifiedBoston University · Chemical Engineering
Active 1978–2026
About
Stephan Anderson, PhD, is an Associate Professor of Radiology at Boston University School of Medicine and a faculty member in the College of Engineering. He holds a PhD from the University of Maryland, Baltimore. Since joining the Department of Radiology at Boston University Medical School, Dr. Anderson has focused his research efforts on the quantitative imaging of liver disease. He has collaborated with Professor Xin Zhang to apply engineering strategies to biomedical applications. His work integrates engineering principles with biomedical research to advance understanding and imaging of liver conditions.
Research topics
- Computer Science
- Acoustics
- Physics
- Medicine
- Materials science
- Engineering
- Optoelectronics
- Telecommunications
- Electrical engineering
- Radiology
- Artificial Intelligence
- Medical physics
- Optics
- Internal medicine
- Chemistry
- Composite material
- Endocrinology
- Pathology
- Electronic engineering
- Biochemistry
- Embedded system
- Pharmacology
- Nuclear medicine
Selected publications
Advanced Materials · 2026-04-18
articleCorrespondingABSTRACT The signal‐to‐noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images. Recent advances in metamaterials have enabled lightweight solutions with selective magnetic responses, offering a route to locally boost SNR in targeted anatomical regions but often with compromised field homogeneity. Here, a wireless metamaterial cage constructed from coaxial cables is engineered for homogeneous SNR enhancement at 3.0 T. With its cylindrical geometry and electromagnetic architecture, the device supports circularly polarized resonance through engineered phase‐shifted currents, enabling selective and omnidirectional interaction with the rotating field to achieve a uniform magnetic field distribution. Integrated with the Birdcage coil (BC), the device yields a 31.45‐fold SNR enhancement while maintaining comparable homogeneity to the BC alone, exhibiting only 12.07% variation within the region of interest (ROI). Benchmarking against a state‐of‐the‐art 16‐channel extremity coil further shows that the metacage achieves at least 1.94‐fold and 2.24‐fold higher SNR in axial and coronal planes, respectively, and exhibits substantially lower SNR variation (12.07% compared to 54.83% for the extremity coil). The results establish the metacage as a compelling platform for next‐generation wireless MRI technologies.
arXiv (Cornell University) · 2026-01-23
preprintOpen accessThe signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images. Recent advances in metamaterials have enabled lightweight solutions with selective magnetic responses, offering a route to locally boost SNR in targeted anatomical regions but often with compromised field homogeneity. Here, a wireless metamaterial cage constructed from coaxial cables is engineered for homogeneous SNR enhancement at 3.0 T. With its cylindrical geometry and electromagnetic architecture, the device supports circularly polarized resonance through engineered phase-shifted currents, enabling selective and omnidirectional interaction with the rotating B_1^- field to achieve uniform magnetic field distribution. Integrated with the body coil, the device yields a 32-fold SNR enhancement while maintaining comparable homogeneity to the body coil alone, exhibiting only 12.07% variation within the region of interest (ROI). Benchmarking against a state-of-the-art 16-channel extremity coil further shows that the metacage achieves at least 1.94-fold and 2.24-fold higher SNR in axial and coronal planes, respectively, and exhibits substantially lower SNR variation (12.07% compared to 54.83% for the extremity coil). The results establish the metacage as a compelling platform for next-generation wireless MRI technologies.
ArXiv.org · 2026-01-23
articleOpen accessThe signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images. Recent advances in metamaterials have enabled lightweight solutions with selective magnetic responses, offering a route to locally boost SNR in targeted anatomical regions but often with compromised field homogeneity. Here, a wireless metamaterial cage constructed from coaxial cables is engineered for homogeneous SNR enhancement at 3.0 T. With its cylindrical geometry and electromagnetic architecture, the device supports circularly polarized resonance through engineered phase-shifted currents, enabling selective and omnidirectional interaction with the rotating B_1^- field to achieve uniform magnetic field distribution. Integrated with the body coil, the device yields a 32-fold SNR enhancement while maintaining comparable homogeneity to the body coil alone, exhibiting only 12.07% variation within the region of interest (ROI). Benchmarking against a state-of-the-art 16-channel extremity coil further shows that the metacage achieves at least 1.94-fold and 2.24-fold higher SNR in axial and coronal planes, respectively, and exhibits substantially lower SNR variation (12.07% compared to 54.83% for the extremity coil). The results establish the metacage as a compelling platform for next-generation wireless MRI technologies.
Magnetic resonance image processing transformer for general accelerated image restoration
Scientific Reports · 2025-11-17 · 3 citations
articleOpen accessRecent advancements in deep learning have enabled the development of generalizable models that achieve state-of-the-art performance across various imaging tasks. Vision Transformer (ViT)-based architectures, in particular, have demonstrated strong feature extraction capabilities when pre-trained on large-scale datasets. In this work, we introduce the Magnetic Resonance Image Processing Transformer (MR-IPT), a ViT-based image-domain framework designed to enhance the generalizability and robustness of accelerated MRI restoration. Unlike conventional deep learning models that require separate training for different acceleration factors, MR-IPT is pre-trained on a large-scale dataset encompassing multiple undersampling patterns and acceleration settings, enabling a unified framework. By leveraging a shared transformer backbone, MR-IPT effectively learns universal feature representations, allowing it to generalize across diverse restoration tasks. Extensive experiments demonstrate that MR-IPT outperforms both CNN-based and existing transformer-based methods, achieving superior quality across varying acceleration factors and sampling masks. Moreover, MR-IPT exhibits strong robustness, maintaining high performance even under unseen acquisition setups, highlighting its potential as a scalable and efficient solution for accelerated MRI. Our findings suggest that transformer-based general models can significantly advance MRI restoration, offering improved adaptability and stability compared to traditional deep learning approaches.
Advanced Science · 2025-01-31 · 4 citations
articleOpen accessBattery-free wireless sensing in extreme environments, such as conductive solutions, is crucial for long-term, maintenance-free monitoring, eliminating the limitations of battery power and enhancing durability in hard-to-reach areas. However, in such environments, the efficiency of wireless power transfer via radio frequecny (RF) energy harvesting is heavily compromised by signal attenuation and environmental interference, which degrade antenna quality factors and detune resonance frequencies. These limitations create substantial challenges in wirelessly powering miniaturized sensor nodes for underwater environmental monitoring. To overcome these challenges, electrically-shielded coils with coaxially aligned dual-layer conductors are introduced that confine the electric field within the coil's inner capacitance. This configuration mitigates electric field interaction with the surrounding medium, making the coils ideal for use as near-field antennas in aquatic applications. Leveraging these electrically-shielded coils, a metamaterial-enhanced reader antenna was developed and a 3-axis sensor antenna for an near-field communication (NFC)-based system. The system demonstrated improved spectral stability, preserving resonance frequency and maintaining a high-quality factor. This advancement enabled the creation of a battery-free wireless sensing platform for real-time environmental monitoring in underwater environments, even in highly conductive saltwater with salinity levels of up to 3.5%.
Advanced Science · 2025-04-01
articleOpen accessBattery‐free Wireless Sensing System The cover image features a battery‐free wireless sensing system for environmental monitoring in conductive underwater environments. In article number 2414299, Xin Zhang, Ke Wu, Xia Zhu, and Stephan Anderson introduce electrically‐shielded coils with dual‐layer conductors to reduce interference and enhance antenna stability. Integrated with a metamaterial‐enhanced reader antenna and a 3‐axis sensor antenna, this NFC‐based system enables real‐time saltwater monitoring. [Image: see text]
Regularization by neural style transfer for MRI field-transfer reconstruction with limited data
Frontiers in Artificial Intelligence · 2025-06-18 · 1 citations
articleOpen accessRecent advances in MRI reconstruction have demonstrated remarkable success through deep learning-based models. However, most existing methods rely heavily on large-scale, task-specific datasets, making reconstruction in data-limited settings a critical yet underexplored challenge. While regularization by denoising (RED) leverages denoisers as priors for reconstruction, we propose Regularization by Neural Style Transfer (RNST), a novel framework that integrates a neural style transfer (NST) engine with a denoiser to enable magnetic field-transfer reconstruction. RNST generates high-field-quality images from low-field inputs without requiring paired training data, leveraging style priors to address limited-data settings. Our experiment results demonstrate RNST's ability to reconstruct high-quality images across diverse anatomical planes (axial, coronal, sagittal) and noise levels, achieving superior clarity, contrast, and structural fidelity compared to lower-field references. Crucially, RNST maintains robustness even when style and content images lack exact alignment, broadening its applicability in clinical environments where precise reference matches are unavailable. By combining the strengths of NST and denoising, RNST offers a scalable, data-efficient solution for MRI field-transfer reconstruction, demonstrating significant potential for resource-limited settings.
Phase gradient ultra open metamaterials for broadband acoustic silencing
Scientific Reports · 2025-07-01 · 1 citations
articleOpen accessNoise pollution is a persistent environmental concern with severe implications for human health and resources. Acoustic metamaterials offer the potential for thin silencing devices; however, existing designs often lack practical openness and are thereby limited by their functional bandwidths. This paper introduces a novel approach utilizing a phase gradient ultra-open metamaterial (PGUOM) to address these challenges. The PGUOM, characterized by a phase gradient across three unit cells, efficiently transforms incident waves into spoof surface waves, effectively blocking sound while allowing for a high degree of ventilation. Our design provides adjustable openness, accommodates various boundary conditions, and ensures sustained broadband sound insulation. Theoretical, numerical, and experimental validations demonstrate the efficacy of our concept. This innovative approach represents a significant advancement in ventilated acoustic metamaterials, providing both ventilation and high-performance, broadband sound insulation simultaneously.
CT imaging of clinically significant abdominopelvic injuries in the damage control surgery patient
Emergency Radiology · 2024-10-15
articleA robust near-field body area network based on coaxially-shielded textile metamaterial
arXiv (Cornell University) · 2024-02-21
preprintOpen accessA body area network (BAN) involving wearable sensors populated around the human body can continuously monitor physiological signals, finding applications in personal healthcare and athletic evaluation. Existing near-field communication (NFC)-enabled BAN solutions, while facilitating reliable and secure interconnection among battery-free sensors, face challenges such as limited spectral stability against external interference. Here we demonstrate a textile metamaterial featuring a coaxially-shielded internal structure designed to mitigate interference from extraneous loadings. The metamaterial can be patterned onto clothing to form a scalable, customizable network, enabling communication between NFC-enabled devices and developed battery-free textile NFC sensing nodes placed within the network. Proof of concept demonstration shows the metamaterial's robustness against mechanical deformation and exposure to lossy, conductive saline solutions, underscoring its potential applications in wet environments, particularly in athletic activities involving water or significant perspiration, offering insights for the future development of radio frequency components for a robust BAN at the system level.
Recent grants
Metamaterial-Enabled magnetic Resonance Imaging Enhancement
NIH · $660k · 2018–2023
Frequent coauthors
- 474 shared
Jorge A. Soto
Boston Medical Center
- 155 shared
Christina A. LeBedis
Boston University
- 99 shared
Hernán Jara
University of Southern California
- 88 shared
James T. Rhea
- 62 shared
Brian C. Lucey
Galway Clinic
- 60 shared
David D. B. Bates
- 58 shared
Jennifer W. Uyeda
Brigham and Women's Hospital
- 57 shared
Xiaoguang Zhao
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