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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Du, Feng Liu, Shengping Li, Nan Liu, Congcong Chen, Qiaoyan Zhang, Shuai Wang, Haifeng Zhang, Xiaoliang Li, Ye Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guang Dong China Chongqing University of Technology Department of Biomedical Engineering Chongqing China Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCA94158 United States UCSF/UC Berkeley Joint Graduate Group in Bioengineering San FranciscoCA94158 United States

ISBN: (纸本)9781728137261

Radio frequency (RF) coils are critical devices in magnetic resonance (MR) systems for transmitting and receiving magnetic resonance signals. However, the required high operating frequency at ultrahigh magnetic fields makes the design of highly efficient radio frequency (RF) transmit or receive coils technically challenging. This is particularly true in the double-tuned or triple-tuned RF coils usually required in heteronuclear MRI and MRS where multiple nuclei, such as 1H, 19F, and 31P, are involved. The performance of RF coils is a critically important factor for realizing the high sensitivity provided by ultrahigh magnetic fields to contribute towards the advancement of a novel human in vivo application at 9.4T. In this work, two different configurations of novel triple-nuclear (1H/19F/31P) RF resonators were proposed for the ultrahigh field 9.4T applications. We also reported on the design and construction, and a field-circuit co-simulation was used to obtain the S-parameters, B1+ field and specific absorption rate (SAR) distributions to evaluate and compare their performance. And the characteristics and advantages of the proposed two different configurations of triple-nuclear RF resonators in the applications of 1H/19F/31P at ultrahigh magnetic field were investigated. © 2019 IEEE.

关键词： Magnetic resonance

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A comparison of coil combination strategies in 3D multi-channel MRSI reconstruction for patients with brain tumors (vol 31, e3929, 2018)

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NMR IN BIOMEDICINE 2018年第12期31卷 e3929-e3929页

作者： Vareth, M. Lupo, J. M. Larson, P. E. Z. Nelson, S. J. UC Berkeley-UCSF Graduate Program in Bioengineering University of California Berkeley and University of California San Francisco California USA Surbeck Laboratory of Advanced Imaging Department of Radiology and Biomedical Imaging University of California San Francisco California USA UC Berkeley-UCSF Graduate Program in Bioengineering University of California Berkeley and University of California San Francisco California USA Surbeck Laboratory of Advanced Imaging Department of Radiology and Biomedical Imaging University of California San Francisco California USA UC Berkeley-UCSF Graduate Program in Bioengineering University of California Berkeley and University of California San Francisco California USA Surbeck Laboratory of Advanced Imaging Department of Radiology and Biomedical Imaging University of California San Francisco California USA UC Berkeley-UCSF Graduate Program in Bioengineering University of California Berkeley and University of California San Francisco California USA Surbeck Laboratory of Advanced Imaging Department of Radiology and Biomedical Imaging University of California San Francisco California USA

The goal of this study was to find the most robust algorithm for a phase‐sensitive coil combination of 3D single‐cycle and lactate‐edited, multi‐channel H‐1 point‐resolved spectroscopy (PRESS) localized echo planar spectroscopic imaging (EPSI) data for clinical applications in the brain. Data were acquired over 5-10 minutes at 3T using 8‐ or 32‐channel array coils. Peak referencing with residual water and N‐acetyl‐aspartate, first‐point phasing, generalized least squared (GLS) and whitened singular‐value decomposition (WSVD) combination algorithms were evaluated relative to unsuppressed water with data from a phantom, six volunteers and 55 patients with brain tumors. Comparison metrics were signal‐to‐noise ratio, coefficient of variance and percent signal increase. Where residual water was present, using it as a reference peak for phasing and weighting factors from an imaging calibration scan gave the best overall performance. Greater improvement was seen for large selected volumes (>720 cm³) and for the 32‐channel array (25%) compared with the 8‐channel array (19%). Applying voxel‐by‐voxel phase corrections produced a larger increase in performance for the 32‐ versus 8‐channel coil. We conclude that, for clinically relevant 3D H‐1 PRESS localized EPSI studies, the most robust technique employed individual phase maps generated from high residual water and individual amplitude maps generated from calibration scans.

关键词： coil combination techniques first‐point phasing GLS lactate editing multichannel EPSI peak referencing PRESS localization WSVD

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Performance Evaluation and Comparison of two Triple-nuclear RF Resonators for 1H/19F/31P MR Imaging at 9.4T

Performance Evaluation and Comparison of two Triple-nuclear ...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Feng Du Shengping Liu Nan Li Congcong Liu Qiaoyan Chen Shuai Zhang Haifeng Wang Xiaoliang Zhang Ye Li Chongqing University of Technology Chongqing Chongqing China Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guang Dong China UCSF/UC Berkeley Joint Graduate Group in Bioengineering San Francisco CA USA

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Radio frequency (RF) coils are critical devices in magnetic resonance (MR) systems for transmitting and receiving magnetic resonance signals. However, the required high operating frequency at ultrahigh magnetic fields makes the design of highly efficient radio frequency (RF) transmit or receive coils technically challenging. This is particularly true in the double-tuned or triple-tuned RF coils usually required in heteronuclear MRI and MRS where multiple nuclei, such as 1 H, 19 F, and 31 P, are involved. The performance of RF coils is a critically important factor for realizing the high sensitivity provided by ultrahigh magnetic fields to contribute towards the advancement of a novel human in vivo application at 9.4T. In this work, two different configurations of novel triple-nuclear ( 1 H/ 19 F/ 31 P) RF resonators were proposed for the ultrahigh field 9.4T applications. We also reported on the design and construction, and a field-circuit co-simulation was used to obtain the S-parameters, B 1 + field and specific absorption rate (SAR) distributions to evaluate and compare their performance. And the characteristics and advantages of the proposed two different configurations of triple-nuclear RF resonators in the applications of 1 H/ 19 F/ 31 P at ultrahigh magnetic field were investigated.

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Novel techniques for large-scale manipulations of cortical networks in non-human primates

Novel techniques for large-scale manipulations of cortical n...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Azadeh Yazdan-Shahmorad Daniel B. Silversmith Philip N. Sabes University of California San Francisco San Francisco CA US UC Berkeley – UCSF Graduate Program in Bioengineering San Francisco CA 94158 USA

Optogenetics is a powerful tool that enables millisecond-level control of the activity of specific groups of neurons. Furthermore, it has the great advantage of artifact free recordings. These characteristics make this technique ideal for relating brain function to behavior in animals with great behavioral capabilities such as non-human primates (NHPs). We recently introduced a practical, stable interface for optogenetic stimulation and recording of large-scale cortical circuits in NHPs. Here we present the various potentials of this interface for studying circuits and connectivity at a large-scale and for relating it to behavior.

关键词： Frequency modulation Oscillators Time series analysis Couplings Electrical stimulation

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Predictive Modeling Leveraging Electronic Health Records Identifies Early Predictors of Alzheimer’s Disease

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Alzheimer's & Dementia 2023年第S15期19卷

作者： Alice S Tang Katherine P Rankin Gabriel Cerano Silvia Miramontes Hunter Mills Tomiko Oskotsky Billy Zeng Charlotte Nelson Karthik Soman Sarah Woldemariam Yaqiao Li Albert M Lee Zachary Miller Isabel Elaine Allen Stephan Sanders Sergio Baranzini Marina Sirota Graduate Program in Bioengineering University of California San Francisco and Berkeley San Francisco CA USA Bakar Computational Health Sciences Institute University of California San Francisco San Francisco CA USA Correspondece Memory and Aging Center UCSF Weill Institute for Neurosciences University of California San Francisco San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA USA Department of Paediatrics University of Oxford Oxford United Kingdom Department of Pediatrics University of California San Francisco San Francisco CA USA

Background Alzheimer’s Disease (AD) is a neurodegenerative disorder of aging that is difficult to diagnose. Electronic health records (EHR) are a valuable source of longitudinal data for disease phenotyping and prediction. Knowledge graphs, such as SPOKE (Scalable Precision Medicine Open Knowledge Engine), help derive biological meaning from phenotypic data. Here we utilize machine learning on EHR to predict and better understand AD. Method AD patients and controls were identified from the ucsf EHR. The index time was identified as the first dementia diagnosis or drug record among the AD patients, and 1 year before the last visit among controls. Controls matched on demographics and visit-related factors were identified at five time points relative to the index time (-7years, -5years, -3years, -1year, and -1day) at a 1:8 ratio. Random forest (RF) classification models for AD were trained utilizing clinical data (conditions, drugs, abnormal measurements) and evaluated with bootstrapped AUROCs on a 30% held-out set. Top features mapped to phecodes were ranked based on average impurity decrease. Networks were created from SPOKE knowledge graph by identifying shortest paths between top 25 model features and AD, and associations were validated with genetic colocalization analysis on shared variants in the UK Biobank. Result From the ucsf EHR, 749 AD patients and 250,545 Controls were identified with clinical concepts 7 years prior to each individual’s index time. RF trained on AD and matched control individuals’ data performed with AUROC of 0.58 (-7years) to 0.77 (-1day). Top phecode features from models include those that are important earlier (osteoarthritis, allergic rhinitis), progress in importance closer to index time (dizziness, vitamin D deficiency, memory loss), and persist in importance throughout time (hyperlipidemia) (Figure 1). Knowledge networks highlight shared biological relationships between top features (e.g., hyperlipidemia, osteoporosis), including ide

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Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies

arXiv

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arXiv 2023年

作者： Larson, Peder E.Z. Bernard, Jenna M.L. Bankson, James A. Bøgh, Nikolaj Bok, Robert A. Chen, Albert P. Cunningham, Charles H. Gordon, Jeremy Hövener, Jan-Bernd Laustsen, Christoffer Mayer, Dirk McLean, Mary A. Schilling, Franz Slater, James Vanderheyden, Jean-Luc von Morze, Cornelius Vigneron, Daniel B. Xu, Duan Hansen, Esben Søvsø Szocska Miller, Jack Bertelsen, Lotte Bonde Vaeggemose, Michael Lin, Gigin Capozzi, Andrea Fuetterer, Max Chen, Albert Leftin, Avi Schulte, Rolf Rius, Irene Marco Booth, Thomas C. Mishkovsky, Mor Walker, Christopher Michael Keshari, Kayvan Woitek, Ramona Takado, Yuhei Vasquez, Crystal Müller, Christoph Floyd, Joel Grashei, Martin Topping, Geoff van Heijster, Frits Che, Jenny Kim, Yaewon Kurhanewicz, John Ohliger, Michael Sriram, Renuka Chaumeil, Myriam Bock, Michael Schmidt, Andreas Gallagher, Ferdia Chowdhury, Rafat Gong, Yangcan Punwani, Shonit Merritt, Matthew Grist, James Tyler, Damian Khashami, Fatemeh Malloy, Craig Park, Jae Mo Zaha, Vlad Department of Radiology and Biomedical Imaging University of California San FranciscoCA94143 United States UC Berkeley-UCSF Graduate Program in Bioengineering University of California San Francisco United States University of California BerkeleyCA94143 United States Department of Imaging Physics MD Anderson Medical Center HoustonTX United States The MR Research Center Department of Clinical Medicine Aarhus University Aarhus Denmark GE Healthcare Menlo ParkCA United States Sunnybrook Research Institute TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Kiel University Am Botanischen Garten 14 Kiel24118 Germany Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine BaltimoreMD United States Greenebaum Cancer Center University of Maryland School of Medicine BaltimoreMD United States Department of Radiology University of Cambridge Cambridge United Kingdom Cancer Research UK Cambridge Institute University of Cambridge Li Ka Shing Center Cambridge United Kingdom Department of Nuclear Medicine School of Medicine Klinikum Rechts der Isar Technical University of Munich Munich81675 Germany JLVMI Consulting LLC DousmanWI United States Department of Radiology Washington University St. LouisMO United States Aarhus University Aarhus Denmark Chang Gung Memorial Hospital Taoyuan Taiwan Technical University of Denmark Copenhagen Denmark ETH Zurich Zurich Switzerland GE Healthcare WaukeshaWI United States Institute for Bioengineering of Catalonia Barcelona Spain Kings College London London United Kingdom École Polytechnique Fédérale de Lausanne Lausanne Switzerland MD Anderson Cancer Center HoustonTX United States Memorial Sloan Kettering Cancer Center New YorkNY United States Danube Private University Kerns-Stein an der Donau Austria National Institutes for Quantum Science and Technology Chiba Japan National Institutes Health National Cancer Institute Bethe

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the "HP 13C MRI Consensus Group" as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Correction to: Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network

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Journal of digital imaging 2019年第5期32卷 898页

作者： Yicheng Chen Javier E Villanueva-Meyer Melanie A Morrison Janine M Lupo UCSF-UC Berkeley Graduate Program in Bioengineering San Francisco USA. Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco USA. UCSF-UC Berkeley Graduate Program in Bioengineering San Francisco USA. Janine.Lupo@ucsf.edu. Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco USA. Janine.Lupo@ucsf.edu.

This paper was published inadvertently as open access. It has been corrected online.

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Publisher Correction: Modular and tunable biological feedback control using a de novo protein switch

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Nature 2020年第7798期579卷 E8页

作者： Andrew H Ng Taylor H Nguyen Mariana Gómez-Schiavon Galen Dods Robert A Langan Scott E Boyken Jennifer A Samson Lucas M Waldburger John E Dueber David Baker Hana El-Samad Department of Biochemistry and Biophysics University of California San Francisco San Francisco CA USA. Department of Bioengineering University of California Berkeley Berkeley CA USA. The UC Berkeley-UCSF Graduate Program in Bioengineering UC Berkeley Berkeley CA USA. Cell Design Initiative University of California San Francisco CA USA. Department of Biochemistry University of Washington Seattle WA USA. Institute for Protein Design University of Washington Seattle WA USA. Graduate Program in Biological Physics Structure and Design University of Washington Seattle WA USA. Howard Hughes Medical Institute University of Washington Seattle WA USA. Department of Biochemistry and Biophysics University of California San Francisco San Francisco CA USA. hana.el-samad@ucsf.edu. Cell Design Initiative University of California San Francisco CA USA. hana.el-samad@ucsf.edu. Chan-Zuckerberg Biohub San Francisco CA USA. hana.el-samad@ucsf.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Ten simple rules for reproducible research in jupyter notebooks

arXiv

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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Drug Delivery: Injectable Drug‐Releasing Microporous Annealed Particle Scaffolds for Treating Myocardial Infarction (Adv. Funct. Mater. 43/2020)

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Advanced Functional Materials 2020年第43期30卷

作者： Jun Fang Jaekyung Koh Qizhi Fang Huiliang Qiu Maani M. Archang Mohammad Mahdi Hasani‐Sadrabadi Hiromi Miwa Xintong Zhong Richard Sievers Dong‐Wei Gao Randall Lee Dino Di Carlo Song Li Department of Bioengineering University of California Los Angeles CA 90095 USA Department of Medicine University of California Los Angeles CA 90095 USA Department of Medicine Cardiovascular Research Institute and Institute for Regeneration Medicine University of California San Francisco CA 94143 USA California NanoSystems Institute (CNSI) University of California Los Angeles CA 90095 USA UC Berkeley‐UCSF Graduate Program in Bioengineering University of California San Francisco San Francisco CA 94158 USA Department of Mechanical and Aerospace Engineering University of California Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Centre University of California Los Angeles CA 90024 USA

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