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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： M.N. Ullah S. J. Lee D. Innes A. Weisenberger S. Abbaszadeh C. Levin Radiology Stanford University Stanford California United States of America Thomas Jefferson National Accelerator Facility Newport News Virginia United States of America Electrical and Computer Engineering University of California Santa Cruz Santa Cruz California United States of America Electrical Engineering Bioengineering Physics and Molecular Imaging Program at Stanford (MIPS) Stanford University Stanford California United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Utilizing Positron Emission Tomography-Computed Tomography (PET-CT) for three-dimensional imaging of plant dynamic rhizosphere phenomena presents a promising avenue for quantitative analysis of soil structure challenges. Integrating such imaging technology into Earth System Models (ESMs) could enhance the precision of soil structure data. Here, we are reporting the evaluation of PhytoPET, a PET scanner developed at the Thomas Jefferson National Accelerator Facility, designed for studying rhizosphere dynamics. PhytoPET features adaptable PET detector configurations, including 5 cm x 5 cm Hamamatsu H8500 position-sensitive photomultiplier tubes (PMT) coupled to $48 \times 48$ LYSO:Ce scintillator arrays, with individual pixel dimensions of $1 \mathrm{~mm} \times 1 \mathrm{~mm} \times 10 \mathrm{~mm}$. To assess and optimize PhytoPET’s imaging performance, three data sets were acquired and reconstructed for mini-resolution phantom scans. These included scenarios without experimental sensitivity maps, with detector sensitivity map correction using experimental data, and with multi-angle scanning to mitigate limited-angle tomography artifacts present due to the relatively large gaps between detectors. The average peak-to-valley ratio (PVR) for selected line profile data with analytical sensitivity, experimental sensitivity, and experimental sensitivity correction plus multi-angle scanning were measured to be 1.90, 1.93, and 3.0, respectively. Reconstructed images from all scenarios are presented, showing that experimental sensitivity map correction combined with multi-angle scanning outperforms the other two approaches.

关键词： Temperature sensors Semiconductor device measurement Sensitivity Scintillators Statistical analysis Semiconductor detectors Positrons

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Novel Circuit Design for a 3D Position Sensitive Scintillation TOF-PET Detector that Achieves ~100 ps CTR

Novel Circuit Design for a 3D Position Sensitive Scintillati...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Shirin Pourashraf Zhixiang Zhao Derek Innes Andrea Gonzalez-Montoro Joshua W. Cates Craig S. Levin Department of Radiology Stanford University Stanford CA USA Shanghai Jiao Tong University School of Biomedical EngineeringShanghaiPeople's Republic of China Applied Nuclear Physics Program Lawrence Berkeley National Laboratory Berkeley CA USA Departments of Radiology Bioengineering Physics and Electrical Engineering Molecular Imaging Program Stanford University Stanford CA USA

We have built a scalable mixed-signal readout circuit for a 3D position sensitive PET detector that achieves ~100 ps FWHM CTR. For this work we used individual 3×3×3 mm 3 and 3×3×10 mm 3 LGSO crystals, as well as 2×4 arrays of 3×3×10 mm 3 crystals side-coupled to arrays of 3.16×3.16 mm 2 SiPMs. The preliminary results verify that the CPLD is able to resolve 3D interaction positions along the array with < 3 mm positioning resolution along the length of each 10 mm long crystal element. We also present the readout strategy to compensate the CPLD digital noise that otherwise reduces the sensitivity and CTR performance. With this novel strategy we achieved nearly %100 sensitivity recovery as well as ~11 ps improved CTR.

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Non-invasive label-free assessment of contraction induced damage to skeletal muscle structure using second harmonic generation (SHG) microscopy 19

Non-invasive label-free assessment of contraction induced da...

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Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX 2021

作者： Willen, Maeve Helton, Michael Zerafa, Samantha A. Diggs, Alisha B. Young, James R. Brooks, Susan V. Larkin, Lisa M. Mycek, Mary-Ann Biomedical Engineering Department University of Michigan Ann ArborMI United States Applied Physics Program University of Michigan Ann ArborMI United States Bioengineering Department Harvard University CambridgeMA United States Molecular and Integrative Physiology Department University of Michigan Ann ArborMI United States

ISBN: (数字)9781510641303

ISBN: (纸本)9781510641297

Second harmonic generation (SHG) microscopy is a second-order nonlinear optical scattering process that can nondestructively image myosin filament microarchitecture up to 500 μm in depth due to its high spatial resolution, optical sectioning capabilities, and elastic scattering properties. The distribution, alignment, and spatial orientation of myosin filaments largely dictates the mechanical properties of skeletal muscle. Noninvasive quantification of these metrics in-vivo or in-vitro could be indicative of skeletal muscle function and viability. Here, we present a novel algorithm to quantitate sarcomere length with high spatial resolution and accuracy when compared to traditional approaches. Our approach runs in real-time and is less sensitive to instrumental noise and artifacts that shade regions of the image. We have tested this technique on ex-vivo SHG images of rat lumbrical muscles that have been damaged following standardized lengthening contraction protocols. Our approach shows accurate quantitation of sarcomere lengths within 1% compared to manual calculations. This algorithm can be used to reliably analyze SHG images in real-time to assess the structure and function of skeletal muscle tissue in a non-invasive, quantitative, and sterile manner. © 2021 SPIE

关键词： Harmonic generation

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cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America. Department of Bioengineering Rice University Houston Texas United States of America. Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America. Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America. Biophysics Graduate Program University of California Berkeley California United States of America. Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America. Department of Electrical and Computer Engineering Rice University Houston Texas United States of America.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells

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Santa Fe Oxa: Self-Assembled Oxaliplatin Nanomicelle for Enhanced Cascade Cancer Chemotherapy Via Self-Sensitized Ferroptosis

SSRN

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SSRN 2024年

作者： Shi, Jianbin Ma, Wenjing Zheng, Shunzhe Xia, Fengli Liu, Xinying Kikkawa, Ayumi Tanaka, Kaho Kamei, Ken-Ichiro Tian, Chutong Department of Pharmaceutics Wuya College of Innovation Shenyang Pharmaceutical University Shenyang110016 China Kyoto University Kyoto606-8501 Japan Program of Biology Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates Program of Bioengineering Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Biomedical Engineering Tandon School of Engineering New York University MetroTech BrooklynNY11201 United States Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province Hangzhou310058 China

The clinical utility of chemotherapy is often compromised by its limited efficacy and significant side effects. Addressing these concerns, we develop a self-assembled nanomicelle, namely SANTA FE OXA, which is composed of hyaluronic acid (HA) conjugated with ferrocene methanol (FC-OH), oxaliplatin prodrug (OXA(Ⅳ)) and glycol-coupled linoleic acid (EG-LA). Targeted delivery is achieved as HA binds to the CD44 receptors that are overexpressed on tumor cells, facilitating drug uptake. Once internalized, hyaluronidase (HAase) catalyzes the digestions of the SANTA FE OXA, releasing FC and reducing OXA(Ⅳ) into an active form. Active OXA induces DNA damage, while simultaneously promoting intracellular hydrogen peroxide levels via cascade reactions. In parallel, FC disrupts the redox balance within tumor cells, inducing ferroptosis. The synergistic combination of cascade chemotherapy and self-sensitized ferroptosis therapy has demonstrated remarkable anti-cancer efficacy in both in vitro and in vivo models. Moreover, this SANTA FE OXA significantly mitigates the systemic toxicity commonly associated with platinum-based chemotherapeutics. Our findings suggest a compelling advancement in nanomedicine for enhanced cascade cancer therapy. © 2024, The Authors. All rights reserved.

关键词： Chemotherapy

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Disrupted sleep, disrupted metabolism: A potential link between circadian rhythms and tumorigenesis

Brain, Behavior, and Immunity - Integrative

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Brain, Behavior, and Immunity - Integrative 2024年 7卷 100070-100070页

作者： Asif, Sarah Gillette, Martha U. Neuroscience Program University of Illinois at Urbana-Champaign USA Departments of Cell & Developmental Biology Molecular & Integrative Physiology Bioengineering Neuroscience Program Chan Zuckerberg Biohub Chicago Investigator University of Illinois at Urbana-Champaign USA

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Design of Modular Readout Electronics for Cadmium Zinc Telluride (CZT) PET Imaging of the Plants Rhizosphere

Design of Modular Readout Electronics for Cadmium Zinc Tellu...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Muhammad Nasir Ullah Andrew Groll Derek Innes Shiva Abbaszadeh Craig S. Levin Department of Radiology Stanford University California USA Department of Radiology and Molecular Imaging Program at Stanford (MIPS) Stanford University California USA Department of Electrical and Computer Engineering University of California Santa Cruz Santa Cruz California USA Departments of Radiology Electrical Engineering Bioengineering Physics Molecular Imaging Program at Stanford MIPS) Stanford University Stanford California USA

To study and quantify the dynamic behavior of a plant’s soil-root interface (rhizosphere), we are developing a hybrid positron emission tomography-computed tomography (PET-CT) system to obtain in situ 3D images to help in modeling the Earth System Model (ESM) in a precise manner. ESM integrates the relevant aspects of the earth system to study the state of climate under various of conditions. The proposed system aims to understand the complex bi-directional flow of carbon in the rhizosphere by quantitatively measuring 11 C concentration. The rhizosphere imaging requires the creation of a PET system with high sensitivity and excellent spatial resolution; a dual panel geometry of cadmium zinc telluride (CZT) detectors with fine electrodes is being refined and scaled to a 4-panel system to answer the demands. This work focuses on the development, evaluation, and integration of the critical intermediate board (IB) needed to support approximately 5 thousand channels in the CZT PET system and provide modularity to the system. A Rigid-Flex PCB approach is adopted to design the IB. The rigid region will provide firm mechanical support for direct placement of the necessary electronic components and connectors, and the flex region will help to align the subsequent readout boards at the backend of the IB while maintaining the dense packing fraction at front-end where the CZT crystals are connected. Three CZT module (each module comprising of 2-crystals of 39x39 x5 mm 3 sandwiched in anode-cathode-cathode-anode configuration) along with 1-mm thick mechanical support on either side of modules will be directly attached to two IBs which forms one tray to modularize the entire system. This modularized design will help to assemble and dissemble the system in an efficient way and minimize the chance of damaging the crystal assemblies. This new IB design will address the troubleshooting/repairing experience and modularity issues that were taking significant amount of time and energy

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Combining Knee Acoustic Emissions, Patient-Reported Measures, and Machine Learning to Assess Osteoarthritis Severity

Combining Knee Acoustic Emissions, Patient-Reported Measures...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Christopher Nichols H. Trask Crane Dave Ewart Omer T. Inan Bioengineering Graduate Program Georgia Institute of Technology Atlanta GA USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Minneapolis Veterans Affairs Medical Center Minneapolis MN USA Coulter Dept. of Biomedical Engineering School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Current methods for quantifying osteoarthritis severity have limited resolution and accessibility. Patient-recorded outcome measures such as the Knee Injury and Osteoarthritis Outcome Score (KOOS) capture symptom severity, but are subjectively reported and have little correlation with quantifiable metrics of disease such as Kellgren-Lawrence x-ray grade or MRI findings. Knee acoustic emissions (KAEs) offer a convenient, noninvasive option for quantifying joint health. Here, we use machine learning and wearable design to create an interpretable two-stage algorithm for combining KAEs and KOOS scores into an objective, more accessible method of quantifying disease severity. Our algorithm successfully discriminated between early and late-stage osteoarthritis (balanced accuracy = 85%, ROC-AUC = 0.88). The addition of KAEs improved classification of osteoarthritis severity over the use of KAEs (balanced accuracy = 53%, ROC-AUC = 0.786) or KOOS scores alone (balanced accuracy = 63%, ROC-AUC = 0.593). The findings suggest that KAEs combined with patient-recorded metrics can be used to make a more objective and accessible metric for digitally monitoring knee joint health.

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Inter-Modular Synchronization in a TOF-PET System Using the Natural LYSO Background Radioactivity

Inter-Modular Synchronization in a TOF-PET System Using the ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Qian Dong Salar Sajedi Craig S. Levin Departments of Electrical Engineering and Radiology Stanford University Stanford CA USA Departments of Radiology Stanford University Stanford CA USA Department of Radiology Electrical Engineering Bioengineering and Physics Stanford University and Molecular Imaging Program at Stanford (MIPS) Stanford CA USA

Simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) is a promising platform for clinical and research with applications in oncology, neurology, cardiology, and inflammatory diseases. One challenge for simultaneous PET/MRI is the mutual electromagnetic interference between the two imaging modalities. MR-compatibility is required for any system integrating with an MR scanner. It is difficult to achieve both time-of-flight (TOF) capability and MR compatibility using the standard inter-modular synchronization method that uses synchronization signals based on either optical fibers or electrical cables. In this paper, we propose a new method to achieve inter-modular synchronization for PET systems without additional electrical cables or optical fibers by using the natural background radiation present in lutetium-yttrium oxyorthosilicate (LYSO) scintillator crystals. We investigated the feasibility of the approach with two full detector modules comprising 1536 one-to-one coupled 3x3x20mm 3 LYSO crystal-silicon photomultipliers (SiPM) channels. Using the natural LYSO background radiation for synchronization resulted in a CTR of 274.6ps FWHM with a peak location at -13.8ps, compared to the re-calibrated (reference) results with a CTR of 271.1ps FWHM with a peak location at -10.3ps.

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Anterior Cruciate Ligament Classification in Knee MRI using Automated Pseudo-Masking

Anterior Cruciate Ligament Classification in Knee MRI using ...

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Medical Imaging 2023: Computer-Aided Diagnosis

作者： Dadsetan, Saba Albers, Marcio Weinstock, Allison Musahl, Volker Kitamura, Gene Arefan, Dooman Wu, Shandong Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA15213 United States Department of Radiology School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Biomedical Informatics School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Bioengineering School of Engineering University of Pittsburgh PittsburghPA15213 United States Department of Orthopedic Surgery School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Radiology Loma Linda University Medical Center Loma LindaCA92354 United States

ISBN: (纸本)9781510660359

Anterior cruciate ligament (ACL) is one of the most common injuries associated with sports. Knee osseous morphology can play a role in increased knee instability. Our hypothesis is that the morphological features of the knee, as seen in knee osseous morphology, can contribute to increased knee instability and, thus, increase the likelihood of ACL tear. To test this relationship, it is necessary to segment the femur and tibia bones and extract relevant imaging features. However, manual annotation of 3D medical images, such as on magnetic resonance imaging (MRI) scans, can be a time-consuming and challenging task. In this work, we propose an automated pipeline for creating pseudo-masks of the femur and tibia bones in knee MRI. Our approach involves unsupervised segmentation and deep learning models to classify ACL integrity (intact or torn). Our results demonstrate a high agreement between the automated pseudo-masks and a radiologist's manual segmentation, which also leads to comparable AUC values for the ACL integrity classification. © 2023 SPIE.

关键词： Magnetic resonance imaging

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