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5. Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps

Author

Zhang, Junhao, Yi, Zheyuan, Zhao, Yujiao, Xiao, Linfang, Hu, Jiahao, Man, Christopher, Lau, Vick, Su, Shi, Chen, Fei, Leong, Alex T. L., and Wu, Ed X.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. Results: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data.

Published
2022

6. Robust Electromagnetic Interference (EMI) Elimination via Simultaneous Sensing and Deep Learning Prediction for RF Shielding-free MRI

Author

Zhao, Yujiao, Xiao, Linfang, Lau, Vick, Liu, Yilong, Leong, Alex T., and Wu, Ed X.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Physics - Instrumentation and Detectors
Abstract

At present, MRI scans are performed inside a fully-enclosed RF shielding room, posing stringent installation requirement and unnecessary patient discomfort. We aim to develop an electromagnetic interference (EMI) cancellation strategy for MRI with no or incomplete RF shielding. In this study, a simultaneous sensing and deep learning driven EMI cancellation strategy is presented to model, predict and remove EMI signals from acquired MRI signals. Specifically, during each MRI scan, separate EMI sensing coils placed in various spatial locations are utilized to simultaneously sample environmental and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). Then a CNN model is trained using the EMI characterization data to relate EMI signals detected by EMI sensing coils to EMI signals in MRI receive coil. This model is utilized to retrospectively predict and remove EMI signals components detected by MRI receive coil during the MRI signal acquisition window. We implemented and demonstrated this strategy for various EMI sources on a mobile ultra-low-field 0.055 T permanent magnet MRI scanner and a 1.5 T superconducting magnet MRI scanner with no or incomplete RF shielding. Our experimental results demonstrate that the method is highly effective and robust in predicting and removing various EMI sources from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (64 MHz), producing final image signal-to-noise ratios that are comparable to those obtained using a fully enclosed RF shielding. Our proposed strategy enables MRI operation with no or incomplete RF shielding, alleviating MRI installation and operational requirements. It is also potentially applicable to other scenarios of accurate RF signal detection or discrimination in presence of external and internal EMI or RF sources., Comment: Submitted to NMR in Biomedicine

Published
2022

20. Early cellular and molecular signatures correlate with severity of West Nile virus infection

Author

Lee, Ho-Joon, Zhao, Yujiao, Fleming, Ira, Mehta, Sameet, Wang, Xiaomei, Wyk, Brent Vander, Ronca, Shannon E., Kang, Heather, Chou, Chih-Hung, Fatou, Benoit, Smolen, Kinga K., Levy, Ofer, Clish, Clary B., Xavier, Ramnik J., Steen, Hanno, Hafler, David A., Love, J. Christopher, Shalek, Alex K., Guan, Leying, Murray, Kristy O., Kleinstein, Steven H., and Montgomery, Ruth R.

Published
2023
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21. New energy exploitation in coal-endowed areas under the target of 'double carbon': a new path for transformation and upgrading of coal mines in the future

Author

WANG Shuangming, LIU Lang, ZHAO Yujiao, ZHANG Bo, WANG Jingyu, ZHU Mengbo, WANG Mei, and ZHANG Xiaoyan

Subjects
“double carbon” target, full-life-cycle, new energy development, energy storage in goaf, multi-energy complementation, Mining engineering. Metallurgy, TN1-997
Abstract

Under the “dual carbon” goal, China’s energy structure is accelerating transformation and upgrading, and new energy represented by solar energy, wind energy, geothermal energy is gradually replacing traditional coal, oil, gas and other fossil energy. Promoting the optimal combination of coal and new energy is a new requirement for achieving the “double carbon” goal, and also an important path for the green and low-carbon transformation of coal mines in the future. Based on the analysis of the energy distribution characteristics of coal-endowed areas in China, it is found that the fossil energy distribution areas dominated by coal are often also areas rich in solar energy, wind energy, thermal energy and other new energy, which are suitable for large-scale development of new energy in in coal-endowed areas. The research results are as follows: ① Put forward the concept of full life cycle energy development in coal-endowed areas, divide the energy development process in coal-endowed areas into three stages: coal, coal and new energy optimization combination, and new energy, and build a general framework for new energy development in coal-endowed areas; ② In terms of solar energy development, the theoretical reserves and development potential of solar energy in the mining area are evaluated based on the overlapping relationship between the coal occurrence and the solar energy resource rich area, and a hierarchical comprehensive utilization scheme of solar energy coupled with photochemical utilization, such as distributed/centralized photovoltaic power generation, solar thermal utilization and solar hydrogen production, is proposed to achieve diversified and efficient conversion and utilization of solar energy and collaborative mining with coal resources; ③ In terms of geothermal energy development, the potential of geothermal energy development in coal-endowed areas is evaluated based on the overlapping relationship between the coal bearing area and the geothermal energy resource rich area. In combination with the formation mechanism, distribution law and mining situation of the mine geothermal, a collaborative development system of deep mineral resources and geothermal resources is established to form a comprehensive application scheme for the graded mining of mine geothermal energy and the cascade utilization of thermal users, including the hot water type, rock temperature type and mixed type of the mine, realizing the strategic transfer of “harm” to “benefit” of mine geothermal; ④ In terms of wind energy development, the potential of wind energy development in the mining area is evaluated based on the overlapping relationship between the coal occurrence and the wind energy resource rich area, and the application approaches of wind power generation, wind water lifting, and wind heating technologies in the coal bearing area are proposed, and the wind energy utilization framework for power supply, water cycle power, heating self-sufficiency, and grid connected power generation of underground and underground facilities is constructed; ⑤ Considering that the roof is generally managed by the caving method in the current underground coal mining, and the caving space cannot be effectively used for underground energy storage, a technical path of energy storage in the goaf of the mine is proposed, which is “advanced planning of energy storage reservoir → construction of energy storage reservoir using functional backfilling→ Energy storage and release operation control”; ⑥ In view of the complex and changeable characteristics of multi energy complementary integrated energy system (MCIES) in coal-endowed areas, a heterogeneous energy flow coupling model of energy production and supply units in coal-endowed areas is established, the mathematical expressions of different energy hubs in MCIES are unified, and the energy management and optimization logic method and operation mode of MCIES in coal-endowed areas are proposed. This paper aims to explore the combination path of coal and new energy, demonstrate the feasibility of large-scale development of new energy in coal-endowed areas, endow coal bearing areas with the function of energy supply in the whole life cycle, and form a new mode of energy industry development of coal enterprises integrating “mine-wind-light-heat-storage”.

Published
2023
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22. Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

Author

Abraham, James, Adkisson, Michael, Albert, Marisa, Altamirano, Luz Torres, Alvarenga, Bonny, Anderson, Matthew L., Anderson, Evan J., Arnett, Azlann, Asashima, Hiromitsu, Atkinson, Mark A., Baden, Lindsey R., Barton, Brenda, Beach, Katherine, Beagle, Elizabeth, Becker, Patrice M., Bell, Matthew R., Bernui, Mariana, Bime, Christian, Boddapati, Arun Kumar, Booth, J. Leland, Borresen, Brittney, Brakenridge, Scott C., Bristow, Laurel, Bryant, Robert, Calfee, Carolyn S., Carreño, Juan Manuel, Carrillo, Sidney, Chak, Suzanna, Chang, Iris, Connors, Jennifer, Conway, Michelle, Corry, David B., Cowan, David, Croen, Brett, Dela Cruz, Charles S., Cusimano, Gina, Eaker, Lily, Edwards, Carolyn, Ehrlich, Lauren I.R., Elashoff, David, Erickson, Heidi, Erle, David J., Farhadian, Shelli, Farrugia, Keith, Fatou, Benoit, Fernandes, Andrea, Fernandez-Sesma, Ana, Fragiadakis, Gabriela K., Furukawa, Sara, Geltman, Janelle N., Ghale, Rajani, Bermúdez González, Maria Carolina, Goonewardene, I. Michael, Guerrero, Estella Sanchez, Guirgis, Faheem W., Hafler, David A., Hamilton, Sydney, Harris, Paul, Hayati, Arash Nemati, Hendrickson, Carolyn M., Agudelo Higuita, Nelson I., Hodder, Thomas, Holland, Steven M., Hough, Catherine L., Huerta, Christopher, Hurley, Kerin C., Hutton, Scott R., Iwasaki, Akiko, Jauregui, Alejandra, Jha, Meenakshi, Johnson, Brandi, Joyner, David, Kangelaris, Kirsten N., Kelly, Geoffrey, Khalil, Zain, Khan, Zenab, Kheradmand, Farrah, Kim, James N., Kimura, Hiroki, Ko, Albert I., Kohr, Bernard, Kraft, Monica, Krummel, Matthew, Kutzler, Michele A., Lasky-Su, Jessica, Lee, Serena, Lee, Deanna, Leipold, Michael, Lentucci, Claudia, Leroux, Carolyn, Lin, Edward, Liu, Shanshan, Love, Christina, Lu, Zhengchun, Maliskova, Lenka, Manning, Brittany Roth, Manohar, Monali, Martens, Mark, McComsey, Grace A., McEnaney, Kerry, McLin, Renee, Melamed, Esther, Melnyk, Nataliya, Mendez, Kevin, Messer, William B., Metcalf, Jordan P., Michelotti, Gregory, Mick, Eran, Mohanty, Subhasis, Mosier, Jarrod, Mulder, Lubbertus C.F., Murphy, Maimouna, Nadeau, Kari R.C., Nelson, Ebony, Nelson, Allison, Nguyen, Viet, Oberhaus, Jordan, Panganiban, Bernadine, Pellegrini, Kathryn L., Pickering, Harry C., Powell, Debra L., Presnell, Scott, Pulendran, Bali, Rahman, Adeeb H., Rashid, Ahmad Sadeed, Raskin, Ariel, Reed, Elaine F., Ribeiro, Susan Pereira, Rivera, Adreanne M., Rogers, Jacob E., Rogers, Angela, Rogowski, Brandon, Rooks, Rebecca, Rosenberg-Hasson, Yael, Rothman, Jessica, Rousseau, Justin F., Salehi-Rad, Ramin, Saluvan, Mehmet, Samaha, Hady, Schaenman, Joanna, Schunk, Ron, Semenza, Nicholas C., Sen, Subha, Sevransky, Jonathan, Seyfert-Margolis, Vicki, Shaheen, Tanzia, Shaw, Albert C., Sieg, Scott, Siegel, Sarah A.R., Sigal, Natalia, Siles, Nadia, Simmons, Brent, Simon, Viviana, Singh, Gagandeep, Sinko, Lauren, Smith, Cecilia M., Smolen, Kinga K., Song, Li-Zhen, Srivastava, Komal, Sullivan, Peter, Syphurs, Caitlin, Tcheou, Johnstone, Tegos, George P., Tharp, Greg K., Tong, Alexandra, Tsitsiklis, Alexandra, Ungaro, Ricardo F., Vaysman, Tatyana, Viode, Arthur, Vita, Randi, Wang, Xiaomei, Ward, Alyssa, Ward, Dawn C., Willmore, Andrew, Woloszczuk, Kyra, Wong, Kari, Woodruff, Prescott G., Xu, Leqi, van Haren, Simon, van de Guchte, Adriana, Zhao, Yujiao, Diray-Arce, Joann, Fourati, Slim, Doni Jayavelu, Naresh, Patel, Ravi, Maguire, Cole, Chang, Ana C., Dandekar, Ravi, Qi, Jingjing, Lee, Brian H., van Zalm, Patrick, Schroeder, Andrew, Chen, Ernie, Konstorum, Anna, Brito, Anderson, Gygi, Jeremy P., Kho, Alvin, Chen, Jing, Pawar, Shrikant, Gonzalez-Reiche, Ana Silvia, Hoch, Annmarie, Milliren, Carly E., Overton, James A., Westendorf, Kerstin, Cairns, Charles B., Rouphael, Nadine, Bosinger, Steven E., Kim-Schulze, Seunghee, Krammer, Florian, Rosen, Lindsey, Grubaugh, Nathan D., van Bakel, Harm, Wilson, Michael, Rajan, Jayant, Steen, Hanno, Eckalbar, Walter, Cotsapas, Chris, Langelier, Charles R., Levy, Ofer, Altman, Matthew C., Maecker, Holden, Montgomery, Ruth R., Haddad, Elias K., Sekaly, Rafick P., Esserman, Denise, Ozonoff, Al, Augustine, Alison D., Guan, Leying, Peters, Bjoern, and Kleinstein, Steven H.

Published
2023
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26. Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study

Author

Ozonoff, Al, Schaenman, Joanna, Jayavelu, Naresh Doni, Milliren, Carly E., Calfee, Carolyn S., Cairns, Charles B., Kraft, Monica, Baden, Lindsey R., Shaw, Albert C., Krammer, Florian, van Bakel, Harm, Esserman, Denise A., Liu, Shanshan, Sesma, Ana Fernandez, Simon, Viviana, Hafler, David A., Montgomery, Ruth R., Kleinstein, Steven H., Levy, Ofer, Bime, Chris, Haddad, Elias K., Erle, David J., Pulendran, Bali, Nadeau, Kari C., Davis, Mark M, Hough, Catherine L., Messer, William B., Higuita, Nelson I. Agudelo, Metcalf, Jordan P., Atkinson, Mark A., Brakenridge, Scott C., Corry, David, Kheradmand, Farrah, Ehrlich, Lauren I.R., Melamed, Esther, McComsey, Grace A., Sekaly, Rafick, Diray-Arce, Joann, Peters, Bjoern, Augustine, Alison D., Reed, Elaine F., McEnaney, Kerry, Barton, Brenda, Lentucci, Claudia, Saluvan, Mehmet, Chang, Ana C., Hoch, Annmarie, Albert, Marisa, Shaheen, Tanzia, Kho, Alvin T., Thomas, Sanya, Chen, Jing, Murphy, Maimouna D., Cooney, Mitchell, Presnell, Scott, Fragiadakis, Gabriela K., Patel, Ravi, Guan, Leying, Gygi, Jeremy, Pawar, Shrikant, Brito, Anderson, Khalil, Zain, Maguire, Cole, Fourati, Slim, Overton, James A., Vita, Randi, Westendorf, Kerstin, Salehi-Rad, Ramin, Leligdowicz, Aleksandra, Matthay, Michael A., Singer, Jonathan P., Kangelaris, Kirsten N., Hendrickson, Carolyn M., Krummel, Matthew F., Langelier, Charles R., Woodruff, Prescott G., Powell, Debra L., Kim, James N., Simmons, Brent, Goonewardene, I. Michael, Smith, Cecilia M., Martens, Mark, Mosier, Jarrod, Kimura, Hiroki, Sherman, Amy C., Walsh, Stephen R., Issa, Nicolas C., Dela Cruz, Charles, Farhadian, Shelli, Iwasaki, Akiko, Ko, Albert I., Chinthrajah, Sharon, Ahuja, Neera, Rogers, Angela J., Artandi, Maja, Siegel, Sarah A.R., Lu, Zhengchun, Drevets, Douglas A., Brown, Brent R., Anderson, Matthew L., Guirgis, Faheem W., Thyagarajan, Rama V., Rousseau, Justin F., Wylie, Dennis, Busch, Johanna, Gandhi, Saurin, Triplett, Todd A., Yendewa, George, Giddings, Olivia, Anderson, Evan J., Mehta, Aneesh K., Sevransky, Jonathan E., Khor, Bernard, Rahman, Adeeb, Stadlbauer, Daniel, Dutta, Jayeeta, Xie, Hui, Kim-Schulze, Seunghee, Gonzalez-Reiche, Ana Silvia, van de Guchte, Adriana, Farrugia, Keith, Khan, Zenab, Maecker, Holden T., Elashoff, David, Brook, Jenny, Ramires-Sanchez, Estefania, Llamas, Megan, Rivera, Adreanne, Perdomo, Claudia, Ward, Dawn C., Magyar, Clara E., Fulcher, Jennifer A., Abe-Jones, Yumiko, Asthana, Saurabh, Beagle, Alexander, Bhide, Sharvari, Carrillo, Sidney A., Chak, Suzanna, Ghale, Rajani, Gonzalez, Ana, Jauregui, Alejandra, Jones, Norman, Lea, Tasha, Lee, Deanna, Lota, Raphael, Milush, Jeff, Nguyen, Viet, Pierce, Logan, Prasad, Priya A., Rao, Arjun, Samad, Bushra, Shaw, Cole, Sigman, Austin, Sinha, Pratik, Ward, Alyssa, Willmore, Andrew, Zhan, Jenny, Rashid, Sadeed, Rodriguez, Nicklaus, Tang, Kevin, Altamirano, Luz Torres, Betancourt, Legna, Curiel, Cindy, Sutter, Nicole, Paz, Maria Tercero, Tietje-Ulrich, Gayelan, Leroux, Carolyn, Connors, Jennifer, Bernui, Mariana, Kutzler, Michel A., Edwards, Carolyn, Lee, Edward, Lin, Edward, Croen, Brett, Semenza, Nicholas C., Rogowski, Brandon, Melnyk, Nataliya, Woloszczuk, Kyra, Cusimano, Gina, Bell, Mathew R., Furukawa, Sara, McLin, Renee, Marrero, Pamela, Sheidy, Julie, Tegos, George P., Nagle, Crystal, Mege, Nathan, Ulring, Kristen, Seyfert-Margolis, Vicki, Conway, Michelle, Francisco, Dave, Molzahn, Allyson, Erickson, Heidi, Wilson, Connie Cathleen, Schunk, Ron, Sierra, Bianca, Hughes, Trina, Smolen, Kinga, Desjardins, Michael, van Haren, Simon, Mitre, Xhoi, Cauley, Jessica, Li, Xiaofang, Tong, Alexandra, Evans, Bethany, Montesano, Christina, Licona, Jose Humberto, Krauss, Jonathan, Chang, Jun Bai Park, Izaguirre, Natalie, Chaudhary, Omkar, Coppi, Andreas, Fournier, John, Mohanty, Subhasis, Muenker, M. Catherine, Nelson, Allison, Raddassi, Khadir, Rainone, Michael, Ruff, William E., Salahuddin, Syim, Schulz, Wade L., Vijayakumar, Pavithra, Wang, Haowei, Wunder Jr., Elsio, Young, H. Patrick, Zhao, Yujiao, Saksena, Miti, Altman, Deena, Kojic, Erna, Srivastava, Komal, Eaker, Lily Q., Bermúdez-González, Maria C., Beach, Katherine F., Sominsky, Levy A., Azad, Arman R., Carreño, Juan Manuel, Singh, Gagandeep, Raskin, Ariel, Tcheou, Johnstone, Bielak, Dominika, Kawabata, Hisaaki, Mulder, Lubbertus CF, Kleiner, Giulio, Lee, Alexandra S., Do, Evan Do, Fernandes, Andrea, Manohar, Monali, Hagan, Thomas, Blish, Catherine A., Din, Hena Naz, Roque, Jonasel, Yang, Samuel, Brunton, Amanda, Sullivan, Peter E., Strnad, Matthew, Lyski, Zoe L., Coulter, Felicity J., Booth, J. Leland, Sinko, Lauren A., Moldawer, Lyle L., Borresen, Brittany, Roth-Manning, Brittney, Song, Li-Zhen, Nelson, Ebony, Lewis-Smith, Megan, Smith, Jacob, Tipan, Pablo Guaman, Siles, Nadia, Bazzi, Sam, Geltman, Janelle, Hurley, Kerin, Gabriele, Gio, Sieg, Scott, Vaysman, Tatyana, Bristow, Laurel, Hussaini, Laila, Hellmeister, Kieffer, Samaha, Hady, Cheng, Andrew, Spainhour, Christine, Scherer, Erin M., Johnson, Brandi, Bechnak, Amer, Ciric, Caroline R., Hewitt, Lauren, Carter, Erin, Mcnair, Nina, Panganiban, Bernadine, Huerta, Christopher, Usher, Jacob, Ribeiro, Susan Pereira, Altman, Matthew C., Becker, Patrice M., Rouphael, Nadine, Bime, Christian, and Davis, Mark M.

Published
2022
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29. Ultra‐low‐field magnetization transfer imaging at 0.055T with low specific absorption rate.

Author

Su, Shi, Zhao, Yujiao, Ding, Ye, Lau, Vick, Xiao, Linfang, Leung, Gilberto K. K., Lau, Gary K. K., Huang, Fan, Vardhanabhuti, Vince, Leong, Alex T. L., and Wu, Ed X.

Subjects
MAGNETIZATION transfer, GRAY matter (Nerve tissue), WHITE matter (Nerve tissue), BRAIN damage, CEREBROSPINAL fluid
Abstract

Purpose: To demonstrate magnetization transfer (MT) effects with low specific absorption rate (SAR) on ultra‐low‐field (ULF) MRI. Methods: MT imaging was implemented by using sinc‐modulated RF pulse train (SPT) modules to provide bilateral off‐resonance irradiation. They were incorporated into 3D gradient echo (GRE) and fast spin echo (FSE) protocols on a shielding‐free 0.055T head scanner. MT effects were first verified using phantoms. Brain MT imaging was conducted in both healthy subjects and patients. Results: MT effects were clearly observed in phantoms using six SPT modules with total flip angle 3600° at central primary saturation bands of approximate offset ±786 Hz, even in the presence of large relative B0 inhomogeneity. For brain, strong MT effects were observed in gray matter, white matter, and muscle in 3D GRE and FSE imaging using six and sixteen SPT modules with total flip angle 3600° and 9600°, respectively. Fat, cerebrospinal fluid, and blood exhibited relatively weak MT effects. MT preparation enhanced tissue contrasts in T2‐weighted and FLAIR‐like images, and improved brain lesion delineation. The estimated MT SAR was 0.0024 and 0.0008 W/kg for two protocols, respectively, which is far below the US Food and Drug Administration (FDA) limit of 3.0 W/kg. Conclusion: Robust MT effects can be readily obtained at ULF with extremely low SAR, despite poor relative B0 homogeneity in ppm. This unique advantage enables flexible MT pulse design and implementation on low‐cost ULF MRI platforms to achieve strong MT effects in brain and beyond, potentially augmenting their clinical utility in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Ultra‐low‐field magnetic resonance angiography at 0.05 T: A preliminary study.

Author

Su, Shi, Hu, Jiahao, Ding, Ye, Zhang, Junhao, Lau, Vick, Zhao, Yujiao, and Wu, Ed X.

Subjects
MAGNETIC resonance angiography, CAROTID artery, CEREBRAL veins, CEREBRAL arteries, JUGULAR vein
Abstract

We aim to explore the feasibility of head and neck time‐of‐flight (TOF) magnetic resonance angiography (MRA) at ultra‐low‐field (ULF). TOF MRA was conducted on a highly simplified 0.05 T MRI scanner with no radiofrequency (RF) and magnetic shielding. A flow‐compensated three‐dimensional (3D) gradient echo (GRE) sequence with a tilt‐optimized nonsaturated excitation RF pulse, and a flow‐compensated multislice two‐dimensional (2D) GRE sequence, were implemented for cerebral artery and vein imaging, respectively. For carotid artery and jugular vein imaging, flow‐compensated 2D GRE sequences were utilized with venous and arterial blood presaturation, respectively. MRA was performed on young healthy subjects. Vessel‐to‐background contrast was experimentally observed with strong blood inflow effect and background tissue suppression. The large primary cerebral arteries and veins, carotid arteries, jugular veins, and artery bifurcations could be identified in both raw GRE images and maximum intensity projections. The primary brain and neck arteries were found to be reproducible among multiple examination sessions. These preliminary experimental results demonstrated the possibility of artery TOF MRA on low‐cost 0.05 T scanners for the first time, despite the extremely low MR signal. We expect to improve the quality of ULF TOF MRA in the near future through sequence development and optimization, ongoing advances in ULF hardware and image formation, and the use of vascular T1 contrast agents. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Ensuring reliability in electronic examinations through UPPAAL-based trustworthy design.

Author

Zhou, Wenbo, Zhao, Yujiao, Zhang, Ye, Mu, Liwen, Wang, Yiyuan, and Yin, Minghao

Subjects
TRUST, SYSTEMS design, EDUCATIONAL outcomes, COMPUTERS, ENCODING
Abstract

Electronic examination serves as an efficient method for assessing learning outcomes, yet the integration of computers into exam processes introduces potential for unreliability. In this article, we propose a formal model for electronic examinations using timed automata, providing a structured approach to understanding and managing the complexities. The electronic examination process is modeled by defining four UPPAAL templates, i.e., candidate, administrator, invigilator, and examiner. Crucial properties specific to electronic examination are encoded as specifications in UPPAAL. Verification against these properties demonstrates the validity and reliability of this model. The modelable and verifiable electronic examination designed with UPPAAL suggests great potential for deeper exploration in trustworthy digital education. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding‐free MRI.

Author

Zhao, Yujiao, Xiao, Linfang, Liu, Yilong, Leong, Alex T., and Wu, Ed X.

Subjects
ELECTROMAGNETIC interference, ARTIFICIAL neural networks, DEEP learning, CONVOLUTIONAL neural networks, MAGNETIC resonance imaging, SIGNAL convolution, RADIO (Medium)
Abstract

At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict, and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI‐sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A convolution neural network model is trained using the EMI characterization data to relate EMI signals detected by EMI‐sensing coils to EMI signals in the MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by the MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low‐cost ultralow‐field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal‐to‐noise ratios that were comparable with those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., spectral domain transfer function and external dynamic interference estimation and removal [EDITER] methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding‐free MRI. The concept is relatively simple and is potentially applicable to other RF signal detection scenarios in the presence of external and/or internal EMI. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Robust EMI elimination for RF shielding‐free MRI through deep learning direct MR signal prediction.

Author

Zhao, Yujiao, Xiao, Linfang, Hu, Jiahao, and Wu, Ed X.

Subjects
DEEP learning, CONVOLUTIONAL neural networks, SIGNAL convolution, MAGNETIC resonance imaging, ELECTROMAGNETIC coupling, ELECTROMAGNETIC interference
Abstract

Purpose: To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding‐free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep‐DSP). Methods: Deep‐DSP is proposed to directly predict EMI‐free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U‐Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI‐free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI‐free MR signals from data acquired by MRI receive and sensing coils during the MR signal‐acquisition window. This strategy was evaluated on an ultralow‐field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole‐body scanner with incomplete RF shielding. Results: Deep‐DSP accurately predicted EMI‐free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data. Conclusion: Deep‐DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient‐friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep‐DSP framework is computationally simple and efficient. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Effect of a novel dwarfing mutant site on chromosome 4B on agronomic traits in common wheat

Author

Hao, Jiamin, primary, Zhao, Zhangchen, additional, Fu, Xiaoge, additional, Zhao, Yujiao, additional, Ateeq, Muhammad, additional, Mou, Liming, additional, Han, Yong, additional, Liu, Yangbin, additional, Yin, Yongan, additional, Zotova, Lyudmila, additional, Serikbay, Dauren, additional, Fan, Chunyan, additional, Hu, Yin-Gang, additional, and Chen, Liang, additional

Published
2024
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